Tag Archives: accessibility

A smart guitar for blind, deaf, and mute people

2019-02-26 Alex Bate

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/smart-guitar-blind-deaf-mute/

ChordAssist aims to bring the joy of learning the guitar to those who otherwise may have problems with accessing guitar tutorials. Offering advice in Braille, in speech, and on-screen, ChordAssist has been built specifically for deaf, blind, and mute people. Creator Joe Birch, who also built the BrailleBox device, used Raspberry Pi, Google Assistant, and a variety of accessibility tools and technology for this accessible instrument.

Chord Assist: An accessible smart guitar for the blind, deaf and mute
Powered by the Google Assistant, read more at chordassist.com

Accessibility and music

Inspired by a hereditary visual impairment in his family, Buffer’s Android Lead Joe Birch spent six months working on ChordAssist, an accessible smart guitar.

“This is a project that I used to bring my love of music and accessibility (inspired by my family condition of retinitis pigmentosa) together to create something that could allow everyone to enjoy learning and playing music — currently an area which might not be accessible to all,” explained Joe when he shared his project on Twitter earlier this month.

BrailleBox

This isn’t Joe’s first step into the world of smart accessibility devices. In 2017, he created BrailleBox, an Android Things news delivery device that converts daily news stories into Braille, using wooden balls atop solenoids that move up and down to form Braille symbols.

ChordAssist

This same technology exists within ChordAssist, along with an LCD screen for visual learning, and a speaker system for text-to-speech conversion.

Chord Assist was already an Action on the Google Project that I built for the Google Home, now I wanted to take that and stick it in a guitar powered by voice, visuals, and Braille. All three of these together will hopefully help to reduce the friction that may be experienced throughout the process of learning an instrument.

ChordAssist is currently still at the prototype stage, and Joe invites everyone to offer feedback so he can make improvements.

To learn more about ChordAssist, visit the ChordAssist website and check out Joe’s write-up on Medium.

The post A smart guitar for blind, deaf, and mute people appeared first on Raspberry Pi.

Synesthiser, an accessible electronic music instrument

2018-07-27 Alex Bate

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/synesthiser-an-accessible-electronic-music-instrument/

A Raspberry Pi is the beating heart of this accessible musical instrument, built by South Korean maker Jaewon “J. One” Choi to enable more people with hearing impairments to create music:

synesthiser.
experimental musical instrument, 2018 Raspberry Pi, Arduino, Pure Data, Python

Making music more accessible

J. One’s latest project, synesthiser, produces vibration alongside sound, and is an exploration into music production and performance for hearing-impaired people.

Its main objective is to make music producing/performing more accessible for those who have a hearing impairment. By producing not only vibration but also audible wave, it could widen the opportunity of designing sound for handicapped and non-handicapped people equally.

synesthiser

The build’s interface is a round surface that reacts to pressure and rotation. By turning it with the flat of their hand, users of synesthiser alter the frequency of sounds; by pressing on it, they manipulate the amplitude and modulation of the waveform.

J. One raspberry Pi synesthiser

A transducer within the unit provides vibrations that resonate throughout the entire device to let people with hearing impairments experience its sound via touch. And hence the project’s title, a portmanteau (or mashup!) of ‘synesthesia‘ and ‘synthesiser’.

Seoul

seoul.
installation / media art, 2017 Max 7, p5.js, Swift, Raspberry Pi filmed by Jaewon Choi special thanks to Gayeong Baek, Jongmin Jung The atmosphere of Seoul is sophisticated. A cold wind of the dawn, endless traffic, people yelling each other, and the rhythm of the footsteps. Everything vaporises to the noise.

You can find more of J. One’s projects on their website, including Seoul, a Raspberry Pi–powered sound exhibit that allows visitors to incorporate their own sounds into layers of real-time noise of Seoul.

Build a synth with Raspberry Pi

Musicians and Raspberry Pis make beautiful music together. This much we know to be true, and a quick search of the interwebs will confirm it for you. We and our community have created Raspberry Pi projects for even the most novice of programmers to try out.

Start with our guide to building a GPIO music box if you’re unsure how to hit the high notes with music and code, and then move on to our introduction to Sonic Pi, the open-source live coding environment.

And for a truly sensational music-based digital making project, have a look at the beautiful mix of sound and aesthetics that is Toby Hendricks’ Raspberry Pi Looper-Synth-Drum…Thing.

Toby Hendricks Raspberry Pi looper

Yes, that’s its actual name.

And if you’re more classically inclined, why not implement this Pi-powered AI algorithm to accompany you on the piano?

The post Synesthiser, an accessible electronic music instrument appeared first on Raspberry Pi.

Putin Asked to Investigate Damage Caused By Telegram Web-Blocking

2018-05-26 Andy

Post Syndicated from Andy original https://torrentfreak.com/putin-asked-to-investigate-damage-caused-by-telegram-web-blocking-180526/

After a Moscow court gave the go-ahead for Telegram to be banned in Russia last month, the Internet became a battleground.

On the instructions of telecoms watchdog Roscomnadzor, ISPs across Russia tried to block Telegram by blackholing millions of IP addresses. The effect was both dramatic and pathetic. While Telegram remained stubbornly online, countless completely innocent services suffered outages as Roscomnadzor charged ahead with its mission.

Over the past several weeks, Roscomnadzor has gone some way to clean up the mess, partly by removing innocent Google and Amazon IP addresses from Russia’s blacklist. However, the collateral damage was so widespread it’s called into question the watchdog’s entire approach to web-blockades and whether they should be carried out at any cost.

This week, thanks to an annual report presented to President Vladimir Putin by business ombudsman Boris Titov, the matter looks set to be escalated. ‘The Book of Complaints and Suggestions of Russian Business’ contains comments from Internet ombudsman Dmitry Marinichev, who says that the Prosecutor General’s Office should launch an investigation into Roscomnadzor’s actions.

Marinichev said that when attempting to take down Telegram using aggressive technical means, Roscomnadzor relied upon “its own interpretation of court decisions” to provide guidance, TASS reports.

“When carrying out blockades of information resources, Roskomnadzor did not assess the related damage caused to them,” he said.

More than 15 million IP addresses were blocked, many of them with functions completely unrelated to the operations of Telegram. Marinichev said that the consequences were very real for those who suffered collateral damage.

“[The blocking led] to a temporary inaccessibility of Internet resources of a number of Russian enterprises in the Internet sector, including several banks and government information resources,” he reported.

In advice to the President, Marinichev suggests that the Prosecutor General’s Office should look into “the legality and validity of Roskomnadzor’s actions” which led to the “violation of availability of information resources of commercial companies” and “threatened the integrity, sustainability, and functioning of the unified telecommunications network of the Russian Federation and its critical information infrastructure.”

Early May, it was reported that in addition to various web services, around 50 VPN, proxy and anonymization platforms had been blocked for providing access to Telegram. In a May 22 report, that number had swelled to more than 80 although 10 were later unblocked after they stopped providing access to the messaging platform.

This week, Roscomnadzor has continued with efforts to block access to torrent and streaming platforms. In a new wave of action, the telecoms watchdog ordered ISPs to block at least 47 mirrors and proxies providing access to previously blocked sites.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Working with the Scout Association on digital skills for life

2018-05-22 Philip Colligan

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/working-with-scout-association-digital-skills-for-life/

Today we’re launching a new partnership between the Scouts and the Raspberry Pi Foundation that will help tens of thousands of young people learn crucial digital skills for life. In this blog post, I want to explain what we’ve got planned, why it matters, and how you can get involved.

This is personal

First, let me tell you why this partnership matters to me. As a child growing up in North Wales in the 1980s, Scouting changed my life. My time with 2nd Rhyl provided me with countless opportunities to grow and develop new skills. It taught me about teamwork and community in ways that continue to shape my decisions today.

As my own kids (now seven and ten) have joined Scouting, I’ve seen the same opportunities opening up for them, and like so many parents, I’ve come back to the movement as a volunteer to support their local section. So this is deeply personal for me, and the same is true for many of my colleagues at the Raspberry Pi Foundation who in different ways have been part of the Scouting movement.

That shouldn’t come as a surprise. Scouting and Raspberry Pi share many of the same values. We are both community-led movements that aim to help young people develop the skills they need for life. We are both powered by an amazing army of volunteers who give their time to support that mission. We both care about inclusiveness, and pride ourselves on combining fun with learning by doing.

Raspberry Pi

Raspberry Pi started life in 2008 as a response to the problem that too many young people were growing up without the skills to create with technology. Our goal is that everyone should be able to harness the power of computing and digital technologies, for work, to solve problems that matter to them, and to express themselves creatively.

In 2012 we launched our first product, the world’s first $35 computer. Just six years on, we have sold over 20 million Raspberry Pi computers and helped kickstart a global movement for digital skills.

The Raspberry Pi Foundation now runs the world’s largest network of volunteer-led computing clubs (Code Clubs and CoderDojos), and creates free educational resources that are used by millions of young people all over the world to learn how to create with digital technologies. And lots of what we are able to achieve is because of partnerships with fantastic organisations that share our goals. For example, through our partnership with the European Space Agency, thousands of young people have written code that has run on two Raspberry Pi computers that Tim Peake took to the International Space Station as part of his Mission Principia.

Digital makers

Today we’re launching the new Digital Maker Staged Activity Badge to help tens of thousands of young people learn how to create with technology through Scouting. Over the past few months, we’ve been working with the Scouts all over the UK to develop and test the new badge requirements, along with guidance, project ideas, and resources that really make them work for Scouting. We know that we need to get two things right: relevance and accessibility.

Relevance is all about making sure that the activities and resources we provide are a really good fit for Scouting and Scouting’s mission to equip young people with skills for life. From the digital compass to nature cameras and the reinvented wide game, we’ve had a lot of fun thinking about ways we can bring to life the crucial role that digital technologies can play in the outdoors and adventure.

Compass Coding with Raspberry Pi
We are beyond excited to be launching a new partnership with the Raspberry Pi Foundation, which will help tens of thousands of young people learn digital skills for life.

We also know that there are great opportunities for Scouts to use digital technologies to solve social problems in their communities, reflecting the movement’s commitment to social action. Today we’re launching the first set of project ideas and resources, with many more to follow over the coming weeks and months.

Accessibility is about providing every Scout leader with the confidence, support, and kit to enable them to offer the Digital Maker Staged Activity Badge to their young people. A lot of work and care has gone into designing activities that require very little equipment: for example, activities at Stages 1 and 2 can be completed with a laptop without access to the internet. For the activities that do require kit, we will be working with Scout Stores and districts to make low-cost kit available to buy or loan.

We’re producing accessible instructions, worksheets, and videos to help leaders run sessions with confidence, and we’ll also be planning training for leaders. We will work with our network of Code Clubs and CoderDojos to connect them with local sections to organise joint activities, bringing both kit and expertise along with them.

Get involved

Today’s launch is just the start. We’ll be developing our partnership over the next few years, and we can’t wait for you to join us in getting more young people making things with technology.

Take a look at the brand-new Raspberry Pi resources designed especially for Scouts, to get young people making and creating right away.

The post Working with the Scout Association on digital skills for life appeared first on Raspberry Pi.

How to Use AWS Config to Monitor for and Respond to Amazon S3 Buckets Allowing Public Access

2018-05-01 Josh Zeiser

Post Syndicated from Josh Zeiser original https://aws.amazon.com/blogs/security/how-to-use-aws-config-to-monitor-for-and-respond-to-amazon-s3-buckets-allowing-public-access/

AWS Config enables continuous monitoring of your AWS resources, making it simple to assess, audit, and record resource configurations and changes. AWS Config does this through the use of rules that define the desired configuration state of your AWS resources. AWS Config provides a number of AWS managed rules that address a wide range of security concerns such as checking if you encrypted your Amazon Elastic Block Store (Amazon EBS) volumes, tagged your resources appropriately, and enabled multi-factor authentication (MFA) for root accounts. You can also create custom rules to codify your compliance requirements through the use of AWS Lambda functions.

In this post we’ll show you how to use AWS Config to monitor our Amazon Simple Storage Service (S3) bucket ACLs and policies for violations which allow public read or public write access. If AWS Config finds a policy violation, we’ll have it trigger an Amazon CloudWatch Event rule to trigger an AWS Lambda function which either corrects the S3 bucket ACL, or notifies you via Amazon Simple Notification Service (Amazon SNS) that the policy is in violation and allows public read or public write access. We’ll show you how to do this in five main steps.

Enable AWS Config to monitor Amazon S3 bucket ACLs and policies for compliance violations.
Create an IAM Role and Policy that grants a Lambda function permissions to read S3 bucket policies and send alerts through SNS.
Create and configure a CloudWatch Events rule that triggers the Lambda function when AWS Config detects an S3 bucket ACL or policy violation.
Create a Lambda function that uses the IAM role to review S3 bucket ACLs and policies, correct the ACLs, and notify your team of out-of-compliance policies.
Verify the monitoring solution.

Note: This post assumes your compliance policies require the buckets you monitor not allow public read or write access. If you have intentionally open buckets serving static content, for example, you can use this post as a jumping-off point for a solution tailored to your needs.

At the end of this post, we provide an AWS CloudFormation template that implements the solution outlined. The template enables you to deploy the solution in multiple regions quickly.

Important: The use of some of the resources deployed, including those deployed using the provided CloudFormation template, will incur costs as long as they are in use. AWS Config Rules incur costs in each region they are active.

Architecture

Here’s an architecture diagram of what we’ll implement:

Figure 1: Architecture diagram

Step 1: Enable AWS Config and Amazon S3 Bucket monitoring

The following steps demonstrate how to set up AWS Config to monitor Amazon S3 buckets.

Sign into the AWS Management Console and open the AWS Config console.
If this is your first time using AWS Config, select Get started. If you’ve already used AWS Config, select Settings.
In the Settings page, under Resource types to record, clear the All resources checkbox. In the Specific types list, select Bucket under S3.

Figure 2: The Settings dialog box showing the “Specific types” list
Choose the Amazon S3 bucket for storing configuration history and snapshots. We’ll create a new Amazon S3 bucket.

Figure 3: Creating an S3 bucket
1. If you prefer to use an existing Amazon S3 bucket in your account, select the Choose a bucket from your account radio button and, using the dropdown, select an existing bucket.
  
