Tag Archives: analysis

Flight Sim Company Embeds Malware to Steal Pirates’ Passwords

Post Syndicated from Andy original https://torrentfreak.com/flight-sim-company-embeds-malware-to-steal-pirates-passwords-180219/

Anti-piracy systems and DRM come in all shapes and sizes, none of them particularly popular, but one deployed by flight sim company FlightSimLabs is likely to go down in history as one of the most outrageous.

It all started yesterday on Reddit when Flight Sim user ‘crankyrecursion’ reported a little extra something in his download of FlightSimLabs’ A320X module.

“Using file ‘FSLabs_A320X_P3D_v2.0.1.231.exe’ there seems to be a file called ‘test.exe’ included,” crankyrecursion wrote.

“This .exe file is from http://securityxploded.com and is touted as a ‘Chrome Password Dump’ tool, which seems to work – particularly as the installer would typically run with Administrative rights (UAC prompts) on Windows Vista and above. Can anyone shed light on why this tool is included in a supposedly trusted installer?”

The existence of a Chrome password dumping tool is certainly cause for alarm, especially if the software had been obtained from a less-than-official source, such as a torrent or similar site, given the potential for third-party pollution.

However, with the possibility of a nefarious third-party dumping something nasty in a pirate release still lurking on the horizon, things took an unexpected turn. FlightSimLabs chief Lefteris Kalamaras made a statement basically admitting that his company was behind the malware installation.

“We were made aware there is a Reddit thread started tonight regarding our latest installer and how a tool is included in it, that indiscriminately dumps Chrome passwords. That is not correct information – in fact, the Reddit thread was posted by a person who is not our customer and has somehow obtained our installer without purchasing,” Kalamaras wrote.

“[T]here are no tools used to reveal any sensitive information of any customer who has legitimately purchased our products. We all realize that you put a lot of trust in our products and this would be contrary to what we believe.

“There is a specific method used against specific serial numbers that have been identified as pirate copies and have been making the rounds on ThePirateBay, RuTracker and other such malicious sites,” he added.

In a nutshell, FlightSimLabs installed a password dumper onto ALL users’ machines, whether they were pirates or not, but then only activated the password-stealing module when it determined that specific ‘pirate’ serial numbers had been used which matched those on FlightSimLabs’ servers.

“Test.exe is part of the DRM and is only targeted against specific pirate copies of copyrighted software obtained illegally. That program is only extracted temporarily and is never under any circumstances used in legitimate copies of the product,” Kalamaras added.

That didn’t impress Luke Gorman, who published an analysis slamming the flight sim company for knowingly installing password-stealing malware on users machines, even those who purchased the title legitimately.

Password stealer in action (credit: Luke Gorman)

Making matters even worse, the FlightSimLabs chief went on to say that information being obtained from pirates’ machines in this manner is likely to be used in court or other legal processes.

“This method has already successfully provided information that we’re going to use in our ongoing legal battles against such criminals,” Kalamaras revealed.

While the use of the extracted passwords and usernames elsewhere will remain to be seen, it appears that FlightSimLabs has had a change of heart. With immediate effect, the company is pointing customers to a new installer that doesn’t include code for stealing their most sensitive data.

“I want to reiterate and reaffirm that we as a company and as flight simmers would never do anything to knowingly violate the trust that you have placed in us by not only buying our products but supporting them and FlightSimLabs,” Kalamaras said in an update.

“While the majority of our customers understand that the fight against piracy is a difficult and ongoing battle that sometimes requires drastic measures, we realize that a few of you were uncomfortable with this particular method which might be considered to be a bit heavy handed on our part. It is for this reason we have uploaded an updated installer that does not include the DRM check file in question.”

To be continued………

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

Community Profile: Estefannie Explains It All

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-estefannie/

This column is from The MagPi issue 59. You can download a PDF of the full issue for free, or subscribe to receive the print edition through your letterbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve our charitable goals.

“Hey, world!” Estefannie exclaims, a wide grin across her face as the camera begins to roll for another YouTube tutorial video. With a growing number of followers and wonderful support from her fans, Estefannie is building a solid reputation as an online maker, creating unique, fun content accessible to all.

A woman sitting at a desk with a laptop and papers — Estefannie Explains it All Raspberry Pi

It’s as if she was born into performing and making for an audience, but this fun, enjoyable journey to social media stardom came not from a desire to be in front of the camera, but rather as a unique approach to her own learning. While studying, Estefannie decided the best way to confirm her knowledge of a subject was to create an educational video explaining it. If she could teach a topic successfully, she knew she’d retained the information. And so her YouTube channel, Estefannie Explains It All, came into being.

Note taking — Estefannie Explains it All

Her first videos featured pages of notes with voice-over explanations of data structure and algorithm analysis. Then she moved in front of the camera, and expanded her skills in the process.

But YouTube isn’t her only outlet. With nearly 50000 followers, Estefannie’s Instagram game is strong, adding to an increasing number of female coders taking to the platform. Across her Instagram grid, you’ll find insights into her daily routine, from programming on location for work to behind-the-scenes troubleshooting as she begins to create another tutorial video. It’s hard work, with content creation for both Instagram and YouTube forever on her mind as she continues to work and progress successfully as a software engineer.

A woman showing off a game on a tablet — Estefannie Explains it All Raspberry Pi

As a thank you to her Instagram fans for helping her reach 10000 followers, Estefannie created a free game for Android and iOS called Gravitris — imagine Tetris with balance issues!

Estefannie was born and raised in Mexico, with ambitions to become a graphic designer and animator. However, a documentary on coding at Pixar, and the beauty of Merida’s hair in Brave, opened her mind to the opportunities of software engineering in animation. She altered her career path, moved to the United States, and switched to a Computer Science course.

A woman wearing safety goggles hugging a keyboard Estefannie Explains it All Raspberry Pi

With a constant desire to make and to learn, Estefannie combines her software engineering profession with her hobby to create fun, exciting content for YouTube.

While studying, Estefannie started a Computer Science Girls Club at the University of Houston, Texas, and she found herself eager to put more time and effort into the movement to increase the percentage of women in the industry. The club was a success, and still is to this day. While Estefannie has handed over the reins, she’s still very involved in the cause.

Through her YouTube videos, Estefannie continues the theme of inclusion, with every project offering a warm sense of approachability for all, regardless of age, gender, or skill. From exploring Scratch and Makey Makey with her young niece and nephew to creating her own Disney ‘Made with Magic’ backpack for a trip to Disney World, Florida, Estefannie’s videos are essentially a documentary of her own learning process, produced so viewers can learn with her — and learn from her mistakes — to create their own tech wonders.

Using the Raspberry Pi, she’s been able to broaden her skills and, in turn, her projects, creating a home-automated gingerbread house at Christmas, building a GPS-controlled GoPro for her trip to London, and making everyone’s life better with an Internet Button–controlled French press.

Estefannie Explains it All Raspberry Pi Home Automated Gingerbread House

Estefannie’s automated gingerbread house project was a labour of love, with electronics, wires, and candy strewn across both her living room and kitchen for weeks before completion. While she already was a skilled programmer, the world of physical digital making was still fairly new for Estefannie. Having ditched her hot glue gun in favour of a soldering iron in a previous video, she continued to experiment and try out new, interesting techniques that are now second nature to many members of the maker community. With the gingerbread house, Estefannie was able to research and apply techniques such as light controls, servos, and app making, although the latter was already firmly within her skill set. The result? A fun video of ups and downs that resulted in a wonderful, festive treat. She even gave her holiday home its own solar panel!

A DAY AT RASPBERRY PI TOWERS!! LINK IN BIO ⚡🎥 @raspberrypifoundation

1,910 Likes, 43 Comments – Estefannie Explains It All (@estefanniegg) on Instagram: “A DAY AT RASPBERRY PI TOWERS!! LINK IN BIO ⚡🎥 @raspberrypifoundation”

And that’s just the beginning of her adventures with Pi…but we won’t spoil her future plans by telling you what’s coming next. Sorry! However, since this article was written last year, Estefannie has released a few more Pi-based project videos, plus some awesome interviews and live-streams with other members of the maker community such as Simone Giertz. She even made us an awesome video for our Raspberry Pi YouTube channel! So be sure to check out her latest releases.

Best day yet!! I got to hangout, play Jenga with a huge arm robot, and have afternoon tea with @simonegiertz and robots!! 🤖👯 #shittyrobotnation

2,264 Likes, 56 Comments – Estefannie Explains It All (@estefanniegg) on Instagram: “Best day yet!! I got to hangout, play Jenga with a huge arm robot, and have afternoon tea with…”

While many wonderful maker videos show off a project without much explanation, or expect a certain level of skill from viewers hoping to recreate the project, Estefannie’s videos exist almost within their own category. We can’t wait to see where Estefannie Explains It All goes next!

The post Community Profile: Estefannie Explains It All appeared first on Raspberry Pi.

New National Academies Report on Crypto Policy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/02/new_national_ac.html

The National Academies has just published “Decrypting the Encryption Debate: A Framework for Decision Makers.” It looks really good, although I have not read it yet.

Not much news or analysis yet. Please post any links you find in the comments, and I will summarize them here.

Early Challenges: Making Critical Hires

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/early-challenges-making-critical-hires/

row of potential employee hires sitting waiting for an interview

In 2009, Google disclosed that they had 400 recruiters on staff working to hire nearly 10,000 people. Someday, that might be your challenge, but most companies in their early days are looking to hire a handful of people — the right people — each year. Assuming you are closer to startup stage than Google stage, let’s look at who you need to hire, when to hire them, where to find them (and how to help them find you), and how to get them to join your company.

Who Should Be Your First Hires

In later stage companies, the roles in the company have been well fleshed out, don’t change often, and each role can be segmented to focus on a specific area. A large company may have an entire department focused on just cubicle layout; at a smaller company you may not have a single person whose actual job encompasses all of facilities. At Backblaze, our CTO has a passion and knack for facilities and mostly led that charge. Also, the needs of a smaller company are quick to change. One of our first hires was a QA person, Sean, who ended up being 100% focused on data center infrastructure. In the early stage, things can shift quite a bit and you need people that are broadly capable, flexible, and most of all willing to pitch in where needed.

That said, there are times you may need an expert. At a previous company we hired Jon, a PhD in Bayesian statistics, because we needed algorithmic analysis for spam fighting. However, even that person was not only able and willing to do the math, but also code, and to not only focus on Bayesian statistics but explore a plethora of spam fighting options.

When To Hire

If you’ve raised a lot of cash and are willing to burn it with mistakes, you can guess at all the roles you might need and start hiring for them. No judgement: that’s a reasonable strategy if you’re cash-rich and time-poor.