  Figure 4: Selecting an existing S3 bucket
Under Amazon SNS topic, check the box next to Stream configuration changes and notifications to an Amazon SNS topic, and then select the radio button to Create a topic.
1. Alternatively, you can choose a topic that you have previously created and subscribed to.
  
  Figure 5: Selecting a topic that you’ve previously created and subscribed to
2. If you created a new SNS topic you need to subscribe to it to receive notifications. We’ll cover this in a later step.
Under AWS Config role, choose Create a role (unless you already have a role you want to use). We’re using the auto-suggested role name.

Figure 6: Creating a role
Select Next.
Configure Amazon S3 bucket monitoring rules:
1. On the AWS Config rules page, search for S3 and choose the s3-bucket-publice-read-prohibited and s3-bucket-public-write-prohibited rules, then click Next.
  
  Figure 7: AWS Config rules dialog
2. On the Review page, select Confirm. AWS Config is now analyzing your Amazon S3 buckets, capturing their current configurations, and evaluating the configurations against the rules we selected.
If you created a new Amazon SNS topic, open the Amazon SNS Management Console and locate the topic you created:

Figure 8: Amazon SNS topic list
Copy the ARN of the topic (the string that begins with arn:) because you’ll need it in a later step.
Select the checkbox next to the topic, and then, under the Actions menu, select Subscribe to topic.
Select Email as the protocol, enter your email address, and then select Create subscription.
After several minutes, you’ll receive an email asking you to confirm your subscription for notifications for this topic. Select the link to confirm the subscription.

Step 2: Create a Role for Lambda

Our Lambda will need permissions that enable it to inspect and modify Amazon S3 bucket ACLs and policies, log to CloudWatch Logs, and publishing to an Amazon SNS topic. We’ll now set up a custom AWS Identity and Access Management (IAM) policy and role to support these actions and assign them to the Lambda function we’ll create in the next section.

In the AWS Management Console, under Services, select IAM to access the IAM Console.

Create a policy with the following permissions, or copy the following policy:


{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "SNSPublish",
            "Effect": "Allow",
            "Action": [
                "sns:Publish"
            ],
            "Resource": "*"
        },
        {
            "Sid": "S3GetBucketACLandPolicy",
            "Effect": "Allow",
            "Action": [
                "s3:GetBucketAcl",
                "s3:GetBucketPolicy"
            ],
            "Resource": "*"
        },
        {
            "Sid": "S3PutBucketACLAccess",
            "Effect": "Allow",
            "Action": "s3:PutBucketAcl",
            "Resource": "arn:aws:s3:::*"
        },
        {
            "Sid": "LambdaBasicExecutionAccess",
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "*"
        }
    ]
}

Create a role for your Lambda function:

Select Lambda from the list of services that will use this role.
Select the check box next to the policy you created previously, and then select Next: Review
Name your role, give it a description, and then select Create Role. In this example, we’re naming the role LambdaS3PolicySecuringRole.

Step 3: Create and Configure a CloudWatch Rule

In this section, we’ll create a CloudWatch Rule to trigger the Lambda function when AWS Config determines that your Amazon S3 buckets are non-compliant.

In the AWS Management Console, under Services, select CloudWatch.
On the left-hand side, under Events, select Rules.
Click Create rule.
In Step 1: Create rule, under Event Source, select the dropdown list and select Build custom event pattern.

Copy the following pattern and paste it into the text box:


{
  "source": [
    "aws.config"
  ],
  "detail": {
    "requestParameters": {
      "evaluations": {
        "complianceType": [
          "NON_COMPLIANT"
        ]
      }
    },
    "additionalEventData": {
      "managedRuleIdentifier": [
        "S3_BUCKET_PUBLIC_READ_PROHIBITED",
        "S3_BUCKET_PUBLIC_WRITE_PROHIBITED"
      ]
    }
  }
}

The pattern matches events generated by AWS Config when it checks the Amazon S3 bucket for public accessibility.

We’ll add a Lambda target later. For now, select your Amazon SNS topic created earlier, and then select Configure details.

Figure 9: The “Create rule” dialog
Give your rule a name and description. For this example, we’ll name ours AWSConfigFoundOpenBucket
Click Create rule.

Step 4: Create a Lambda Function

In this section, we’ll create a new Lambda function to examine an Amazon S3 bucket’s ACL and bucket policy. If the bucket ACL is found to allow public access, the Lambda function overwrites it to be private. If a bucket policy is found, the Lambda function creates an SNS message, puts the policy in the message body, and publishes it to the Amazon SNS topic we created. Bucket policies can be complex, and overwriting your policy may cause unexpected loss of access, so this Lambda function doesn’t attempt to alter your policy in any way.

Get the ARN of the Amazon SNS topic created earlier.
In the AWS Management Console, under Services, select Lambda to go to the Lambda Console.
From the Dashboard, select Create Function. Or, if you were taken directly to the Functions page, select the Create Function button in the upper-right.
On the Create function page:
1. Choose Author from scratch.
2. Provide a name for the function. We’re using AWSConfigOpenAccessResponder.
3. The Lambda function we’ve written is Python 3.6 compatible, so in the Runtime dropdown list, select Python 3.6.
4. Under Role, select Choose an existing role. Select the role you created in the previous section, and then select Create function.
  
  Figure 10: The “Create function” dialog
We’ll now add a CloudWatch Event based on the rule we created earlier.
1. In the Add triggers section, select CloudWatch Events. A CloudWatch Events box should appear connected to the left side of the Lambda Function and have a note that states Configuration required.
  
  Figure 11: CloudWatch Events in the “Add triggers” section
2. From the Rule dropdown box, choose the rule you created earlier, and then select Add.
Scroll up to the Designer section and select the name of your Lambda function.

Delete the default code and paste in the following code:


import boto3
from botocore.exceptions import ClientError
import json
import os

ACL_RD_WARNING = "The S3 bucket ACL allows public read access."
PLCY_RD_WARNING = "The S3 bucket policy allows public read access."
ACL_WRT_WARNING = "The S3 bucket ACL allows public write access."
PLCY_WRT_WARNING = "The S3 bucket policy allows public write access."
RD_COMBO_WARNING = ACL_RD_WARNING + PLCY_RD_WARNING
WRT_COMBO_WARNING = ACL_WRT_WARNING + PLCY_WRT_WARNING

def policyNotifier(bucketName, s3client):
    try:
        bucketPolicy = s3client.get_bucket_policy(Bucket = bucketName)
        # notify that the bucket policy may need to be reviewed due to security concerns
        sns = boto3.client('sns')
        subject = "Potential compliance violation in " + bucketName + " bucket policy"
        message = "Potential bucket policy compliance violation. Please review: " + json.dumps(bucketPolicy['Policy'])
        # send SNS message with warning and bucket policy
        response = sns.publish(
            TopicArn = os.environ['TOPIC_ARN'],
            Subject = subject,
            Message = message
        )
    except ClientError as e:
        # error caught due to no bucket policy
        print("No bucket policy found; no alert sent.")

def lambda_handler(event, context):
    # instantiate Amazon S3 client
    s3 = boto3.client('s3')
    resource = list(event['detail']['requestParameters']['evaluations'])[0]
    bucketName = resource['complianceResourceId']
    complianceFailure = event['detail']['requestParameters']['evaluations'][0]['annotation']
    if(complianceFailure == ACL_RD_WARNING or complianceFailure == PLCY_RD_WARNING):
        s3.put_bucket_acl(Bucket = bucketName, ACL = 'private')
    elif(complianceFailure == PLCY_RD_WARNING or complianceFailure == PLCY_WRT_WARNING):
        policyNotifier(bucketName, s3)
    elif(complianceFailure == RD_COMBO_WARNING or complianceFailure == WRT_COMBO_WARNING):
        s3.put_bucket_acl(Bucket = bucketName, ACL = 'private')
        policyNotifier(bucketName, s3)
    return 0  # done

Scroll down to the Environment variables section. This code uses an environment variable to store the Amazon SNS topic ARN.
1. For the key, enter TOPIC_ARN.
2. For the value, enter the ARN of the Amazon SNS topic created earlier.
Under Execution role, select Choose an existing role, and then select the role created earlier from the dropdown.
Leave everything else as-is, and then, at the top, select Save.

Step 5: Verify it Works

We now have the Lambda function, an Amazon SNS topic, AWS Config watching our Amazon S3 buckets, and a CloudWatch Rule to trigger the Lambda function if a bucket is found to be non-compliant. Let’s test them to make sure they work.

We have an Amazon S3 bucket, myconfigtestbucket that’s been created in the region monitored by AWS Config, as well as the associated Lambda function. This bucket has no public read or write access set in an ACL or a policy, so it’s compliant.

Figure 12: The “Config Dashboard”

Let’s change the bucket’s ACL to allow public listing of objects:

Screen shot of "Permissions" tab showing Everyone granted access

Figure 13: Screen shot of “Permissions” tab showing Everyone granted list access

After saving, the bucket now has public access. After several minutes, the AWS Config Dashboard notes that there is one non-compliant resource:

Figure 14: The “Config Dashboard” shown with a non-compliant resource

In the Amazon S3 Console, we can see that the bucket no longer has public listing of objects enabled after the invocation of the Lambda function triggered by the CloudWatch Rule created earlier.

The "Permissions" tab showing access no longer allowed

Figure 15: The “Permissions” tab showing list access no longer allowed

Notice that the AWS Config Dashboard now shows that there are no longer any non-compliant resources:

Figure 16: The “Config Dashboard” showing zero non-compliant resources

Now, let’s try out the Amazon S3 bucket policy check by configuring a bucket policy that allows list access:

Figure 17: A bucket policy that allows list access

A few minutes after setting this bucket policy on the
myconfigtestbucket bucket, AWS Config recognizes the bucket is no longer compliant. Because this is a bucket policy rather than an ACL, we publish a notification to the SNS topic we created earlier that lets us know about the potential policy violation:

Figure 18: Notification about potential policy violation

Knowing that the policy allows open listing of the bucket, we can now modify or delete the policy, after which AWS Config will recognize that the resource is compliant.

Conclusion

In this post, we demonstrated how you can use AWS Config to monitor for Amazon S3 buckets with open read and write access ACLs and policies. We also showed how to use Amazon CloudWatch, Amazon SNS, and Lambda to overwrite a public bucket ACL, or to alert you should a bucket have a suspicious policy. You can use the CloudFormation template to deploy this solution in multiple regions quickly. With this approach, you will be able to easily identify and secure open Amazon S3 bucket ACLs and policies. Once you have deployed this solution to multiple regions you can aggregate the results using an AWS Config aggregator. See this post to learn more.

If you have feedback about this blog post, submit comments in the Comments section below. If you have questions about this blog post, start a new thread on the AWS Config forum or contact AWS Support.

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AIY Projects 2: Google’s AIY Projects Kits get an upgrade

2018-04-17 Alex Bate

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/google-aiy-projects-2/

After the outstanding success of their AIY Projects Voice and Vision Kits, Google has announced the release of upgraded kits, complete with Raspberry Pi Zero WH, Camera Module, and preloaded SD card.

Google’s AIY Projects Kits

Google launched the AIY Projects Voice Kit last year, first as a cover gift with The MagPi magazine and later as a standalone product.

Makers needed to provide their own Raspberry Pi for the original kit. The new kits include everything you need, from Pi to SD card.

Within a DIY cardboard box, makers were able to assemble their own voice-activated AI assistant akin to the Amazon Alexa, Apple’s Siri, and Google’s own Google Home Assistant. The Voice Kit was an instant hit that spurred no end of maker videos and tutorials, including our own free tutorial for controlling a robot using voice commands.

Later in the year, the team followed up the success of the Voice Kit with the AIY Projects Vision Kit — the same cardboard box hosting a camera perfect for some pretty nifty image recognition projects.

For more on the AIY Voice Kit, here’s our release video hosted by the rather delightful Rob Zwetsloot.

AIY Projects adds natural human interaction to your Raspberry Pi
Check out the exclusive Google AIY Projects Kit that comes free with The MagPi 57! Grab yourself a copy in stores or online now: http://magpi.cc/2pI6IiQ This first AIY Projects kit taps into the Google Assistant SDK and Cloud Speech API using the AIY Projects Voice HAT (Hardware Accessory on Top) board, stereo microphone, and speaker (included free with the magazine).

AIY Projects 2

So what’s new with version 2 of the AIY Projects Voice Kit? The kit now includes the recently released Raspberry Pi Zero WH, our Zero W with added pre-soldered header pins for instant digital making accessibility. Purchasers of the kits will also get a micro SD card with preloaded OS to help them get started without having to set the card up themselves.

Google AIY Projects Vision Kit 2 Raspberry Pi

Everything you need to build your own Raspberry Pi-powered Google voice assistant

In the newly upgraded AIY Projects Vision Kit v1.2, makers are also treated to an official Raspberry Pi Camera Module v2, the latest model of our add-on camera.

“Everything you need to get started is right there in the box,” explains Billy Rutledge, Google’s Director of AIY Projects. “We knew from our research that even though makers are interested in AI, many felt that adding it to their projects was too difficult or required expensive hardware.”

Google is also hard at work producing AIY Projects companion apps for Android, iOS, and Chrome. The Android app is available now to coincide with the launch of the upgraded kits, with the other two due for release soon. The app supports wireless setup of the AIY Kit, though avid coders will still be able to hack theirs to better suit their projects.

Google has also updated the AIY Projects website with an AIY Models section highlighting a range of neural network projects for the kits.

Get your kit

The updated Voice and Vision Kits were announced last night, and in the US they are available now from Target. UK-based makers should be able to get their hands on them this summer — keep an eye on our social channels for updates and links.

The post AIY Projects 2: Google’s AIY Projects Kits get an upgrade appeared first on Raspberry Pi.

WHOIS Limits Under GDPR Will Make Pirates Harder to Catch, Groups Fear

2018-04-13 Andy

Post Syndicated from Andy original https://torrentfreak.com/whois-limits-under-gdpr-will-make-pirates-harder-to-catch-groups-fear-180413/

The General Data Protection Regulation (GDPR) is a regulation in EU law covering data protection and privacy for all individuals within the European Union.

As more and more personal data is gathered, stored and (ab)used online, the aim of the GDPR is to protect EU citizens from breaches of privacy. The regulation applies to all companies processing the personal data of subjects residing in the Union, no matter where in the world the company is located.