If your cash is limited, try to see what you and your team are already doing and then hire people to take those jobs. It may sound counterintuitive, but if you’re already doing it presumably it needs to be done, you have a good sense of the type of skills required to do it, and you can bring someone on-board and get them up to speed quickly. That then frees you up to focus on tasks that can’t be done by someone else. At Backblaze, I ran marketing internally for years before hiring a VP of Marketing, making it easier for me to know what we needed. Once I was hiring, my primary goal was to find someone I could trust to take that role completely off of me so I could focus solely on my CEO duties

Where To Find the Right People

Finding great people is always difficult, particularly when the skillsets you’re looking for are highly in-demand by larger companies with lots of cash and cachet. You, however, have one massive advantage: you need to hire 5 people, not 5,000.

People You Worked With

The absolutely best people to hire are ones you’ve worked with before that you already know are good in a work situation. Consider your last job, the one before, and the one before that. A significant number of the people we recruited at Backblaze came from our previous startup MailFrontier. We knew what they could do and how they would fit into the culture, and they knew us and thus could quickly meld into the environment. If you didn’t have a previous job, consider people you went to school with or perhaps individuals with whom you’ve done projects previously.

People You Know

Hiring friends, family, and others can be risky, but should be considered. Sometimes a friend can be a “great buddy,” but is not able to do the job or isn’t a good fit for the organization. Having to let go of someone who is a friend or family member can be rough. Have the conversation up front with them about that possibility, so you have the ability to stay friends if the position doesn’t work out. Having said that, if you get along with someone as a friend, that’s one critical component of succeeding together at work. At Backblaze we’ve hired a number of people successfully that were friends of someone in the organization.

Friends Of People You Know

Your network is likely larger than you imagine. Your employees, investors, advisors, spouses, friends, and other folks all know people who might be a great fit for you. Make sure they know the roles you’re hiring for and ask them if they know anyone that would fit. Search LinkedIn for the titles you’re looking for and see who comes up; if they’re a 2nd degree connection, ask your connection for an introduction.

People You Know About

Sometimes the person you want isn’t someone anyone knows, but you may have read something they wrote, used a product they’ve built, or seen a video of a presentation they gave. Reach out. You may get a great hire: worst case, you’ll let them know they were appreciated, and make them aware of your organization.

Other Places to Find People

There are a million other places to find people, including job sites, community groups, Facebook/Twitter, GitHub, and more. Consider where the people you’re looking for are likely to congregate online and in person.

A Comment on Diversity

Hiring “People You Know” can often result in “Hiring People Like You” with the same workplace experiences, culture, background, and perceptions. Some studies have shown [1, 2, 3, 4] that homogeneous groups deliver faster, while heterogeneous groups are more creative. Also, “Hiring People Like You” often propagates the lack of women and minorities in tech and leadership positions in general. When looking for people you know, keep an eye to not discount people you know who don’t have the same cultural background as you.

Helping People To Find You

Reaching out proactively to people is the most direct way to find someone, but you want potential hires coming to you as well. To do this, they have to a) be aware of you, b) know you have a role they’re interested in, and c) think they would want to work there. Let’s tackle a) and b) first below.

Your Blog

I started writing our blog before we launched the product and talked about anything I found interesting related to our space. For several years now our team has owned the content on the blog and in 2017 over 1.5 million people read it. Each time we have a position open it’s published to the blog. If someone finds reading about backup and storage interesting, perhaps they’d want to dig in deeper from the inside. Many of the people we’ve recruited have mentioned reading the blog as either how they found us or as a factor in why they wanted to work here.
[BTW, this is Gleb’s 200th post on Backblaze’s blog. The first was in 2008. — Editor]

Your Email List

In addition to the emails our blog subscribers receive, we send regular emails to our customers, partners, and prospects. These are largely focused on content we think is directly useful or interesting for them. However, once every few months we include a small mention that we’re hiring, and the positions we’re looking for. Often a small blurb is all you need to capture people’s imaginations whether they might find the jobs interesting or can think of someone that might fit the bill.

Your Social Involvement

Whether it’s Twitter or Facebook, Hacker News or Slashdot, your potential hires are engaging in various communities. Being socially involved helps make people aware of you, reminds them of you when they’re considering a job, and paints a picture of what working with you and your company would be like. Adam was in a Reddit thread where we were discussing our Storage Pods, and that interaction was ultimately part of the reason he left Apple to come to Backblaze.

Convincing People To Join

Once you’ve found someone or they’ve found you, how do you convince them to join? They may be currently employed, have other offers, or have to relocate. Again, while the biggest companies have a number of advantages, you might have more unique advantages than you realize.

Why Should They Join You

Here are a set of items that you may be able to offer which larger organizations might not:

Role: Consider the strengths of the role. Perhaps it will have broader scope? More visibility at the executive level? No micromanagement? Ability to take risks? Option to create their own role?

Compensation: In addition to salary, will their options potentially be worth more since they’re getting in early? Can they trade-off salary for more options? Do they get option refreshes?

Benefits: In addition to healthcare, food, and 401(k) plans, are there unique benefits of your company? One company I knew took the entire team for a one-month working retreat abroad each year.