Penalties for non-compliance can be severe. While there is a tiered approach according to severity, organizations can be fined up to 4% of annual global turnover or €20 million, whichever is greater. Needless to say, the regulations will need to be taken seriously.

Among those affected are domain name registries and registrars who publish the personal details of domain name owners in the public WHOIS database. In a full entry, a person or organization’s name, address, telephone numbers and email addresses can often be found.

This raises a serious issue. While registries and registrars are instructed and contractually obliged to publish data in the WHOIS database by global domain name authority ICANN, in millions of cases this conflicts with the requirements of the GDPR, which prevents the details of private individuals being made freely available on the Internet.

As explained in detail by the EFF, ICANN has been trying to resolve this clash. Its proposed interim model for GDPR compliance (pdf) envisions registrars continuing to collect full WHOIS data but not necessarily publishing it, to “allow the existing data
to be preserved while the community discussions continue on the next generation of WHOIS.”

But the proposed changes that will inevitably restrict free access to WHOIS information has plenty of people spooked, including thousands of companies belonging to entertainment industry groups such as the MPAA, IFPI, RIAA and the Copyright Alliance.

In a letter sent to Vice President Andrus Ansip of the European Commission, these groups and dozens of others warn that restricted access to WHOIS will have a serious effect on their ability to protect their intellectual property rights from “cybercriminals” which pose a threat to their businesses.

Signed by 50 organizations involved in IP protection and other areas of online security, the letter expresses concern that in attempting to comply with the GDPR, ICANN is on a course to “over-correct” while disregarding proportionality, accountability and transparency.

A small sample of the groups calling on ICANN

“We strongly assert that this model does not properly account for the critical public and legitimate interests served by maintaining a sufficient amount of data publicly available while respecting privacy interests of registrants by instituting a tiered or layered access system for the vast majority of personal data as defined by the GDPR,” the groups write.

The letter focuses on two aspects of “over-correction”, the first being ICANN’s proposal that no personal data whatsoever of a domain name registrant will be made available “without appropriate consideration or balancing of the countervailing interests in public disclosure of a limited amount of such data.”

In response to ICANN’s proposal that only the province/state and country of a domain name registrant be made publicly available, the groups advise the organization that publishing “a natural person registrant’s e-mail address” in a publicly accessible WHOIS directory will not constitute a breach of the GDPR.

“[W]e strongly believe that the continued public availability of the registrant’s e-mail address – specifically the e-mail address that the registrant supplies to the registrar at the time the domain name is purchased and which e-mail address the registrar is required to validate – is critical for several reasons,” the groups write.

“First, it is the data element that is typically the most important to have readily available for law enforcement, consumer protection, particularly child protection, intellectual property enforcement and cybersecurity/anti-malware purposes.

“Second, the public accessibility of the registrant’s e-mail address permits a broad array of threats and illegal activities to be addressed quickly and the damage from such threats mitigated and contained in a timely manner, particularly where the abusive/illegal activity may be spawned from a variety of different domain names on different generic Top Level Domains,” they add.

The groups also argue that since making email addresses is effectively required in light of Article 5.1(c) ECD, “there is no legitimate justification to discontinue public availability of the registrant’s e-mail address in the WHOIS directory and especially not in light of other legitimate purposes.”

The EFF, on the other hand, says that being able to contact a domain owner wouldn’t necessarily require an email address to be made public.

“There are other cases in which it makes sense to allow members of the public to contact the owner of a domain, without having to obtain a court order,” EFF writes.

“But this could be achieved very simply if ICANN were simply to provide something like a CAPTCHA-protected contact form, which would deliver email to the appropriate contact point with no need to reveal the registrant’s actual email address.”

The groups’ second main concern is that ICANN reportedly makes no distinction between name registrants that are “natural persons versus those that are legal entities” and intends to treat them all as if they are subject to the GDPR, despite the fact that the regulation only applies to data associated with an “identified or identifiable natural person”.

They say it is imperative that EU Data Protection Authorities are made to understand that when registrants obtain a domain for illegal purposes, they often only register it as a “natural person” when registering as a legal person (legal entity) would be more appropriate, despite that granting them less privacy.

“Consequently, the test for differentiating between a legal and natural person should not merely be the legal status of the registrant, but also whether the registrant is, in fact, acting as a legal or natural person vis a vis the use of the domain name,” the groups note.

“We therefore urge that ICANN be given appropriate guidance as to the importance of maintaining a distinction between natural person and legal person registrants and keeping as much data about legal person domain name registrants as publicly accessible as possible,” they conclude.

What will happen with WHOIS on May 25 still isn’t clear. It wasn’t until October 2017 that ICANN finally determined that it would be affected by the GDPR, meaning that it’s been scrambling ever since to meet the compliance date. And it still is, according to the latest available documentation (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Barcode reader for visually impaired shoppers

2018-03-05 Alex Bate

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/barcode-reader/

To aid his mother in reading the labels of her groceries, Russell Grokett linked a laser barcode reader to a Raspberry Pi Zero W to read out the names of scanned item.

RASPBERRY PI TALKING BARCODE READER
My mom is unable to read labels on grocery items anymore, so I went looking for solutions. After seeing that bar code readers for the blind run many hundreds of dollars, I wanted to see what could be done using a Raspberry Pi and a USB Barcode reader.

Exploring accessibility issues

As his mother is no longer able to read the labels on her groceries, Russell Grokett started exploring accessibility devices to help her out. When he came across high-priced barcode readers, he decided to take matters into his own hands.

Camera vs scanner

Originally opting for a camera to read the codes, Russell encountered issues with light and camera angle. This forced him to think of a new option, and he soon changed his prototype to include a laser barcode reader for around $30. The added bonus was that Raspbian supported the reader out of the box, reducing the need for configuration — always a plus for any maker.

A screenshot from the video showing the laser scanner used for the Raspberry Pi-powered barcode reader

Russell’s laser barcode scanner, picked up online for around $30

No internet, please

With the issues of the camera neatly resolved, Russell had another obstacle to overcome: the device’s internet access, or lack thereof, when his mother was out of range of WiFi, for example at a store.

Another key requirement was that this should work WITHOUT an internet connection (such as at a store or friend’s house). So the database and text-to-speech had to be self-contained.

Russell tackled this by scouring the internet for open-source UPC code databases, collecting barcode data to be stored on the Raspberry Pi. Due to cost (few databases are available for free), he was forced to stitch together bits of information he could find, resigning himself to inputting new information manually in the future.

I was able to put a couple open-source databases together (sources in appendix below), but even with nearly 700000 items in it, a vast number are missing.
To this end, I have done two things: one is to focus on grocery items specifically, and the other is to add a webserver to the Raspberry Pi to allow adding new UPC codes manually, though this does require at least local network connectivity.

Read it aloud

For the text-to-speech function of the project, Russell used Flite, as this interface makes a healthy compromise between quality of audio and speed. As he explains in his Instructables tutorial, you can find out more about using Flite on the Adafruit website.

A screenshot from the video showing the laser scanner used for the Raspberry Pi-powered barcode reader scanned an item

When an item is scanned, the Raspberry Pi plays back audio of its name

In order to maintain the handheld size of the scanner, Russell used a Raspberry Pi Zero W for the project, and he repurposed his audio setup of a previous build, the Earthquake Pi.

Make your own

Find a full breakdown of the build, including ingredients, code, and future plans on Instructables. And while you’re there, be sure to check out Russell’s other Raspberry Pi–based projects, such as PiTextReader, a DIY text-to-speech reader; and the aforementioned Earthquake Pi, a light-flashing, box-rattling earthquake indicator for your desk.

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OTON GLASS: turning text to speech

2018-02-23 Alex Bate

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/oton-glass/

With OTON GLASS, users are able to capture text with a blink and have it read back to them in their chosen language. It’s wonderful tool for people with dyslexia or poor vision, or for travellers abroad.

OTON GLASS
A wearable device for people who have difficulty reading.

OTON GLASS

Inspired by his father’s dyslexia, Keisuke Shimakage of the Media Creation Research Department at the Institute of Advanced Media Arts and Sciences, Japan, began to develop OTON GLASS:

I was determined to develop OTON GLASS because of my father’s dyslexia experience. In 2012, my father had a brain tumor, and developed dyslexia after his operation — the catalyst for OTON GLASS. Fortunately, he recovered fully after rehabilitation. However, many people have congenital dyslexia regardless of their health.

Assembling a team of engineers and designers, Keisuke got to work.

The OTON GLASS device includes a Raspberry Pi 3, two cameras, and an earphone. One camera on the inside of the frame tracks the user’s eyes, and when it detects the blinked trigger, the outward-facing camera captures an image of what the user is looking at. This image is then processed by the Raspberry Pi via a program that performs optical character recognition. If the Pi detects written words, it converts them to speech, which the earphone plays back for the user.

The initial prototype of OTON GLASS had a 15-second delay between capturing text and replaying audio. This was cut down to three seconds in the team’s second prototype, designed in CAD software and housed within a 3D-printed case. The makers were then able to do real-world testing of the prototype to collect feedback from dyslexic users, and continued to upgrade the device based on user opinions.

Awards buzz

OTON GLASS is on its way to public distribution this year, and is currently doing the rounds at various trade and tech shows throughout Japan. Models are also available for trial at the Japan Blind Party Association, Kobe Eye Centre, and Nippon Keihan Library. In 2016, the device was runner-up for the James Dyson Award, and it has also garnered attention at various other awards shows and in the media. We’re looking forward to getting out hands on OTON GLASS, and we can’t wait to find out where team will take this device in the future.

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Running ActiveMQ in a Hybrid Cloud Environment with Amazon MQ

2018-02-20 Tara Van Unen

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/running-activemq-in-a-hybrid-cloud-environment-with-amazon-mq/

This post courtesy of Greg Share, AWS Solutions Architect

Many organizations, particularly enterprises, rely on message brokers to connect and coordinate different systems. Message brokers enable distributed applications to communicate with one another, serving as the technological backbone for their IT environment, and ultimately their business services. Applications depend on messaging to work.

In many cases, those organizations have started to build new or “lift and shift” applications to AWS. In some cases, there are applications, such as mainframe systems, too costly to migrate. In these scenarios, those on-premises applications still need to interact with cloud-based components.

Amazon MQ is a managed message broker service for ActiveMQ that enables organizations to send messages between applications in the cloud and on-premises to enable hybrid environments and application modernization. For example, you can invoke AWS Lambda from queues and topics managed by Amazon MQ brokers to integrate legacy systems with serverless architectures. ActiveMQ is an open-source message broker written in Java that is packaged with clients in multiple languages, Java Message Server (JMS) client being one example.

This post shows you can use Amazon MQ to integrate on-premises and cloud environments using the network of brokers feature of ActiveMQ. It provides configuration parameters for a one-way duplex connection for the flow of messages from an on-premises ActiveMQ message broker to Amazon MQ.

ActiveMQ and the network of brokers

First, look at queues within ActiveMQ and then at the network of brokers as a mechanism to distribute messages.

The network of brokers behaves differently from models such as physical networks. The key consideration is that the production (sending) of a message is disconnected from the consumption of that message. Think of the delivery of a parcel: The parcel is sent by the supplier (producer) to the end customer (consumer). The path it took to get there is of little concern to the customer, as long as it receives the package.

The same logic can be applied to the network of brokers. Here’s how you build the flow from a simple message to a queue and build toward a network of brokers. Before you look at setting up a hybrid connection, I discuss how a broker processes messages in a simple scenario.

When a message is sent from a producer to a queue on a broker, the following steps occur:

A message is sent to a queue from the producer.
The broker persists this in its store or journal.
At this point, an acknowledgement (ACK) is sent to the producer from the broker.

When a consumer looks to consume the message from that same queue, the following steps occur:

The message listener (consumer) calls the broker, which creates a subscription to the queue.
Messages are fetched from the message store and sent to the consumer.
The consumer acknowledges that the message has been received before processing it.
Upon receiving the ACK, the broker sets the message as having been consumed. By default, this deletes it from the queue.
- You can set the consumer to ACK after processing by setting up transaction management or handle it manually using Session.CLIENT_ACKNOWLEDGE.

Static propagation

I now introduce the concept of static propagation with the network of brokers as the mechanism for message transfer from on-premises brokers to Amazon MQ. Static propagation refers to message propagation that occurs in the absence of subscription information. In this case, the objective is to transfer messages arriving at your selected on-premises broker to the Amazon MQ broker for consumption within the cloud environment.

After you configure static propagation with a network of brokers, the following occurs:

The on-premises broker receives a message from a producer for a specific queue.
The on-premises broker sends (statically propagates) the message to the Amazon MQ broker.
The Amazon MQ broker sends an acknowledgement to the on-premises broker, which marks the message as having been consumed.
Amazon MQ holds the message in its queue ready for consumption.
A consumer connects to Amazon MQ broker, subscribes to the queue in which the message resides, and receives the message.
Amazon MQ broker marks the message as having been consumed.

Getting started

The first step is creating an Amazon MQ broker.

Sign in to the Amazon MQ console and launch a new Amazon MQ broker.
Name your broker and choose Next step.
For Broker instance type, choose your instance size:
– mq.t2.micro
– mq.m4.large
For Deployment mode, enter one of the following:
– Single-instance broker for development and test implementations (recommended)
– Active/standby broker for high availability in production environments
Scroll down and enter your user name and password.
Expand Advanced Settings.
For VPC, Subnet, and Security Group, pick the values for the resources in which your broker will reside.
For Public Accessibility, choose Yes, as connectivity is internet-based. Another option would be to use private connectivity between your on-premises network and the VPC, an example being an AWS Direct Connect or VPN connection. In that case, you could set Public Accessibility to No.
For Maintenance, leave the default value, No preference.
Choose Create Broker. Wait several minutes for the broker to be created.

After creation is complete, you see your broker listed.

For connectivity to work, you must configure the security group where Amazon MQ resides. For this post, I focus on the OpenWire protocol.