Location: Most people prefer to work close to home. If you’re located outside of the San Francisco Bay Area, you might be at a disadvantage for not being in the heart of tech. But if you find employees close to you you’ve got a huge advantage. Sometimes it’s micro; even in the Bay Area the difference of 5 miles can save 20 minutes each way every day. We located the Backblaze headquarters in San Mateo, a middle-ground that made it accessible to those coming from San Jose and San Francisco. We also chose a downtown location near a train, restaurants, and cafes: all to make it easier and more pleasant. Also, are you flexible in letting your employees work remotely? Our systems administrator Elliott is about to embark on a long-term cross-country journey working from an RV.

Environment: Open office, cubicle, cafe, work-from-home? Loud/quiet? Social or focused? 24×7 or work-life balance? Different environments appeal to different people.

Team: Who will they be working with? A company with 100,000 people might have 100 brilliant ones you’d want to work with, but ultimately we work with our core team. Who will your prospective hires be working with?

Market: Some people are passionate about gaming, others biotech, still others food. The market you’re targeting will get different people excited.

Product: Have an amazing product people love? Highlight that. If you’re lucky, your potential hire is already a fan.

Mission: Curing cancer, making people happy, and other company missions inspire people to strive to be part of the journey. Our mission is to make storing data astonishingly easy and low-cost. If you care about data, information, knowledge, and progress, our mission helps drive all of them.

Culture: I left this for last, but believe it’s the most important. What is the culture of your company? Finding people who want to work in the culture of your organization is critical. If they like the culture, they’ll fit and continue it. We’ve worked hard to build a culture that’s collaborative, friendly, supportive, and open; one in which people like coming to work. For example, the five founders started with (and still have) the same compensation and equity. That started a culture of “we’re all in this together.” Build a culture that will attract the people you want, and convey what the culture is.

Writing The Job Description

Most job descriptions focus on the all the requirements the candidate must meet. While important to communicate, the job description should first sell the job. Why would the appropriate candidate want the job? Then share some of the requirements you think are critical. Remember that people read not just what you say but how you say it. Try to write in a way that conveys what it is like to actually be at the company. Ahin, our VP of Marketing, said the job description itself was one of the things that attracted him to the company.

Orchestrating Interviews

Much can be said about interviewing well. I’m just going to say this: make sure that everyone who is interviewing knows that their job is not only to evaluate the candidate, but give them a sense of the culture, and sell them on the company. At Backblaze, we often have one person interview core prospects solely for company/culture fit.

Onboarding

Hiring success shouldn’t be defined by finding and hiring the right person, but instead by the right person being successful and happy within the organization. Ensure someone (usually their manager) provides them guidance on what they should be concentrating on doing during their first day, first week, and thereafter. Giving new employees opportunities and guidance so that they can achieve early wins and feel socially integrated into the company does wonders for bringing people on board smoothly

In Closing

Our Director of Production Systems, Chris, said to me the other day that he looks for companies where he can work on “interesting problems with nice people.” I’m hoping you’ll find your own version of that and find this post useful in looking for your early and critical hires.

Of course, I’d be remiss if I didn’t say, if you know of anyone looking for a place with “interesting problems with nice people,” Backblaze is hiring. 😉

The post Early Challenges: Making Critical Hires appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Troubleshooting event publishing issues in Amazon SES

Post Syndicated from Dustin Taylor original https://aws.amazon.com/blogs/ses/troubleshooting-event-publishing-issues-in-amazon-ses/

Over the past year, we’ve released several features that make it easier to track the metrics that are associated with your Amazon SES account. The first of these features, launched in November of last year, was event publishing.

Initially, event publishing let you capture basic metrics related to your email sending and publish them to other AWS services, such as Amazon CloudWatch and Amazon Kinesis Data Firehose. Some examples of these basic metrics include the number of emails that were sent and delivered, as well as the number that bounced or received complaints. A few months ago, we expanded this feature by adding engagement metrics—specifically, information about the number of emails that your customers opened or engaged with by clicking links.

As a former Cloud Support Engineer, I’ve seen Amazon SES customers do some amazing things with event publishing, but I’ve also seen some common issues. In this article, we look at some of these issues, and discuss the steps you can take to resolve them.

Before we begin

This post assumes that your Amazon SES account is already out of the sandbox, that you’ve verified an identity (such as an email address or domain), and that you have the necessary permissions to use Amazon SES and the service that you’ll publish event data to (such as Amazon SNS, CloudWatch, or Kinesis Data Firehose).

We also assume that you’re familiar with the process of creating configuration sets and specifying event destinations for those configuration sets. For more information, see Using Amazon SES Configuration Sets in the Amazon SES Developer Guide.

Amazon SNS event destinations

If you want to receive notifications when events occur—such as when recipients click a link in an email, or when they report an email as spam—you can use Amazon SNS as an event destination.

Occasionally, customers ask us why they’re not receiving notifications when they use an Amazon SNS topic as an event destination. One of the most common reasons for this issue is that they haven’t configured subscriptions for their Amazon SNS topic yet.

A single topic in Amazon SNS can have one or more subscriptions. When you subscribe to a topic, you tell that topic which endpoints (such as email addresses or mobile phone numbers) to contact when it receives a notification. If you haven’t set up any subscriptions, nothing will happen when an email event occurs.

For more information about setting up topics and subscriptions, see Getting Started in the Amazon SNS Developer Guide. For information about publishing Amazon SES events to Amazon SNS topics, see Set Up an Amazon SNS Event Destination for Amazon SES Event Publishing in the Amazon SES Developer Guide.

Kinesis Data Firehose event destinations

If you want to store your Amazon SES event data for the long term, choose Amazon Kinesis Data Firehose as a destination for Amazon SES events. With Kinesis Data Firehose, you can stream data to Amazon S3 or Amazon Redshift for storage and analysis.