For Openwire connectivity, allow port 61617 access for Amazon MQ from your on-premises ActiveMQ broker source IP address. For alternate protocols, see the Amazon MQ broker configuration information for the ports required:

OpenWire – ssl://xxxxxxx.xxx.com:61617
AMQP – amqp+ssl:// xxxxxxx.xxx.com:5671
STOMP – stomp+ssl:// xxxxxxx.xxx.com:61614
MQTT – mqtt+ssl:// xxxxxxx.xxx.com:8883
WSS – wss:// xxxxxxx.xxx.com:61619

Configuring the network of brokers

Configuring the network of brokers with static propagation occurs on the on-premises broker by applying changes to the following file:
<activemq install directory>/conf activemq.xml

Network connector

This is the first configuration item required to enable a network of brokers. It is only required on the on-premises broker, which initiates and creates the connection with Amazon MQ. This connection, after it’s established, enables the flow of messages in either direction between the on-premises broker and Amazon MQ. The focus of this post is the uni-directional flow of messages from the on-premises broker to Amazon MQ.

The default activemq.xml file does not include the network connector configuration. Add this with the networkConnector element. In this scenario, edit the on-premises broker activemq.xml file to include the following information between <systemUsage> and <transportConnectors>:

<networkConnectors>
             <networkConnector 
                name="Q:source broker name->target broker name"
                duplex="false" 
                uri="static:(ssl:// aws mq endpoint:61617)" 
                userName="username"
                password="password" 
                networkTTL="2" 
                dynamicOnly="false">
                <staticallyIncludedDestinations>
                    <queue physicalName="queuename"/>
                </staticallyIncludedDestinations> 
                <excludedDestinations>
                      <queue physicalName=">" />
                </excludedDestinations>
             </networkConnector> 
     <networkConnectors>

The highlighted components are the most important elements when configuring your on-premises broker.

name – Name of the network bridge. In this case, it specifies two things:
- That this connection relates to an ActiveMQ queue (Q) as opposed to a topic (T), for reference purposes.
- The source broker and target broker.
duplex –Setting this to false ensures that messages traverse uni-directionally from the on-premises broker to Amazon MQ.
uri –Specifies the remote endpoint to which to connect for message transfer. In this case, it is an Openwire endpoint on your Amazon MQ broker. This information could be obtained from the Amazon MQ console or via the API.
username and password – The same username and password configured when creating the Amazon MQ broker, and used to access the Amazon MQ ActiveMQ console.
networkTTL – Number of brokers in the network through which messages and subscriptions can pass. Leave this setting at the current value, if it is already included in your broker connection.
staticallyIncludedDestinations > queue physicalName – The destination ActiveMQ queue for which messages are destined. This is the queue that is propagated from the on-premises broker to the Amazon MQ broker for message consumption.

After the network connector is configured, you must restart the ActiveMQ service on the on-premises broker for the changes to be applied.

Verify the configuration

There are a number of places within the ActiveMQ console of your on-premises and Amazon MQ brokers to browse to verify that the configuration is correct and the connection has been established.

On-premises broker

Launch the ActiveMQ console of your on-premises broker and navigate to Network. You should see an active network bridge similar to the following:

This identifies that the connection between your on-premises broker and your Amazon MQ broker is up and running.

Now navigate to Connections and scroll to the bottom of the page. Under the Network Connectors subsection, you should see a connector labeled with the name: value that you provided within the ActiveMQ.xml configuration file. You should see an entry similar to:

Amazon MQ broker

Launch the ActiveMQ console of your Amazon MQ broker and navigate to Connections. Scroll to the Connections openwire subsection and you should see a connection specified that references the name: value that you provided within the ActiveMQ.xml configuration file. You should see an entry similar to:

If you configured the uri: for AMQP, STOMP, MQTT, or WSS as opposed to Openwire, you would see this connection under the corresponding section of the Connections page.

Testing your message flow

The setup described outlines a way for messages produced on premises to be propagated to the cloud for consumption in the cloud. This section provides steps on verifying the message flow.

Verify that the queue has been created

After you specify this queue name as staticallyIncludedDestinations > queue physicalName: and your ActiveMQ service starts, you see the following on your on-premises ActiveMQ console Queues page.

As you can see, no messages have been sent but you have one consumer listed. If you then choose Active Consumers under the Views column, you see Active Consumers for TestingQ.

This is telling you that your Amazon MQ broker is a consumer of your on-premises broker for the testing queue.

Produce and send a message to the on-premises broker

Now, produce a message on an on-premises producer and send it to your on-premises broker to a queue named TestingQ. If you navigate back to the queues page of your on-premises ActiveMQ console, you see that the messages enqueued and messages dequeued column count for your TestingQ queue have changed:

What this means is that the message originating from the on-premises producer has traversed the on-premises broker and propagated immediately to the Amazon MQ broker. At this point, the message is no longer available for consumption from the on-premises broker.

If you access the ActiveMQ console of your Amazon MQ broker and navigate to the Queues page, you see the following for the TestingQ queue:

This means that the message originally sent to your on-premises broker has traversed the network of brokers unidirectional network bridge, and is ready to be consumed from your Amazon MQ broker. The indicator is the Number of Pending Messages column.

Consume the message from an Amazon MQ broker

Connect to the Amazon MQ TestingQ queue from a consumer within the AWS Cloud environment for message consumption. Log on to the ActiveMQ console of your Amazon MQ broker and navigate to the Queue page:

As you can see, the Number of Pending Messages column figure has changed to 0 as that message has been consumed.

This diagram outlines the message lifecycle from the on-premises producer to the on-premises broker, traversing the hybrid connection between the on-premises broker and Amazon MQ, and finally consumption within the AWS Cloud.

Conclusion

This post focused on an ActiveMQ-specific scenario for transferring messages within an ActiveMQ queue from an on-premises broker to Amazon MQ.

For other on-premises brokers, such as IBM MQ, another approach would be to run ActiveMQ on-premises broker and use JMS bridging to IBM MQ, while using the approach in this post to forward to Amazon MQ. Yet another approach would be to use Apache Camel for more sophisticated routing.

I hope that you have found this example of hybrid messaging between an on-premises environment in the AWS Cloud to be useful. Many customers are already using on-premises ActiveMQ brokers, and this is a great use case to enable hybrid cloud scenarios.

To learn more, see the Amazon MQ website and Developer Guide. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

2017’s “Piracy is Dangerous” Rhetoric Was Digital Reefer Madness

2017-12-30 Andy

Post Syndicated from Andy original https://torrentfreak.com/2017s-piracy-is-dangerous-rhetoric-was-digital-reefer-madness-171230/

On dozens of occasions during the past year, TF has been compelled to cover the latest entertainment industry anti-piracy scare campaigns. We never have a problem doing so since news is to be reported and we’re all adults with our own minds to evaluate what we’re reading.

Unfortunately, many people behind these efforts seem to be under the impression that their target audience is comprised of simpletons, none of whom are blessed with a brain of their own. Frankly it’s insulting but before we go on, let’s get a few things clear.

Copyright infringement – including uploading, downloading, sharing or streaming – is illegal in most countries. That means that copyright holders are empowered under law to do something about those offenses, either through the civil or criminal courts. While unpalatable to some, most people accept that position and understand that should they be caught in the act, there might be some consequences.

With that said, there are copyright holders out there that need to stop treating people like children at best, idiots at worst. At this point in 2017, there’s no adult out there with the ability to pirate that truly believes that obtaining or sharing the latest movies, TV shows and sports is likely to be completely legal.

If you don’t believe me, ask a pirate why he or she is so excited by their fully-loaded Kodi setup. Hint: It’s because they’re getting content for free and they know full well that isn’t what the copyright holder wants. Then ask them if they want the copyright holder to know their name, address and everything they’ve downloaded. There. That’s your answer.

The point is that these people are not dumb. They know what they’re doing and understand that getting caught is something that might possibly happen. They may not understand precisely how and they may consider the risk to be particularly small (they’d be right too) but they know that it’s something best kept fairly quiet when they aren’t shouting about it to anyone who will listen down the pub.

Copyright holders aren’t dumb either. They know only too well that pirates recognize what they’re doing is probably illegal but they’re at a loss as to what to do about it. For reputable content owners, suing is expensive, doesn’t scale, is a public relations nightmare and, moreover, isn’t effective in solving the problem.

So, we now have a concerted effort to convince pirates that piracy is not only bad for their computers but also bad for their lives. It’s a stated industry aim and we’re going to see more of it in 2018.

If pirate sites aren’t infecting people’s computers with malware from God-knows-where, they’re stealing their identities and emptying their bank accounts, the industries warned in 2017. And if somehow people manage to run this gauntlet of terror without damaging their technology or their finances, then they’ll probably have their house burnt down by an exploding set-top box.

Look, the intention is understandable. Entertainment companies need to contain the piracy problem because if they don’t, it only gets worse. Again, there are few people out there who genuinely expect them to do anything different but this current stampede towards blatant scaremongering is disingenuous at best and utterly ridiculous at worst.

And it won’t work.

While piracy can be engaged in as a solo activity, it’s inherently a social phenomenon. That things can be pirated from here and there, in this way and that, is the stuff of conversations between friends and colleagues, in person and via social media. The information is passed around today like VHS and compact cassettes were passed around three decades ago and people really aren’t talking about malware or their houses catching fire.

In the somewhat unlikely event these topics do get raised for more than a minute, they get dealt with in the same way as anything else.

People inquire whether their friends have ever had their bank accounts emptied or houses burnt down, or if they know anyone who has. When the answer comes back as “no” from literally everyone, people are likely to conclude that the stories are being spread by people trying to stop them getting movies, TV shows, and live sports for free. And they would be right.

That’s not to say that these scare stories don’t have at least some basis in fact, they do.

Many pirate sites do have low-tier advertising which can put users at risk. However, it’s nothing that a decent anti-virus program and/or ad blocker can’t handle, which is something everyone should be running when accessing untrusted sites. Also, being cautious about all electronics imported from overseas is something people should be aware of too, despite the tiny risk these devices appear to pose in the scheme of things.

So, what we have here is the modern day equivalent of Reefer Madness, the 1930’s propaganda movie that tried to scare people away from marijuana with tales of car accidents, suicide, attempted rape and murder.

While somewhat more refined, these modern-day cautionary messages over piracy are destined to fall on ears that are far more shrewd and educated than their 20th-century counterparts. Yet they’re all born out of the same desire, to stop people from getting involved in an activity by warning them that it’s dangerous to them, rather than it having a negative effect on someone else – an industry executive, for example.

It’s all designed to appeal to the selfish nature of people, rather than their empathy for others, but that’s a big mistake.

Most people really do want to do the right thing, as the staggering success of Netflix, iTunes, Spotify, and Amazon show. But the ridiculous costs and/or inaccessibility of live sports, latest movies, or packaged TV shows mean that no matter what warnings get thrown out there, some people will still cut corners if they feel they’re being taken advantage of.

Worst still, if they believe the scare stories are completely ridiculous, eventually they’ll also discount the credibility of the messenger. When that happens, what little trust remains will be eroded.

Then, let’s face it, who wants to buy something from people you can’t trust?

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

AWS Big Data & Analytics Sessions at Re:Invent 2017

2017-11-24 Roy Ben-Alta

Post Syndicated from Roy Ben-Alta original https://aws.amazon.com/blogs/big-data/aws-big-data-analytics-sessions-at-reinvent-2017/

We can’t believe that there are just few days left before re:Invent 2017. If you are attending this year, you’ll want to check out our Big Data sessions! The Big Data and Machine Learning categories are bigger than ever. As in previous years, you can find these sessions in various tracks, including Analytics & Big Data, Deep Learning Summit, Artificial Intelligence & Machine Learning, Architecture, and Databases.

We have great sessions from organizations and companies like Vanguard, Cox Automotive, Pinterest, Netflix, FINRA, Amtrak, AmazonFresh, Sysco Foods, Twilio, American Heart Association, Expedia, Esri, Nextdoor, and many more. All sessions are recorded and made available on YouTube. In addition, all slide decks from the sessions will be available on SlideShare.net after the conference.

This post highlights the sessions that will be presented as part of the Analytics & Big Data track, as well as relevant sessions from other tracks like Architecture, Artificial Intelligence & Machine Learning, and IoT. If you’re interested in Machine Learning sessions, don’t forget to check out our Guide to Machine Learning at re:Invent 2017.

This year’s session catalog contains the following breakout sessions.

DAT201 – AWS Database and Analytics State of the Union – Thursday

Raju Gulabani, VP, Database, Analytics and AI at AWS will discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.

Deep dive customer use cases

ABD401 – How Netflix Monitors Applications in Near Real-Time with Amazon Kinesis
Thousands of services work in concert to deliver millions of hours of video streams to Netflix customers every day. These applications vary in size, function, and technology, but they all make use of the Netflix network to communicate. Understanding the interactions between these services is a daunting challenge both because of the sheer volume of traffic and the dynamic nature of deployments. In this session, we first discuss why Netflix chose Kinesis Streams to address these challenges at scale. We then dive deep into how Netflix uses Kinesis Streams to enrich network traffic logs and identify usage patterns in real time. Lastly, we cover how Netflix uses this system to build comprehensive dependency maps, increase network efficiency, and improve failure resiliency. From this session, you will learn how to build a real-time application monitoring system using network traffic logs and get real-time, actionable insights.

SRV319 – How Nextdoor Built a Scalable, Serverless Data Pipeline for Billions of Events per Day

In this session, learn how Nextdoor replaced their home-grown data pipeline based on a topology of Flume nodes with a completely serverless architecture based on Kinesis and Lambda. By making these changes, they improved both the reliability of their data and the delivery times of billions of records of data to their Amazon S3–based data lake and Amazon Redshift cluster. Nextdoor is a private social networking service for neighborhoods.

ABD205 – Taking a Page Out of Ivy Tech’s Book: Using Data for Student Success
Data speaks. Discover how Ivy Tech, the nation’s largest singly accredited community college, uses AWS to gather, analyze, and take action on student behavioral data for the betterment of over 3,100 students. This session outlines the process from inception to implementation across the state of Indiana and highlights how Ivy Tech’s model can be applied to your own complex business problems.

ABD207 – Leveraging AWS to Fight Financial Crime and Protect National Security
Banks aren’t known to share data and collaborate with one another. But that is exactly what the Mid-Sized Bank Coalition of America (MBCA) is doing to fight digital financial crime—and protect national security. Using the AWS Cloud, the MBCA developed a shared data analytics utility that processes terabytes of non-competitive customer account, transaction, and government risk data. The intelligence produced from the data helps banks increase the efficiency of their operations, cut labor and operating costs, and reduce false positive volumes. The collective intelligence also allows greater enforcement of Anti-Money Laundering (AML) regulations by helping members detect internal risks—and identify the challenges to detecting these risks in the first place. This session demonstrates how the AWS Cloud supports the MBCA to deliver advanced data analytics, provide consistent operating models across financial institutions, reduce costs, and strengthen national security.