The process of setting up Kinesis Data Firehose as an event destination is similar to the process for setting up Amazon SNS: you choose the types of events (such as deliveries, opens, clicks, or bounces) that you want to export, and the name of the Kinesis Data Firehose stream that you want to export to. However, there’s one important difference. When you set up a Kinesis Data Firehose event destination, you must also choose the IAM role that Amazon SES uses to send event data to Kinesis Data Firehose.

When you set up the Kinesis Data Firehose event destination, you can choose to have Amazon SES create the IAM role for you automatically. For many users, this is the best solution—it ensures that the IAM role has the appropriate permissions to move event data from Amazon SES to Kinesis Data Firehose.

Customers occasionally run into issues with the Kinesis Data Firehose event destination when they use an existing IAM role. If you use an existing IAM role, or create a new role for this purpose, make sure that the role includes the firehose:PutRecord and firehose:PutRecordBatch permissions. If the role doesn’t include these permissions, then the Amazon SES event data isn’t published to Kinesis Data Firehose. For more information, see Controlling Access with Amazon Kinesis Data Firehose in the Amazon Kinesis Data Firehose Developer Guide.

CloudWatch event destinations

By publishing your Amazon SES event data to Amazon CloudWatch, you can create dashboards that track your sending statistics in real time, as well as alarms that notify you when your event metrics reach certain thresholds.

The amount that you’re charged for using CloudWatch is based on several factors, including the number of metrics you use. In order to give you more control over the specific metrics you send to CloudWatch—and to help you avoid unexpected charges—you can limit the email sending events that are sent to CloudWatch.

When you choose CloudWatch as an event destination, you must choose a value source. The value source can be one of three options: a message tag, a link tag, or an email header. After you choose a value source, you then specify a name and a value. When you send an email using a configuration set that refers to a CloudWatch event destination, it only sends the metrics for that email to CloudWatch if the email contains the name and value that you specified as the value source. This requirement is commonly overlooked.

For example, assume that you chose Message Tag as the value source, and specified “CategoryId” as the dimension name and “31415” as the dimension value. When you want to send events for an email to CloudWatch, you must specify the name of the configuration set that uses the CloudWatch destination. You must also include a tag in your message. The name of the tag must be “CategoryId” and the value must be “31415”.

For more information about adding tags and email headers to your messages, see Send Email Using Amazon SES Event Publishing in the Amazon SES Developer Guide. For more information about adding tags to links, see Amazon SES Email Sending Metrics FAQs in the Amazon SES Developer Guide.

Troubleshooting event publishing for open and click data

Occasionally, customers ask why they’re not seeing open and click data for their emails. This issue most often occurs when the customer only sends text versions of their emails. Because of the way Amazon SES tracks open and click events, you can only see open and click data for emails that are sent as HTML. For more information about how Amazon SES modifies your emails when you enable open and click tracking, see Amazon SES Email Sending Metrics FAQs in the Amazon SES Developer Guide.

The process that you use to send HTML emails varies based on the email sending method you use. The Code Examples section of the Amazon SES Developer Guide contains examples of several methods of sending email by using the Amazon SES SMTP interface or an AWS SDK. All of the examples in this section include methods for sending HTML (as well as text-only) emails.

If you encounter any issues that weren’t covered in this post, please open a case in the Support Center and we’d be more than happy to assist.

How I built a data warehouse using Amazon Redshift and AWS services in record time

Post Syndicated from Stephen Borg original https://aws.amazon.com/blogs/big-data/how-i-built-a-data-warehouse-using-amazon-redshift-and-aws-services-in-record-time/

This is a customer post by Stephen Borg, the Head of Big Data and BI at Cerberus Technologies.

Cerberus Technologies, in their own words: Cerberus is a company founded in 2017 by a team of visionary iGaming veterans. Our mission is simple – to offer the best tech solutions through a data-driven and a customer-first approach, delivering innovative solutions that go against traditional forms of working and process. This mission is based on the solid foundations of reliability, flexibility and security, and we intend to fundamentally change the way iGaming and other industries interact with technology.

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of Amazon Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

In two of my recent projects, I ran into challenges when scaling our data warehouse using on-premises infrastructure. Data was growing at many tens of gigabytes per day, and query performance was suffering. Scaling required major capital investment for hardware and software licenses, and also significant operational costs for maintenance and technical staff to keep it running and performing well. Unfortunately, I couldn’t get the resources needed to scale the infrastructure with data growth, and these projects were abandoned. Thanks to cloud data warehousing, the bottleneck of infrastructure resources, capital expense, and operational costs have been significantly reduced or have totally gone away. There is no more excuse for allowing obstacles of the past to delay delivering timely insights to decision makers, no matter how much data you have.

With Amazon Redshift and AWS, I delivered a cloud data warehouse to the business very quickly, and with a small team: me. I didn’t have to order hardware or software, and I no longer needed to install, configure, tune, or keep up with patches and version updates. Instead, I easily set up a robust data processing pipeline and we were quickly ingesting and analyzing data. Now, my data warehouse team can be extremely lean, and focus more time on bringing in new data and delivering insights. In this post, I show you the AWS services and the architecture that I used.

Handling data feeds

I have several different data sources that provide everything needed to run the business. The data includes activity from our iGaming platform, social media posts, clickstream data, marketing and campaign performance, and customer support engagements.