ABD208 – Cox Automotive Empowered to Scale with Splunk Cloud & AWS and Explores New Innovation with Amazon Kinesis Firehose
In this session, learn how Cox Automotive is using Splunk Cloud for real time visibility into its AWS and hybrid environments to achieve near instantaneous MTTI, reduce auction incidents by 90%, and proactively predict outages. We also introduce a highly anticipated capability that allows you to ingest, transform, and analyze data in real time using Splunk and Amazon Kinesis Firehose to gain valuable insights from your cloud resources. It’s now quicker and easier than ever to gain access to analytics-driven infrastructure monitoring using Splunk Enterprise & Splunk Cloud.

ABD209 – Accelerating the Speed of Innovation with a Data Sciences Data & Analytics Hub at Takeda
Historically, silos of data, analytics, and processes across functions, stages of development, and geography created a barrier to R&D efficiency. Gathering the right data necessary for decision-making was challenging due to issues of accessibility, trust, and timeliness. In this session, learn how Takeda is undergoing a transformation in R&D to increase the speed-to-market of high-impact therapies to improve patient lives. The Data and Analytics Hub was built, with Deloitte, to address these issues and support the efficient generation of data insights for functions such as clinical operations, clinical development, medical affairs, portfolio management, and R&D finance. In the AWS hosted data lake, this data is processed, integrated, and made available to business end users through data visualization interfaces, and to data scientists through direct connectivity. Learn how Takeda has achieved significant time reductions—from weeks to minutes—to gather and provision data that has the potential to reduce cycle times in drug development. The hub also enables more efficient operations and alignment to achieve product goals through cross functional team accountability and collaboration due to the ability to access the same cross domain data.

ABD210 – Modernizing Amtrak: Serverless Solution for Real-Time Data Capabilities
As the nation’s only high-speed intercity passenger rail provider, Amtrak needs to know critical information to run their business such as: Who’s onboard any train at any time? How are booking and revenue trending? Amtrak was faced with unpredictable and often slow response times from existing databases, ranging from seconds to hours; existing booking and revenue dashboards were spreadsheet-based and manual; multiple copies of data were stored in different repositories, lacking integration and consistency; and operations and maintenance (O&M) costs were relatively high. Join us as we demonstrate how Deloitte and Amtrak successfully went live with a cloud-native operational database and analytical datamart for near-real-time reporting in under six months. We highlight the specific challenges and the modernization of architecture on an AWS native Platform as a Service (PaaS) solution. The solution includes cloud-native components such as AWS Lambda for microservices, Amazon Kinesis and AWS Data Pipeline for moving data, Amazon S3 for storage, Amazon DynamoDB for a managed NoSQL database service, and Amazon Redshift for near-real time reports and dashboards. Deloitte’s solution enabled “at scale” processing of 1 million transactions/day and up to 2K transactions/minute. It provided flexibility and scalability, largely eliminate the need for system management, and dramatically reduce operating costs. Moreover, it laid the groundwork for decommissioning legacy systems, anticipated to save at least $1M over 3 years.

ABD211 – Sysco Foods: A Journey from Too Much Data to Curated Insights
In this session, we detail Sysco’s journey from a company focused on hindsight-based reporting to one focused on insights and foresight. For this shift, Sysco moved from multiple data warehouses to an AWS ecosystem, including Amazon Redshift, Amazon EMR, AWS Data Pipeline, and more. As the team at Sysco worked with Tableau, they gained agile insight across their business. Learn how Sysco decided to use AWS, how they scaled, and how they became more strategic with the AWS ecosystem and Tableau.

ABD217 – From Batch to Streaming: How Amazon Flex Uses Real-time Analytics to Deliver Packages on Time
Reducing the time to get actionable insights from data is important to all businesses, and customers who employ batch data analytics tools are exploring the benefits of streaming analytics. Learn best practices to extend your architecture from data warehouses and databases to real-time solutions. Learn how to use Amazon Kinesis to get real-time data insights and integrate them with Amazon Aurora, Amazon RDS, Amazon Redshift, and Amazon S3. The Amazon Flex team describes how they used streaming analytics in their Amazon Flex mobile app, used by Amazon delivery drivers to deliver millions of packages each month on time. They discuss the architecture that enabled the move from a batch processing system to a real-time system, overcoming the challenges of migrating existing batch data to streaming data, and how to benefit from real-time analytics.

ABD218 – How EuroLeague Basketball Uses IoT Analytics to Engage Fans
IoT and big data have made their way out of industrial applications, general automation, and consumer goods, and are now a valuable tool for improving consumer engagement across a number of industries, including media, entertainment, and sports. The low cost and ease of implementation of AWS analytics services and AWS IoT have allowed AGT, a leader in IoT, to develop their IoTA analytics platform. Using IoTA, AGT brought a tailored solution to EuroLeague Basketball for real-time content production and fan engagement during the 2017-18 season. In this session, we take a deep dive into how this solution is architected for secure, scalable, and highly performant data collection from athletes, coaches, and fans. We also talk about how the data is transformed into insights and integrated into a content generation pipeline. Lastly, we demonstrate how this solution can be easily adapted for other industries and applications.

ABD222 – How to Confidently Unleash Data to Meet the Needs of Your Entire Organization
Where are you on the spectrum of IT leaders? Are you confident that you’re providing the technology and solutions that consistently meet or exceed the needs of your internal customers? Do your peers at the executive table see you as an innovative technology leader? Innovative IT leaders understand the value of getting data and analytics directly into the hands of decision makers, and into their own. In this session, Daren Thayne, Domo’s Chief Technology Officer, shares how innovative IT leaders are helping drive a culture change at their organizations. See how transformative it can be to have real-time access to all of the data that’ is relevant to YOUR job (including a complete view of your entire AWS environment), as well as understand how it can help you lead the way in applying that same pattern throughout your entire company

ABD303 – Developing an Insights Platform – Sysco’s Journey from Disparate Systems to Data Lake and Beyond
Sysco has nearly 200 operating companies across its multiple lines of business throughout the United States, Canada, Central/South America, and Europe. As the global leader in food services, Sysco identified the need to streamline the collection, transformation, and presentation of data produced by the distributed units and systems, into a central data ecosystem. Sysco’s Business Intelligence and Analytics team addressed these requirements by creating a data lake with scalable analytics and query engines leveraging AWS services. In this session, Sysco will outline their journey from a hindsight reporting focused company to an insights driven organization. They will cover solution architecture, challenges, and lessons learned from deploying a self-service insights platform. They will also walk through the design patterns they used and how they designed the solution to provide predictive analytics using Amazon Redshift Spectrum, Amazon S3, Amazon EMR, AWS Glue, Amazon Elasticsearch Service and other AWS services.

ABD309 – How Twilio Scaled Its Data-Driven Culture
As a leading cloud communications platform, Twilio has always been strongly data-driven. But as headcount and data volumes grew—and grew quickly—they faced many new challenges. One-off, static reports work when you’re a small startup, but how do you support a growth stage company to a successful IPO and beyond? Today, Twilio’s data team relies on AWS and Looker to provide data access to 700 colleagues. Departments have the data they need to make decisions, and cloud-based scale means they get answers fast. Data delivers real-business value at Twilio, providing a 360-degree view of their customer, product, and business. In this session, you hear firsthand stories directly from the Twilio data team and learn real-world tips for fostering a truly data-driven culture at scale.

ABD310 – How FINRA Secures Its Big Data and Data Science Platform on AWS
FINRA uses big data and data science technologies to detect fraud, market manipulation, and insider trading across US capital markets. As a financial regulator, FINRA analyzes highly sensitive data, so information security is critical. Learn how FINRA secures its Amazon S3 Data Lake and its data science platform on Amazon EMR and Amazon Redshift, while empowering data scientists with tools they need to be effective. In addition, FINRA shares AWS security best practices, covering topics such as AMI updates, micro segmentation, encryption, key management, logging, identity and access management, and compliance.

ABD331 – Log Analytics at Expedia Using Amazon Elasticsearch Service
Expedia uses Amazon Elasticsearch Service (Amazon ES) for a variety of mission-critical use cases, ranging from log aggregation to application monitoring and pricing optimization. In this session, the Expedia team reviews how they use Amazon ES and Kibana to analyze and visualize Docker startup logs, AWS CloudTrail data, and application metrics. They share best practices for architecting a scalable, secure log analytics solution using Amazon ES, so you can add new data sources almost effortlessly and get insights quickly

ABD316 – American Heart Association: Finding Cures to Heart Disease Through the Power of Technology
Combining disparate datasets and making them accessible to data scientists and researchers is a prevalent challenge for many organizations, not just in healthcare research. American Heart Association (AHA) has built a data science platform using Amazon EMR, Amazon Elasticsearch Service, and other AWS services, that corrals multiple datasets and enables advanced research on phenotype and genotype datasets, aimed at curing heart diseases. In this session, we present how AHA built this platform and the key challenges they addressed with the solution. We also provide a demo of the platform, and leave you with suggestions and next steps so you can build similar solutions for your use cases

ABD319 – Tooling Up for Efficiency: DIY Solutions @ Netflix
At Netflix, we have traditionally approached cloud efficiency from a human standpoint, whether it be in-person meetings with the largest service teams or manually flipping reservations. Over time, we realized that these manual processes are not scalable as the business continues to grow. Therefore, in the past year, we have focused on building out tools that allow us to make more insightful, data-driven decisions around capacity and efficiency. In this session, we discuss the DIY applications, dashboards, and processes we built to help with capacity and efficiency. We start at the ten thousand foot view to understand the unique business and cloud problems that drove us to create these products, and discuss implementation details, including the challenges encountered along the way. Tools discussed include Picsou, the successor to our AWS billing file cost analyzer; Libra, an easy-to-use reservation conversion application; and cost and efficiency dashboards that relay useful financial context to 50+ engineering teams and managers.

ABD312 – Deep Dive: Migrating Big Data Workloads to AWS
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to AWS in order to save costs, increase availability, and improve performance. AWS offers a broad set of analytics services, including solutions for batch processing, stream processing, machine learning, data workflow orchestration, and data warehousing. This session will focus on identifying the components and workflows in your current environment; and providing the best practices to migrate these workloads to the right AWS data analytics product. We will cover services such as Amazon EMR, Amazon Athena, Amazon Redshift, Amazon Kinesis, and more. We will also feature Vanguard, an American investment management company based in Malvern, Pennsylvania with over $4.4 trillion in assets under management. Ritesh Shah, Sr. Program Manager for Cloud Analytics Program at Vanguard, will describe how they orchestrated their migration to AWS analytics services, including Hadoop and Spark workloads to Amazon EMR. Ritesh will highlight the technical challenges they faced and overcame along the way, as well as share common recommendations and tuning tips to accelerate the time to production.

ABD402 – How Esri Optimizes Massive Image Archives for Analytics in the Cloud
Petabyte scale archives of satellites, planes, and drones imagery continue to grow exponentially. They mostly exist as semi-structured data, but they are only valuable when accessed and processed by a wide range of products for both visualization and analysis. This session provides an overview of how ArcGIS indexes and structures data so that any part of it can be quickly accessed, processed, and analyzed by reading only the minimum amount of data needed for the task. In this session, we share best practices for structuring and compressing massive datasets in Amazon S3, so it can be analyzed efficiently. We also review a number of different image formats, including GeoTIFF (used for the Public Datasets on AWS program, Landsat on AWS), cloud optimized GeoTIFF, MRF, and CRF as well as different compression approaches to show the effect on processing performance. Finally, we provide examples of how this technology has been used to help image processing and analysis for the response to Hurricane Harvey.

ABD329 – A Look Under the Hood – How Amazon.com Uses AWS Services for Analytics at Massive Scale
Amazon’s consumer business continues to grow, and so does the volume of data and the number and complexity of the analytics done in support of the business. In this session, we talk about how Amazon.com uses AWS technologies to build a scalable environment for data and analytics. We look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel, scalable compute engines such as Amazon EMR and Amazon Redshift.

ABD327 – Migrating Your Traditional Data Warehouse to a Modern Data Lake
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.

MCL202 – Ally Bank & Cognizant: Transforming Customer Experience Using Amazon Alexa
Given the increasing popularity of natural language interfaces such as Voice as User technology or conversational artificial intelligence (AI), Ally® Bank was looking to interact with customers by enabling direct transactions through conversation or voice. They also needed to develop a capability that allows third parties to connect to the bank securely for information sharing and exchange, using oAuth, an authentication protocol seen as the future of secure banking technology. Cognizant’s Architecture team partnered with Ally Bank’s Enterprise Architecture group and identified the right product for oAuth integration with Amazon Alexa and third-party technologies. In this session, we discuss how building products with conversational AI helps Ally Bank offer an innovative customer experience; increase retention through improved data-driven personalization; increase the efficiency and convenience of customer service; and gain deep insights into customer needs through data analysis and predictive analytics to offer new products and services.

MCL317 – Orchestrating Machine Learning Training for Netflix Recommendations
At Netflix, we use machine learning (ML) algorithms extensively to recommend relevant titles to our 100+ million members based on their tastes. Everything on the member home page is an evidence-driven, A/B-tested experience that we roll out backed by ML models. These models are trained using Meson, our workflow orchestration system. Meson distinguishes itself from other workflow engines by handling more sophisticated execution graphs, such as loops and parameterized fan-outs. Meson can schedule Spark jobs, Docker containers, bash scripts, gists of Scala code, and more. Meson also provides a rich visual interface for monitoring active workflows and inspecting execution logs. It has a powerful Scala DSL for authoring workflows as well as the REST API. In this session, we focus on how Meson trains recommendation ML models in production, and how we have re-architected it to scale up for a growing need of broad ETL applications within Netflix. As a driver for this change, we have had to evolve the persistence layer for Meson. We talk about how we migrated from Cassandra to Amazon RDS backed by Amazon Aurora

MCL350 – Humans vs. the Machines: How Pinterest Uses Amazon Mechanical Turk’s Worker Community to Improve Machine Learning
Ever since the term “crowdsourcing” was coined in 2006, it’s been a buzzword for technology companies and social institutions. In the technology sector, crowdsourcing is instrumental for verifying machine learning algorithms, which, in turn, improves the user’s experience. In this session, we explore how Pinterest adapted to an increased reliability on human evaluation to improve their product, with a focus on how they’ve integrated with Mechanical Turk’s platform. This presentation is aimed at engineers, analysts, program managers, and product managers who are interested in how companies rely on Mechanical Turk’s human evaluation platform to better understand content and improve machine learning algorithms. The discussion focuses on the analysis and product decisions related to building a high quality crowdsourcing system that takes advantage of Mechanical Turk’s powerful worker community.