To handle the diversity of data feeds, I developed abstract integration applications using Docker that run on Amazon EC2 Container Service (Amazon ECS) and feed data to Amazon Kinesis Data Streams. These data streams can be used for real time analytics. In my system, each record in Kinesis is preprocessed by an AWS Lambda function to cleanse and aggregate information. My system then routes it to be stored where I need on Amazon S3 by Amazon Kinesis Data Firehose. Suppose that you used an on-premises architecture to accomplish the same task. A team of data engineers would be required to maintain and monitor a Kafka cluster, develop applications to stream data, and maintain a Hadoop cluster and the infrastructure underneath it for data storage. With my stream processing architecture, there are no servers to manage, no disk drives to replace, and no service monitoring to write.

Setting up a Kinesis stream can be done with a few clicks, and the same for Kinesis Firehose. Firehose can be configured to automatically consume data from a Kinesis Data Stream, and then write compressed data every N minutes to Amazon S3. When I want to process a Kinesis data stream, it’s very easy to set up a Lambda function to be executed on each message received. I can just set a trigger from the AWS Lambda Management Console, as shown following.

I also monitor the duration of function execution using Amazon CloudWatch and AWS X-Ray.

Regardless of the format I receive the data from our partners, I can send it to Kinesis as JSON data using my own formatters. After Firehose writes this to Amazon S3, I have everything in nearly the same structure I received but compressed, encrypted, and optimized for reading.

This data is automatically crawled by AWS Glue and placed into the AWS Glue Data Catalog. This means that I can immediately query the data directly on S3 using Amazon Athena or through Amazon Redshift Spectrum. Previously, I used Amazon EMR and an Amazon RDS–based metastore in Apache Hive for catalog management. Now I can avoid the complexity of maintaining Hive Metastore catalogs. Glue takes care of high availability and the operations side so that I know that end users can always be productive.

Working with Amazon Athena and Amazon Redshift for analysis

I found Amazon Athena extremely useful out of the box for ad hoc analysis. Our engineers (me) use Athena to understand new datasets that we receive and to understand what transformations will be needed for long-term query efficiency.

For our data analysts and data scientists, we’ve selected Amazon Redshift. Amazon Redshift has proven to be the right tool for us over and over again. It easily processes 20+ million transactions per day, regardless of the footprint of the tables and the type of analytics required by the business. Latency is low and query performance expectations have been more than met. We use Redshift Spectrum for long-term data retention, which enables me to extend the analytic power of Amazon Redshift beyond local data to anything stored in S3, and without requiring me to load any data. Redshift Spectrum gives me the freedom to store data where I want, in the format I want, and have it available for processing when I need it.

To load data directly into Amazon Redshift, I use AWS Data Pipeline to orchestrate data workflows. I create Amazon EMR clusters on an intra-day basis, which I can easily adjust to run more or less frequently as needed throughout the day. EMR clusters are used together with Amazon RDS, Apache Spark 2.0, and S3 storage. The data pipeline application loads ETL configurations from Spring RESTful services hosted on AWS Elastic Beanstalk. The application then loads data from S3 into memory, aggregates and cleans the data, and then writes the final version of the data to Amazon Redshift. This data is then ready to use for analysis. Spark on EMR also helps with recommendations and personalization use cases for various business users, and I find this easy to set up and deliver what users want. Finally, business users use Amazon QuickSight for self-service BI to slice, dice, and visualize the data depending on their requirements.

Each AWS service in this architecture plays its part in saving precious time that’s crucial for delivery and getting different departments in the business on board. I found the services easy to set up and use, and all have proven to be highly reliable for our use as our production environments. When the architecture was in place, scaling out was either completely handled by the service, or a matter of a simple API call, and crucially doesn’t require me to change one line of code. Increasing shards for Kinesis can be done in a minute by editing a stream. Increasing capacity for Lambda functions can be accomplished by editing the megabytes allocated for processing, and concurrency is handled automatically. EMR cluster capacity can easily be increased by changing the master and slave node types in Data Pipeline, or by using Auto Scaling. Lastly, RDS and Amazon Redshift can be easily upgraded without any major tasks to be performed by our team (again, me).

In the end, using AWS services including Kinesis, Lambda, Data Pipeline, and Amazon Redshift allows me to keep my team lean and highly productive. I eliminated the cost and delays of capital infrastructure, as well as the late night and weekend calls for support. I can now give maximum value to the business while keeping operational costs down. My team pushed out an agile and highly responsive data warehouse solution in record time and we can handle changing business requirements rapidly, and quickly adapt to new data and new user requests.


Additional Reading

If you found this post useful, be sure to check out Deploy a Data Warehouse Quickly with Amazon Redshift, Amazon RDS for PostgreSQL and Tableau Server and Top 8 Best Practices for High-Performance ETL Processing Using Amazon Redshift.


About the Author

Stephen Borg is the Head of Big Data and BI at Cerberus Technologies. He has a background in platform software engineering, and first became involved in data warehousing using the typical RDBMS, SQL, ETL, and BI tools. He quickly became passionate about providing insight to help others optimize the business and add personalization to products. He is now the Head of Big Data and BI at Cerberus Technologies.

 

 

 

EU Anti-Piracy Agreement Has Little Effect on Advertising, Research Finds

Post Syndicated from Ernesto original https://torrentfreak.com/eu-anti-piracy-agreement-has-little-effect-on-advertising-research-finds-180204/

In recent years various copyright holder groups have adopted a “follow-the-money” approach in the hope of cutting off funding to so-called pirate sites.

Thus far this has resulted in some notable developments. In the UK, hundreds of advertising agencies began banning pirate sites in 2014 and similar initiatives have popped up elsewhere too.

One of the more prominent plans was orchestrated by the European Commission. In October 2016, this resulted in a voluntary self-regulation agreement signed by leading EU advertising organizations, which promised to reduce ad placement on pirate sites. The question is, how effective is this agreement?

To find out, researchers from European universities in Munich, Copenhagen, and Lisbon, conducted an extensive study. They collected data on the prevalence of ads from various advertisers on hundreds of pirate sites. The data were collected on several occasions, both before and after the agreement.

The findings are published in the article “Follow The Money: Online Piracy and Self-Regulation in the Advertising Industry.” Christian Peukert, one of the authors, informs TF that the latest version of the working paper was published last month and is currently under review at an academic journal.

The results show that the effects of the anti-piracy agreement are fairly minimal. On a whole, there is no significant change in the volume of piracy sites that ad agencies serve. Only when looking at the larger ad-networks in isolation, a downward trend is visible.

“Our results suggests that the presence of advertising services on piracy websites does not change significantly, at least not on average,” the researchers write in their paper.

“Once we allow for heterogeneity in terms of size, we show that more popular advertising services, i.e. those that are overall more diffused on the Internet, reduce their presence on piracy websites significantly more.”

When larger advertising companies are given more weight in the analysis, the average effect equates to a 17% drop in pirate site connections.

That larger companies are more likely to comply with the agreement can be explained by a variety of reasons. They could simply be more aware of the agreement, or they feel more pressure to take appropriate steps in response.

Interestingly, there are also advertising companies that began advertising on pirate sites after the agreement was signed.

“We further provide some evidence that ad services that were not active in the piracy market before the self-regulation agreement increase their presence on piracy websites afterwards,” the researchers write.

This may have been partly triggered by site owners looking for alternatives, or advertising companies looking for new opportunities. However, the effect is not statistically significant, which means that people shouldn’t read into it too much.

Overall, however, the researchers conclude that the voluntary agreement only had a relatively small impact on the EU advertising as a whole, and that there’s room for improvement.

“These results raise concerns about the overall effectiveness of the self-regulation effort with respect to reducing incentives for publishers to supply unlicensed content,” they write.

The EU agreement coincided with a series of similar agreements which, according to this data, had little effect on EU advertisers either over the researched timespan. And by looking at the average pirate site today, it becomes instantly clear that there are still plenty advertisers who are willing to work with these sites.

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

Backblaze Hard Drive Stats for 2017

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/hard-drive-stats-for-2017/

Backbalze Drive Stats 2017 Review

Beginning in April 2013, Backblaze has recorded and saved daily hard drive statistics from the drives in our data centers. Each entry consists of the date, manufacturer, model, serial number, status (operational or failed), and all of the SMART attributes reported by that drive. As of the end of 2017, there are about 88 million entries totaling 23 GB of data. You can download this data from our website if you want to do your own research, but for starters here’s what we found.

Overview

At the end of 2017 we had 93,240 spinning hard drives. Of that number, there were 1,935 boot drives and 91,305 data drives. This post looks at the hard drive statistics of the data drives we monitor. We’ll review the stats for Q4 2017, all of 2017, and the lifetime statistics for all of the drives Backblaze has used in our cloud storage data centers since we started keeping track. Along the way we’ll share observations and insights on the data presented and we look forward to you doing the same in the comments.

Hard Drive Reliability Statistics for Q4 2017

At the end of Q4 2017 Backblaze was monitoring 91,305 hard drives used to store data. For our evaluation we remove from consideration those drives which were used for testing purposes and those drive models for which we did not have at least 45 drives (read why after the chart). This leaves us with 91,243 hard drives. The table below is for the period of Q4 2017.

Hard Drive Annualized Failure Rates for Q4 2017

A few things to remember when viewing this chart:

  • The failure rate listed is for just Q4 2017. If a drive model has a failure rate of 0%, it means there were no drive failures of that model during Q4 2017.
  • There were 62 drives (91,305 minus 91,243) that were not included in the list above because we did not have at least 45 of a given drive model. The most common reason we would have fewer than 45 drives of one model is that we needed to replace a failed drive and we had to purchase a different model as a replacement because the original model was no longer available. We use 45 drives of the same model as the minimum number to qualify for reporting quarterly, yearly, and lifetime drive statistics.
  • Quarterly failure rates can be volatile, especially for models that have a small number of drives and/or a small number of drive days. For example, the Seagate 4 TB drive, model ST4000DM005, has a annualized failure rate of 29.08%, but that is based on only 1,255 drive days and 1 (one) drive failure.
  • AFR stands for Annualized Failure Rate, which is the projected failure rate for a year based on the data from this quarter only.

Bulking Up and Adding On Storage

Looking back over 2017, we not only added new drives, we “bulked up” by swapping out functional and smaller 2, 3, and 4TB drives with larger 8, 10, and 12TB drives. The changes in drive quantity by quarter are shown in the chart below:

Backblaze Drive Population by Drive Size

For 2017 we added 25,746 new drives, and lost 6,442 drives to retirement for a net of 19,304 drives. When you look at storage space, we added 230 petabytes and retired 19 petabytes, netting us an additional 211 petabytes of storage in our data center in 2017.