Service sessions: architecture and best practices

Featuring Amazon EMR, Amazon Athena, Amazon Kinesis, AWS Glue, Amazon QuickSight, Amazon Redshift, Amazon Elasticsearch Service, and Amazon DynamoDB.

ABD201 – Big Data Architectural Patterns and Best Practices on AWS
In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost

ABD202 – Best Practices for Building Serverless Big Data Applications
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.

ABD206 – Building Visualizations and Dashboards with Amazon QuickSight
Just as a picture is worth a thousand words, a visual is worth a thousand data points. A key aspect of our ability to gain insights from our data is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe. In this session, we will show you how to quickly and easily visualize your data using Amazon QuickSight. We will show you how you can connect to data sources, generate custom metrics and calculations, create comprehensive business dashboards with various chart types, and setup filters and drill downs to slice and dice the data.

ABD203 – Real-Time Streaming Applications on AWS: Use Cases and Patterns
To win in the marketplace and provide differentiated customer experiences, businesses need to be able to use live data in real time to facilitate fast decision making. In this session, you learn common streaming data processing use cases and architectures. First, we give an overview of streaming data and AWS streaming data capabilities. Next, we look at a few customer examples and their real-time streaming applications. Finally, we walk through common architectures and design patterns of top streaming data use cases.

ABD213 – How to Build a Data Lake with AWS Glue Data Catalog
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

ABD214 – Real-time User Insights for Mobile and Web Applications with Amazon Pinpoint
With customers demanding relevant and real-time experiences across a range of devices, digital businesses are looking to gather user data at scale, understand this data, and respond to customer needs instantly. This requires tools that can record large volumes of user data in a structured fashion, and then instantly make this data available to generate insights. In this session, we demonstrate how you can use Amazon Pinpoint to capture user data in a structured yet flexible manner. Further, we demonstrate how this data can be set up for instant consumption using services like Amazon Kinesis Firehose and Amazon Redshift. We walk through example data based on real world scenarios, to illustrate how Amazon Pinpoint lets you easily organize millions of events, record them in real-time, and store them for further analysis.

ABD223 – IT Innovators: New Technology for Leveraging Data to Enable Agility, Innovation, and Business Optimization
Companies of all sizes are looking for technology to efficiently leverage data and their existing IT investments to stay competitive and understand where to find new growth. Regardless of where companies are in their data-driven journey, they face greater demands for information by customers, prospects, partners, vendors and employees. All stakeholders inside and outside the organization want information on-demand or in “real time”, available anywhere on any device. They want to use it to optimize business outcomes without having to rely on complex software tools or human gatekeepers to relevant information. Learn how IT innovators at companies such as MasterCard, Jefferson Health, and TELUS are using Domo’s Business Cloud to help their organizations more effectively leverage data at scale.

ABD301 – Analyzing Streaming Data in Real Time with Amazon Kinesis
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we present an end-to-end streaming data solution using Kinesis Streams for data ingestion, Kinesis Analytics for real-time processing, and Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system

ABD302 – Real-Time Data Exploration and Analytics with Amazon Elasticsearch Service and Kibana
In this session, we use Apache web logs as example and show you how to build an end-to-end analytics solution. First, we cover how to configure an Amazon ES cluster and ingest data using Amazon Kinesis Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data. Then we demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we review approaches for generating custom, ad-hoc reports.

ABD304 – Best Practices for Data Warehousing with Amazon Redshift & Redshift Spectrum
Most companies are over-run with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we take an in-depth look at how modern data warehousing blends and analyzes all your data, inside and outside your data warehouse without moving the data, to give you deeper insights to run your business. We will cover best practices on how to design optimal schemas, load data efficiently, and optimize your queries to deliver high throughput and performance.

ABD305 – Design Patterns and Best Practices for Data Analytics with Amazon EMR
Amazon EMR is one of the largest Hadoop operators in the world, enabling customers to run ETL, machine learning, real-time processing, data science, and low-latency SQL at petabyte scale. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about lowering cost with Auto Scaling and Spot Instances, and security best practices for encryption and fine-grained access control. Finally, we dive into some of our recent launches to keep you current on our latest features.

ABD307 – Deep Analytics for Global AWS Marketing Organization
To meet the needs of the global marketing organization, the AWS marketing analytics team built a scalable platform that allows the data science team to deliver custom econometric and machine learning models for end user self-service. To meet data security standards, we use end-to-end data encryption and different AWS services such as Amazon Redshift, Amazon RDS, Amazon S3, Amazon EMR with Apache Spark and Auto Scaling. In this session, you see real examples of how we have scaled and automated critical analysis, such as calculating the impact of marketing programs like re:Invent and prioritizing leads for our sales teams.

ABD311 – Deploying Business Analytics at Enterprise Scale with Amazon QuickSight
One of the biggest tradeoffs customers usually make when deploying BI solutions at scale is agility versus governance. Large-scale BI implementations with the right governance structure can take months to design and deploy. In this session, learn how you can avoid making this tradeoff using Amazon QuickSight. Learn how to easily deploy Amazon QuickSight to thousands of users using Active Directory and Federated SSO, while securely accessing your data sources in Amazon VPCs or on-premises. We also cover how to control access to your datasets, implement row-level security, create scheduled email reports, and audit access to your data.

ABD315 – Building Serverless ETL Pipelines with AWS Glue
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.

ABD318 – Architecting a data lake with Amazon S3, Amazon Kinesis, and Amazon Athena
Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users – from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways. In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users.

ABD 323 Extending Analytics Beyond the Data warehouse

Companies have valuable data that they may not be analyzing due to the complexity, scalability, and performance issues of loading the data into their data warehouse. However, with the right tools, you can extend your analytics to query data in your data lake—with no loading required. Amazon Redshift Spectrum extends the analytic power of Amazon Redshift beyond data stored in your data warehouse to run SQL queries directly against vast amounts of unstructured data in your Amazon S3 data lake. This gives you the freedom to store your data where you want, in the format you want, and have it available for analytics when you need it. Join a discussion with AWS solution architects to ask question.

ABD330 – Combining Batch and Stream Processing to Get the Best of Both Worlds
Today, many architects and developers are looking to build solutions that integrate batch and real-time data processing, and deliver the best of both approaches. Lambda architecture (not to be confused with the AWS Lambda service) is a design pattern that leverages both batch and real-time processing within a single solution to meet the latency, accuracy, and throughput requirements of big data use cases. Come join us for a discussion on how to implement Lambda architecture (batch, speed, and serving layers) and best practices for data processing, loading, and performance tuning

ABD335 – Real-Time Anomaly Detection Using Amazon Kinesis
Amazon Kinesis Analytics offers a built-in machine learning algorithm that you can use to easily detect anomalies in your VPC network traffic and improve security monitoring. Join us for an interactive discussion on how to stream your VPC flow Logs to Amazon Kinesis Streams and identify anomalies using Kinesis Analytics.

ABD339 – Deep Dive and Best Practices for Amazon Athena
Amazon Athena is an interactive query service that enables you to process data directly from Amazon S3 without the need for infrastructure. Since its launch at re:invent 2016, several organizations have adopted Athena as the central tool to process all their data. In this talk, we dive deep into the most common use cases, including working with other AWS services. We review the best practices for creating tables and partitions and performance optimizations. We also dive into how Athena handles security, authorization, and authentication. Lastly, we hear from a customer who has reduced costs and improved time to market by deploying Athena across their organization.

We look forward to meeting you at re:Invent 2017!

About the Author

Roy Ben-Alta is a solution architect and principal business development manager at Amazon Web Services in New York. He focuses on Data Analytics and ML Technologies, working with AWS customers to build innovative data-driven products.

Bringing Datacenter-Scale Hardware-Software Co-design to the Cloud with FireSim and Amazon EC2 F1 Instances

2017-10-25 Mia Champion

Post Syndicated from Mia Champion original https://aws.amazon.com/blogs/compute/bringing-datacenter-scale-hardware-software-co-design-to-the-cloud-with-firesim-and-amazon-ec2-f1-instances/

The recent addition of Xilinx FPGAs to AWS Cloud compute offerings is one way that AWS is enabling global growth in the areas of advanced analytics, deep learning and AI. The customized F1 servers use pooled accelerators, enabling interconnectivity of up to 8 FPGAs, each one including 64 GiB DDR4 ECC protected memory, with a dedicated PCIe x16 connection. That makes this a powerful engine with the capacity to process advanced analytical applications at scale, at a significantly faster rate. For example, AWS commercial partner Edico Genome is able to achieve an approximately 30X speedup in analyzing whole genome sequencing datasets using their DRAGEN platform powered with F1 instances.

While the availability of FPGA F1 compute on-demand provides clear accessibility and cost advantages, many mainstream users are still finding that the “threshold to entry” in developing or running FPGA-accelerated simulations is too high. Researchers at the UC Berkeley RISE Lab have developed “FireSim”, powered by Amazon FPGA F1 instances as an open-source resource, FireSim lowers that entry bar and makes it easier for everyone to leverage the power of an FPGA-accelerated compute environment. Whether you are part of a small start-up development team or working at a large datacenter scale, hardware-software co-design enables faster time-to-deployment, lower costs, and more predictable performance. We are excited to feature FireSim in this post from Sagar Karandikar and his colleagues at UC-Berkeley.

―Mia Champion, Sr. Data Scientist, AWS

Mapping an 8-node FireSim cluster simulation to Amazon EC2 F1

As traditional hardware scaling nears its end, the data centers of tomorrow are trending towards heterogeneity, employing custom hardware accelerators and increasingly high-performance interconnects. Prototyping new hardware at scale has traditionally been either extremely expensive, or very slow. In this post, I introduce FireSim, a new hardware simulation platform under development in the computer architecture research group at UC Berkeley that enables fast, scalable hardware simulation using Amazon EC2 F1 instances.

FireSim benefits both hardware and software developers working on new rack-scale systems: software developers can use the simulated nodes with new hardware features as they would use a real machine, while hardware developers have full control over the hardware being simulated and can run real software stacks while hardware is still under development. In conjunction with this post, we’re releasing the first public demo of FireSim, which lets you deploy your own 8-node simulated cluster on an F1 Instance and run benchmarks against it. This demo simulates a pre-built “vanilla” cluster, but demonstrates FireSim’s high performance and usability.

Why FireSim + F1?

FPGA-accelerated hardware simulation is by no means a new concept. However, previous attempts to use FPGAs for simulation have been fraught with usability, scalability, and cost issues. FireSim takes advantage of EC2 F1 and open-source hardware to address the traditional problems with FPGA-accelerated simulation:
Problem #1: FPGA-based simulations have traditionally been expensive, difficult to deploy, and difficult to reproduce.
FireSim uses public-cloud infrastructure like F1, which means no upfront cost to purchase and deploy FPGAs. Developers and researchers can distribute pre-built AMIs and AFIs, as in this public demo (more details later in this post), to make experiments easy to reproduce. FireSim also automates most of the work involved in deploying an FPGA simulation, essentially enabling one-click conversion from new RTL to deploying on an FPGA cluster.

Problem #2: FPGA-based simulations have traditionally been difficult (and expensive) to scale.
Because FireSim uses F1, users can scale out experiments by spinning up additional EC2 instances, rather than spending hundreds of thousands of dollars on large FPGA clusters.

Problem #3: Finding open hardware to simulate has traditionally been difficult. Finding open hardware that can run real software stacks is even harder.
FireSim simulates RocketChip, an open, silicon-proven, RISC-V-based processor platform, and adds peripherals like a NIC and disk device to build up a realistic system. Processors that implement RISC-V automatically support real operating systems (such as Linux) and even support applications like Apache and Memcached. We provide a custom Buildroot-based FireSim Linux distribution that runs on our simulated nodes and includes many popular developer tools.

Problem #4: Writing hardware in traditional HDLs is time-consuming.
Both FireSim and RocketChip use the Chisel HDL, which brings modern programming paradigms to hardware description languages. Chisel greatly simplifies the process of building large, highly parameterized hardware components.

How to use FireSim for hardware/software co-design

FireSim drastically improves the process of co-designing hardware and software by acting as a push-button interface for collaboration between hardware developers and systems software developers. The following diagram describes the workflows that hardware and software developers use when working with FireSim.

Figure 2. The FireSim custom hardware development workflow.

The hardware developer’s view:

Write custom RTL for your accelerator, peripheral, or processor modification in a productive language like Chisel.
Run a software simulation of your hardware design in standard gate-level simulation tools for early-stage debugging.
Run FireSim build scripts, which automatically build your simulation, run it through the Vivado toolchain/AWS shell scripts, and publish an AFI.
Deploy your simulation on EC2 F1 using the generated simulation driver and AFI
Run real software builds released by software developers to benchmark your hardware

The software developer’s view:

Deploy the AMI/AFI generated by the hardware developer on an F1 instance to simulate a cluster of nodes (or scale out to many F1 nodes for larger simulated core-counts).
Connect using SSH into the simulated nodes in the cluster and boot the Linux distribution included with FireSim. This distribution is easy to customize, and already supports many standard software packages.
Directly prototype your software using the same exact interfaces that the software will see when deployed on the real future system you’re prototyping, with the same performance characteristics as observed from software, even at scale.

FireSim demo v1.0

Figure 3. Cluster topology simulated by FireSim demo v1.0.

This first public demo of FireSim focuses on the aforementioned “software-developer’s view” of the custom hardware development cycle. The demo simulates a cluster of 1 to 8 RocketChip-based nodes, interconnected by a functional network simulation. The simulated nodes work just like “real” machines: they boot Linux, you can connect to them using SSH, and you can run real applications on top. The nodes can see each other (and the EC2 F1 instance on which they’re deployed) on the network and communicate with one another. While the demo currently simulates a pre-built “vanilla” cluster, the entire hardware configuration of these simulated nodes can be modified after FireSim is open-sourced.

In this post, I walk through bringing up a single-node FireSim simulation for experienced EC2 F1 users. For more detailed instructions for new users and instructions for running a larger 8-node simulation, see FireSim Demo v1.0 on Amazon EC2 F1. Both demos walk you through setting up an instance from a demo AMI/AFI and booting Linux on the simulated nodes. The full demo instructions also walk you through an example workload, running Memcached on the simulated nodes, with YCSB as a load generator to demonstrate network functionality.

Deploying the demo on F1

In this release, we provide pre-built binaries for driving simulation from the host and a pre-built AFI that contains the FPGA infrastructure necessary to simulate a RocketChip-based node.

Starting your F1 instances

First, launch an instance using the free FireSim Demo v1.0 product available on the AWS Marketplace on an f1.2xlarge instance. After your instance has booted, log in using the user name centos. On the first login, you should see the message “FireSim network config completed.” This sets up the necessary tap interfaces and bridge on the EC2 instance to enable communicating with the simulated nodes.

AMI contents

The AMI contains a variety of tools to help you run simulations and build software for RISC-V systems, including the riscv64 toolchain, a Buildroot-based Linux distribution that runs on the simulated nodes, and the simulation driver program. For more details, see the AMI Contents section on the FireSim website.

Single-node demo

First, you need to flash the FPGA with the FireSim AFI. To do so, run:

[[email protected]_ADDR ~]$ sudo fpga-load-local-image -S 0 -I agfi-00a74c2d615134b21

To start a simulation, run the following at the command line:

[[email protected]_ADDR ~]$ boot-firesim-singlenode

This automatically calls the simulation driver, telling it to load the Linux kernel image and root filesystem for the Linux distro. This produces output similar to the following:

Simulations Started. You can use the UART console of each simulated node by attaching to the following screens:

There is a screen on:

2492.fsim0 (Detached)

1 Socket in /var/run/screen/S-centos.

You could connect to the simulated UART console by connecting to this screen, but instead opt to use SSH to access the node instead.

First, ping the node to make sure it has come online. This is currently required because nodes may get stuck at Linux boot if the NIC does not receive any network traffic. For more information, see Troubleshooting/Errata. The node is always assigned the IP address 192.168.1.10:

[[email protected]_ADDR ~]$ ping 192.168.1.10

This should eventually produce the following output:

PING 192.168.1.10 (192.168.1.10) 56(84) bytes of data.

From 192.168.1.1 icmp_seq=1 Destination Host Unreachable

…

64 bytes from 192.168.1.10: icmp_seq=1 ttl=64 time=2017 ms

64 bytes from 192.168.1.10: icmp_seq=2 ttl=64 time=1018 ms

64 bytes from 192.168.1.10: icmp_seq=3 ttl=64 time=19.0 ms

…

At this point, you know that the simulated node is online. You can connect to it using SSH with the user name root and password firesim. It is also convenient to make sure that your TERM variable is set correctly. In this case, the simulation expects TERM=linux, so provide that:

[[email protected]_ADDR ~]$ TERM=linux ssh [email protected]

The authenticity of host ‘192.168.1.10 (192.168.1.10)’ can’t be established.

ECDSA key fingerprint is 63:e9:66:d0:5c:06:2c:1d:5c:95:33:c8:36:92:30:49.

Are you sure you want to continue connecting (yes/no)? yes

Warning: Permanently added ‘192.168.1.10’ (ECDSA) to the list of known hosts.

[email protected]’s password:

At this point, you’re connected to the simulated node. Run uname -a as an example. You should see the following output, indicating that you’re connected to a RISC-V system:

# uname -a

Linux buildroot 4.12.0-rc2 #1 Fri Aug 4 03:44:55 UTC 2017 riscv64 GNU/Linux

Now you can run programs on the simulated node, as you would with a real machine. For an example workload (running YCSB against Memcached on the simulated node) or to run a larger 8-node simulation, see the full FireSim Demo v1.0 on Amazon EC2 F1 demo instructions.

Finally, when you are finished, you can shut down the simulated node by running the following command from within the simulated node:

# poweroff

You can confirm that the simulation has ended by running screen -ls, which should now report that there are no detached screens.

Future plans

At Berkeley, we’re planning to keep improving the FireSim platform to enable our own research in future data center architectures, like FireBox. The FireSim platform will eventually support more sophisticated processors, custom accelerators (such as Hwacha), network models, and peripherals, in addition to scaling to larger numbers of FPGAs. In the future, we’ll open source the entire platform, including Midas, the tool used to transform RTL into FPGA simulators, allowing users to modify any part of the hardware/software stack. Follow @firesimproject on Twitter to stay tuned to future FireSim updates.

Acknowledgements

FireSim is the joint work of many students and faculty at Berkeley: Sagar Karandikar, Donggyu Kim, Howard Mao, David Biancolin, Jack Koenig, Jonathan Bachrach, and Krste Asanović. This work is partially funded by AWS through the RISE Lab, by the Intel Science and Technology Center for Agile HW Design, and by ASPIRE Lab sponsors and affiliates Intel, Google, HPE, Huawei, NVIDIA, and SK hynix.

Now Available – Amazon Aurora with PostgreSQL Compatibility

2017-10-25 Jeff Barr

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-amazon-aurora-with-postgresql-compatibility/

Late last year I told you about our plans to add PostgreSQL compatibility to Amazon Aurora. We launched the private beta shortly after that announcement, and followed it up earlier this year with an open preview. We’ve received lots of great feedback during the beta and the preview and have done our best to make sure that the product meets your needs and exceeds your expectations!

Now Generally Available
I am happy to report that Amazon Aurora with PostgreSQL Compatibility is now generally available and that you can use it today in four AWS Regions, with more to follow. It is compatible with PostgreSQL 9.6.3 and scales automatically to support up to 64 TB of storage, with 6-way replication behind the scenes to improve performance and availability.

Just like Amazon Aurora with MySQL compatibility, this edition is fully managed and is very easy to set up and to use. On the performance side, you can expect up to 3x the throughput that you’d get if you ran PostgreSQL on your own (you can read Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases to learn more about how we did this).

You can launch a PostgreSQL-compatible Amazon Aurora instance from the RDS Console by selecting Amazon Aurora as the engine and PostgreSQL-compatible as the edition, and clicking on Next:

Then choose your instance class, single or Multi-AZ deployment (good for dev/test and production, respectively), set the instance name, and the administrator credentials, and click on Next:

You can choose between six instance classes (2 to 64 vCPUs and 15.25 to 488 GiB of memory):

The db.r4 instance class is new addition to Aurora and to RDS, and gives you an additional size at the top-end. The db.r4.16xlarge will give you additional write performance, and may allow you to use a single Aurora database instead of two or more sharded databases.

You can also set many advanced options on the next page, starting with network options such as the VPC and public accessibility:

You can set the cluster name and other database options. Encryption is easy to use and enabled by default; you can use the built-in default master key or choose one of your own:

You can also set failover behavior, the retention period for snapshot backups, and choose to enable collection of detailed (OS-level) metrics via Enhanced Monitoring:

After you have set it up to your liking, click on Launch DB Instance to proceed!

The new instances (primary and secondary since I specified Multi-AZ) are up and running within minutes:

Each PostgreSQL-compatible instance publishes 44 metrics to CloudWatch automatically:

With enhanced monitoring enabled, each instance collects additional per-instance and per-process metrics. It can be enabled when the instance is launched, or afterward, via Modify Instance. Here are some of the metrics collected when enhanced monitoring is enabled:

Clicking on Manage Graphs lets you choose which metrics are shown:

Per-process metrics are also available:

You can scale your read capacity by creating up to 15 Aurora replicas:

The cluster provides a single reader endpoint that you can access in order to load-balance requests across the replicas:

Performance Insights
As I noted earlier, Performance Insights is turned on automatically. This Amazon Aurora feature is wired directly into the database engine and allows you to look deep inside of each query, seeing the database resources that it uses and how they contribute to the overall response time. Here’s the initial view:

I can slice the view by SQL query in order to see how many concurrent copies of each query are running:

There are more views and options than I can fit in this post; to learn more take a look at Using Performance Insights.

Migrating to Amazon Aurora with PostgreSQL Compatibility
AWS Database Migration Service and the Schema Conversion Tool are ready to help you to move data stored in commercial and open-source databases to Amazon Aurora. The Schema Conversion Tool will perform a quick assessment of your database schemas and your code in order to help you to choose between MySQL and PostgreSQL. Our new, limited-time, Free DMS program allows you to use DMS and SCT to migrate to Aurora at no cost, with access to several types of DMS Instances for up to 6 months.

If you are already using PostgreSQL, you will be happy to hear that we support a long list of extensions including PostGIS and dblink.

Available Now
You can use Amazon Aurora with PostgreSQL Compatibility today in the US East (Northern Virginia), EU (Ireland), US West (Oregon), and US East (Ohio) Regions, with others to follow as soon as possible.

— Jeff;

BrailleBox: Android Things Braille news display

2017-10-12 Janina Ander

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/braillebox-android-things/

Joe Birch has built a simple device that converts online news stories to Braille, inspired by his family’s predisposition to loss of eyesight. He has based his BrailleBox on Android Things, News API, and a Raspberry Pi 3.

The background

Braille is a symbol system for people with visual impairment which represents letters and numbers as raised points. Commercial devices that dynamically produce Braille are very expensive, so Joe decided to build a low-cost alternative that is simple to recreate.

Image by DIPF CC BY-SA 3.0

News API is a tool for fetching JSON metadata from over 70 online news sources. You can use it to integrate headlines or articles into websites and text-based applications.

The BrailleBox

To create the six nubs necessary to form Braille symbols, Joe topped solenoids with wooden balls. He then wired them up to GPIO pins of the Pi 3 via a breadboard.

One of the solenoids of Joe Birch's BrailleBox

One of the solenoids Joe built into the BrailleBox

Next, he took control of the solenoids using Android Things. He set up the BrailleBox software to start running on boot, and added a push button. When he presses the button, the program fetches a news story using News API, and the solenoids start moving.

BrailleBox Demo
Uploaded by Joe Birch on 2017-06-20.

Since Joe is an Android Engineer, looking through his write-up and code for BrailleBox might be useful for anyone interested in Android Things.

If you like this project, make sure you keep an eye on Joe’s Twitter, since he has plans to update the BrailleBox design. His next step is to move on from the prototyping stage and house all the hardware inside the box. Moreover, he is thinking about adding a potentiometer so that users can choose their preferred reading speed.

Accessibility

If you want to find our community’s conversation about accessibility and assistive technology, head to the forums. And if you’re working to make computing more accessible, or if you’ve built an assistive project, let us know in the comments or on social media, so that we can boost the signal!

The post BrailleBox: Android Things Braille news display appeared first on Raspberry Pi.

AWS Hot Startups – September 2017

2017-09-29 Tina Barr

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/aws-hot-startups-september-2017/

As consumers continue to demand faster, simpler, and more on-the-go services, FinTech companies are responding with ever more innovative solutions to fit everyone’s needs and to improve customer experience. This month, we are excited to feature the following startups—all of whom are disrupting traditional financial services in unique ways:

Acorns – allowing customers to invest spare change automatically.
Bondlinc – improving the bond trading experience for clients, financial institutions, and private banks.
Lenda – reimagining homeownership with a secure and streamlined online service.

Acorns (Irvine, CA)

Driven by the belief that anyone can grow wealth, Acorns is relentlessly pursuing ways to help make that happen. Currently the fastest-growing micro-investing app in the U.S., Acorns takes mere minutes to get started and is currently helping over 2.2 million people grow their wealth. And unlike other FinTech apps, Acorns is focused on helping America’s middle class – namely the 182 million citizens who make less than $100,000 per year – and looking after their financial best interests.

Acorns is able to help their customers effortlessly invest their money, little by little, by offering ETF portfolios put together by Dr. Harry Markowitz, a Nobel Laureate in economic sciences. They also offer a range of services, including “Round-Ups,” whereby customers can automatically invest spare change from every day purchases, and “Recurring Investments,” through which customers can set up automatic transfers of just $5 per week into their portfolio. Additionally, Found Money, Acorns’ earning platform, can help anyone spend smarter as the company connects customers to brands like Lyft, Airbnb, and Skillshare, who then automatically invest in customers’ Acorns account.

The Acorns platform runs entirely on AWS, allowing them to deliver a secure and scalable cloud-based experience. By utilizing AWS, Acorns is able to offer an exceptional customer experience and fulfill its core mission. Acorns uses Terraform to manage services such as Amazon EC2 Container Service, Amazon CloudFront, and Amazon S3. They also use Amazon RDS and Amazon Redshift for data storage, and Amazon Glacier to manage document retention.

Acorns is hiring! Be sure to check out their careers page if you are interested.

Bondlinc (Singapore)

Eng Keong, Founder and CEO of Bondlinc, has long wanted to standardize, improve, and automate the traditional workflows that revolve around bond trading. As a former trader at BNP Paribas and Jefferies & Company, E.K. – as Keong is known – had personally seen how manual processes led to information bottlenecks in over-the-counter practices. This drove him, along with future Bondlinc CTO Vincent Caldeira, to start a new service that maximizes efficiency, information distribution, and accessibility for both clients and bankers in the bond market.

Currently, bond trading requires banks to spend a significant amount of resources retrieving data from expensive and restricted institutional sources, performing suitability checks, and attaching required documentation before presenting all relevant information to clients – usually by email. Bankers are often overwhelmed by these time-consuming tasks, which means clients don’t always get proper access to time-sensitive bond information and pricing. Bondlinc bridges this gap between banks and clients by providing a variety of solutions, including easy access to basic bond information and analytics, updates of new issues and relevant news, consolidated management of your portfolio, and a chat function between banker and client. By making the bond market much more accessible to clients, Bondlinc is taking private banking to the next level, while improving efficiency of the banks as well.

As a startup running on AWS since inception, Bondlinc has built and operated its SaaS product by leveraging Amazon EC2, Amazon S3, Elastic Load Balancing, and Amazon RDS across multiple Availability Zones to provide its customers (namely, financial institutions) a highly available and seamlessly scalable product distribution platform. Bondlinc also makes extensive use of Amazon CloudWatch, AWS CloudTrail, and Amazon SNS to meet the stringent operational monitoring, auditing, compliance, and governance requirements of its customers. Bondlinc is currently experimenting with Amazon Lex to build a conversational interface into its mobile application via a chat-bot that provides trading assistance services.

To see how Bondlinc works, request a demo at Bondlinc.com.

Lenda (San Francisco, CA)

Lenda is a digital mortgage company founded by seasoned FinTech entrepreneur Jason van den Brand. Jason wanted to create a smarter, simpler, and more streamlined system for people to either get a mortgage or refinance their homes. With Lenda, customers can find out if they are pre-approved for loans, and receive accurate, real-time mortgage rate quotes from industry-experienced home loan advisors. Lenda’s advisors support customers through the loan process by providing financial advice and guidance for a seamless experience.

Lenda’s innovative platform allows borrowers to complete their home loans online from start to finish. Through a savvy combination of being a direct lender with proprietary technology, Lenda has simplified the mortgage application process to save customers time and money. With an interactive dashboard, customers know exactly where they are in the mortgage process and can manage all of their documents in one place. The company recently received its Series A funding of $5.25 million, and van den Brand shared that most of the capital investment will be used to improve Lenda’s technology and fulfill the company’s mission, which is to reimagine homeownership, starting with home loans.

AWS allows Lenda to scale its business while providing a secure, easy-to-use system for a faster home loan approval process. Currently, Lenda uses Amazon S3, Amazon EC2, Amazon CloudFront, Amazon Redshift, and Amazon WorkSpaces.

Visit Lenda.com to find out more.

—

Thanks for reading and see you in October for another round of hot startups!

-Tina

Block The Pirate Bay Within 10 Days, Dutch Court Tells ISPs

2017-09-22 Andy

Post Syndicated from Andy original https://torrentfreak.com/block-the-pirate-bay-within-10-days-dutch-court-tells-isps-170922/

Three years ago in 2014, The Court of The Hague handed down its decision in a long-running case which had previously forced two Dutch ISPs, Ziggo and XS4ALL, to block The Pirate Bay.

Ruling against local anti-piracy outfit BREIN, which brought the case, the Court decided that a blockade would be ineffective and also restrict the ISPs’ entrepreneurial freedoms.

The Pirate Bay was unblocked while BREIN took its case to the Supreme Court, which in turn referred the matter to the EU Court of Justice for clarification. This June, the ECJ ruled that as a platform effectively communicating copyright works to the public, The Pirate Bay can indeed be blocked.

The ruling meant there were no major obstacles preventing the Dutch Supreme Court from ordering a future ISP blockade. Clearly, however, BREIN wanted a blocking decision more quickly. A decision handed down today means the anti-piracy group will achieve that in just a few days’ time.

The Hague Court of Appeal today ruled (Dutch) that the 2014 decision, which lifted the blockade against The Pirate Bay, is now largely obsolete.

“According to the Court of Appeal, the Hague Court did not give sufficient weight to the interests of the beneficiaries represented by BREIN,” BREIN said in a statement.

“The Court also wrongly looked at whether torrent traffic had been reduced by the blockade. It should have also considered whether visits to the website of The Pirate Bay itself decreased with a blockade, which speaks for itself.”

As a result, an IP address and DNS blockade of The Pirate Bay, similar to those already in place in the UK and other EU countries, will soon be put in place. BREIN says that four IP addresses will be affected along with hundreds of domain names through which the torrent platform can be reached.

The ISPs have been given just 10 days to put the blocks in place and if they fail there are fines of 2,000 euros per day, up to a maximum of one million euros.

“It is nice that obviously harmful and illegal sites like The Pirate Bay will be blocked again in the Netherlands,” says BREIN chief Tim Kuik.

“A very bad time for our culture, which was free to access via these sites, is now happily behind us.”

Today’s interim decision by the Court of Appeal will stand until the Supreme Court hands down its decision in the main case between BREIN and Ziggo / XS4ALL.

Looking forward, it seems extremely unlikely that the Supreme Court will hand down a conflicting decision, so we’re probably already looking at the beginning of the end for direct accessibility of The Pirate Bay in the Netherlands.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

The Things Pirates Do To Hinder Anti-Piracy Investigations

2017-09-09 Andy

Post Syndicated from Andy original https://torrentfreak.com/the-things-pirates-do-to-hinder-anti-piracy-outfits-170909/

Dedicated Internet pirates dealing in fresh content or operating at any significant scale can be pretty sure that rightsholders and their anti-piracy colleagues are interested in their activities at some level.

With this in mind, most pirates these days are aware of things they can do to enhance their security, with products like VPNs often get discussed on the consumer side.

This week, in a report detailing the challenges social media poses to intellectual property rights, UK anti-piracy outfit Federation Against Copyright Theft published a list of techniques deployed by pirates that hinder their investigations.

Fake/hidden website registration details

“Website registration details are often fake or hidden, which provides no further links to the person controlling the domain and its illegal activities,” the group reveals.

Protected WHOIS records are nothing new and can sometimes be uncloaked by a determined adversary via court procedures. However, in the early stages of an investigation, open records provide leads that can be extremely useful in building an early picture about who might be involved in the operation of a website.

Having them hidden is a definite plus for pirate site operators, especially when the underlying details are also fake, which is particularly common practice. And, with companies like Peter Sunde’s Njalla entering the market, hiding registrations is easier than ever.

Overseas servers

“Investigating servers located offshore cause some specific problems for FACT’s law-enforcement partners. In order to complete a full investigation into an offshore server, a law-enforcement agency must liaise with its counterpart in the country where the server is located. The difficulties of obtaining evidence from other countries are well known,” FACT notes.

While FACT no doubt corresponds with entities overseas, the anti-piracy outfit has a history of targeting UK citizens who are reportedly infringing copyright. It regularly involves UK police in its investigations (FACT itself employs former police officers) but jurisdiction is necessarily limited to the UK.

It is possible to get overseas law enforcement entities involved to seize a server, for example, but they have to be convinced of the need to do so by the police, which isn’t easy and is usually reserved for more serious cases. The bottom line is that by placing a server a long way away from a pirate’s home territory, things can be made much more difficult for local investigators.

Torrent websites and DMCA compliance

“Some torrent website operators who maintain a high DMCA compliance rate will often use this to try to appease the law, while continuing to provide infringing links,” FACT says.

This is an interesting one. Under law in both the United States and Europe, service providers are required to remove infringing content from their systems when they are notified of its existence by a rightsholder or its agent. Not doing so can render them liable, if the content is indeed infringing.

What FACT appears to be saying is that sites that comply with the law, by removing infringing content when asked to, become more difficult targets for legal action. It sounds very obvious but the underlying suggestion is that compliance on the surface is used as a protective mechanism. No example sites are mentioned but the strategy has clearly hindered FACT.

Current legislation too vague to remove infringing live sports streams

“Current legislation is insufficient to effectively tackle the issue of websites illegally offering coverage of live sports events. Section 512 (c) of the Digital Millennium Copyright Act (DMCA) states that: upon notification of claimed infringement, the service provider should ‘respond expeditiously’ to remove or disable access to the copyright-infringing material. Most live sports events are under two hours long, so such non-specific timeframes for required action are inadequate,” FACT complains.

Since government reports like these can take a long time to prepare, it appears that FACT and its partners may have already found a solution to this particular problem. Major FACT client the Premier League now has a High Court injunction in place which allows it to block infringing streams on a real-time basis. It doesn’t remove the content at its source, but it still renders it largely inaccessible in the UK.

Nevertheless, FACT calls for takedowns to be actioned more swiftly, noting that “the law needs to reflect this narrow timeframe with a specified required response period for websites offering such live feeds.”

Camming content directly from cinema screen to the cloud

“Recent advancements in technology have made this a viable option to ‘cammers’ to avoid detection. Attempts to curtail and delete illicitly recorded film footage may become increasingly difficult with the emergence of streaming apps that automatically upload recorded video to cloud services,” FACT reports.

Over the years, FACT has been involved in numerous operations to hinder those who record movies with cameras in theaters and then upload them to the Internet. Once the perpetrator has exited the theater, FACT has effectively lost the battle, but the possibility that a live upload can now take place is certainly an interesting proposition.

“While enforcing officers may delete the footage held on the device, the footage has potentially already been stored remotely on a cloud system,” FACT warns.

Equally, this could also prove a problem for those seeking to secure evidence. With a cloud upload, the person doing the recording could safely delete the footage from the local device. That could be an obstacle to proving that an offense had even been committed when a suspect is confronted in situ.

Virtual currencies

“There is great potential in virtual currencies for money launderers and illicit traders. Government and law enforcement have raised concerns on how virtual currencies can be sent anonymously, leaving little or no trail for regulators or law-enforcement agencies,” FACT writes.

For many years, pirates of all kinds have relied on systems like PayPal, Mastercard, and Visa, to shift money around. However, these payment systems are now more difficult to deploy on pirate services and are more easily traced, even when operators manage to squeeze them through the gaps.

The same cannot be said of bitcoin and similar currencies that are gaining in popularity all the time. They are harder to use, of course, but there’s little doubt accessibility issues will be innovated out of the equation at some point. Once that happens, these currencies will be a force to be reckoned with.

The UK government’s Share and Share Alike report, which examines the challenges social media poses to intellectual property rights, can be downloaded here (pdf)

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Internet Archive Blocked in 2,650 Site Anti-Piracy Sweep

2017-08-10 Andy

Post Syndicated from Andy original https://torrentfreak.com/internet-archive-blocked-in-2650-site-anti-piracy-sweep-170810/

Reports of sites becoming mysteriously inaccessible in India have been a regular occurance over the past several years. In many cases, sites simply stop functioning, leaving users wondering whether sites are actually down or whether there’s a technical issue.

Due to their increasing prevalence, fingers are often pointed at so-called ‘John Doe’ orders, which are handed down by the court to prevent Internet piracy. Often sweeping in nature (and in some cases pre-emptive rather than preventative), these injunctions have been known to block access to both file-sharing platforms and innocent bystanders.

Earlier this week (and again for no apparent reason), the world renowned Internet Archive was rendered inaccessible to millions of users in India. The platform, which is considered by many to be one of the Internet’s most valued resources, hosts more than 15 petabytes of data, a figure which grows on a daily basis. Yet despite numerous requests for information, none was forthcoming from authorities.

The ‘blocked’ message seen by users accessing Archive.org

Quoted by local news outlet Medianama, Chris Butler, Office Manager at the Internet Archive, said that their attempts to contact the Indian Department of Telecom (DoT) and the Ministry of Electronics and Information Technology (Meity) had proven fruitless.

Noting that site had previously been blocked in India, Butler said they were no clearer on the reasons why the same kind of action had seemingly been taken this week.

“We have no information about why a block would have been implemented,” he said. “Obviously, we are disappointed and concerned by this situation and are very eager to understand why it’s happening and see full access restored to archive.org.”

Now, however, the mystery has been solved. The BBC says a local government agency provided a copy of a court order obtained by two Bollywood production companies who are attempting to slow down piracy of their films in India.

Issued by a local judge, the sweeping order compels local ISPs to block access to 2,650 mainly file-sharing websites, including The Pirate Bay, RARBG, the revived KickassTorrents, and hundreds of other ‘usual suspects’. However, it also includes the URL for the Internet Archive, hence the problems with accessibility this week.

The injunction, which appears to be another John Doe order as previously suspected, was granted by the High Court of the Judicature at Madras on August 2, 2017. Two film productions companies – Prakash Jah Productions and Red Chillies Entertainment – obtained the order to protect their films Lipstick Under My Burkha and Jab Harry Met Sejal.

While India-based visitors to blocked resources are often greeted with a message saying that domains have been blocked at the orders of the Department of Telecommunications, these pages never give a reason why.

This always leads to confusion, with news outlets having to pressure local government agencies to discover the reason behind the blockades. In the interests of transparency, providing a link to a copy of a relevant court order would probably benefit all involved.

A few hours ago, the Internet Archive published a statement questioning the process undertaken before the court order was handed down.

“Is the Court aware of and did it consider the fact that the Internet Archive has a well-established and standard procedure for rights holders to submit take down requests and processes them expeditiously?” the platform said.

“We find several instances of take down requests submitted for one of the plaintiffs, Red Chillies Entertainments, throughout the past year, each of which were processed and responded to promptly.

“After a preliminary review, we find no instance of our having been contacted by anyone at all about these films. Is there a specific claim that someone posted these films to archive.org? If so, we’d be eager to address it directly with the claimant.”

But while the Internet Archive appears to be the highest profile collateral damage following the ISP blocks, it isn’t the only victim. Now that the court orders have become available (1,2), it’s clear that other non-pirate entities have also been affected including news site WN.com, website hosting service Weebly, and French ISP Free.fr.

Also, in a sign that sites aren’t being checked to see if they host the movies in question, one of the orders demands that former torrent index BitSnoop is blocked. The site shut down earlier this year. The same is true for Shaanig.org.

This is not the first time that the Internet Archive has been blocked in India. In 2014/2015, Archive.org was rendered inaccessible after it was accused of hosting extremist material. In common with Google, the site copies and stores huge amounts of data, much of it in automated processes. This can leave it exposed to these kinds of accusations.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Encrypted Media Extensions a W3C Recommendation

2017-07-11 ris

Post Syndicated from ris original https://lwn.net/Articles/727499/rss

Encrypted Media Extensions (EME) have been under review by the W3C Advisory
Committee since last March. This report
from the committee addresses comments and objections to EME.
“After consideration of the issues, the Director reached a decision
that the EME specification should move to W3C Recommendation. The Encrypted
Media Extensions specification remains a better alternative for users than
other platforms, including for reasons of security, privacy, and
accessibility, by taking advantage of the Web platform. While additional
work in some areas may be beneficial for the future of the Web Platform, it
remains appropriate for the W3C to make the EME specification a W3C
Recommendation. Formal publication of the W3C Recommendation will happen at
a later date. We encourage W3C Members and the community to work in both
technical and policy areas to find better solutions in this space.”

The Free Software Foundation’s Defective by Design campaign opposes
EME arguing that it infringes on Web users’ control of their own
computers, and weakens their security and privacy. “Opponents’ last opportunity to stop EME is an appeal by the Advisory Committee of the World Wide Web Consortium (W3C), the body which Tim Berners-Lee heads. Requiring 5% of the Committee’s 475 members (corporate, nonprofit, and educational institutions) to sign on within a two-week period, the appeal would then trigger a vote from the whole Committee to make a final decision to ratify or reject EME.”

Noise

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