2017 Hard Drive Failure Stats

Below are the lifetime hard drive failure statistics for the hard drive models that were operational at the end of Q4 2017. As with the quarterly results above, we have removed any non-production drives and any models that had fewer than 45 drives.

Hard Drive Annualized Failure Rates

The chart above gives us the lifetime view of the various drive models in our data center. The Q4 2017 chart at the beginning of the post gives us a snapshot of the most recent quarter of the same models.

Let’s take a look at the same models over time, in our case over the past 3 years (2015 through 2017), by looking at the annual failure rates for each of those years.

Annual Hard Drive Failure Rates by Year

The failure rate for each year is calculated for just that year. In looking at the results the following observations can be made:

  • The failure rates for both of the 6 TB models, Seagate and WDC, have decreased over the years while the number of drives has stayed fairly consistent from year to year.
  • While it looks like the failure rates for the 3 TB WDC drives have also decreased, you’ll notice that we migrated out nearly 1,000 of these WDC drives in 2017. While the remaining 180 WDC 3 TB drives are performing very well, decreasing the data set that dramatically makes trend analysis suspect.
  • The Toshiba 5 TB model and the HGST 8 TB model had zero failures over the last year. That’s impressive, but with only 45 drives in use for each model, not statistically useful.
  • The HGST/Hitachi 4 TB models delivered sub 1.0% failure rates for each of the three years. Amazing.

A Few More Numbers

To save you countless hours of looking, we’ve culled through the data to uncover the following tidbits regarding our ever changing hard drive farm.

  • 116,833 — The number of hard drives for which we have data from April 2013 through the end of December 2017. Currently there are 91,305 drives (data drives) in operation. This means 25,528 drives have either failed or been removed from service due for some other reason — typically migration.
  • 29,844 — The number of hard drives that were installed in 2017. This includes new drives, migrations, and failure replacements.
  • 81.76 — The number of hard drives that were installed each day in 2017. This includes new drives, migrations, and failure replacements.
  • 95,638 — The number of drives installed since we started keeping records in April 2013 through the end of December 2017.
  • 55.41 — The average number of hard drives installed per day from April 2013 to the end of December 2017. The installations can be new drives, migration replacements, or failure replacements.
  • 1,508 — The number of hard drives that were replaced as failed in 2017.
  • 4.13 — The average number of hard drives that have failed each day in 2017.
  • 6,795 — The number of hard drives that have failed from April 2013 until the end of December 2017.
  • 3.94 — The average number of hard drives that have failed each day from April 2013 until the end of December 2017.

Can’t Get Enough Hard Drive Stats?

We’ll be presenting the webinar “Backblaze Hard Drive Stats for 2017” on Thursday February 9, 2017 at 10:00 Pacific time. The webinar will dig deeper into the quarterly, yearly, and lifetime hard drive stats and include the annual and lifetime stats by drive size and manufacturer. You will need to subscribe to the Backblaze BrightTALK channel to view the webinar. Sign up today.

As a reminder, the complete data set used to create the information used in this review is available on our Hard Drive Test Data page. You can download and use this data for free for your own purpose. All we ask are three things: 1) you cite Backblaze as the source if you use the data, 2) you accept that you are solely responsible for how you use the data, and 3) you do not sell this data to anyone — it is free.

Good luck and let us know if you find anything interesting.

The post Backblaze Hard Drive Stats for 2017 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Subway Elevators and Movie-Plot Threats

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/subway_elevator.html

Local residents are opposing adding an elevator to a subway station because terrorists might use it to detonate a bomb. No, really. There’s no actual threat analysis, only fear:

“The idea that people can then ride in on the subway with a bomb or whatever and come straight up in an elevator is awful to me,” said Claudia Ward, who lives in 15 Broad Street and was among a group of neighbors who denounced the plan at a recent meeting of the local community board. “It’s too easy for someone to slip through. And I just don’t want my family and my neighbors to be the collateral on that.”

[…]

Local residents plan to continue to fight, said Ms. Gerstman, noting that her building’s board decided against putting decorative planters at the building’s entrance over fears that shards could injure people in the event of a blast.

“Knowing that, and then seeing the proposal for giant glass structures in front of my building ­- ding ding ding! — what does a giant glass structure become in the event of an explosion?” she said.

In 2005, I coined the term “movie-plot threat” to denote a threat scenario that caused undue fear solely because of its specificity. Longtime readers of this blog will remember my annual Movie-Plot Threat Contests. I ended the contest in 2015 because I thought the meme had played itself out. Clearly there’s more work to be done.

2017 Weather Station round-up

Post Syndicated from Richard Hayler original https://www.raspberrypi.org/blog/2017-weather-station/

As we head into 2018 and start looking forward to longer days in the Northern hemisphere, I thought I’d take a look back at last year’s weather using data from Raspberry Pi Oracle Weather Stations. One of the great things about the kit is that as well as uploading all its readings to the shared online Oracle database, it stores them locally on the Pi in a MySQL or MariaDB database. This means you can use the power of SQL queries coupled with Python code to do automatic data analysis.

Soggy Surrey

My Weather Station has only been installed since May, so I didn’t have a full 52 weeks of my own data to investigate. Still, my station recorded more than 70000 measurements. Living in England, the first thing I wanted to know was: which was the wettest month? Unsurprisingly, both in terms of average daily rainfall and total rainfall, the start of the summer period — exactly when I went on a staycation — was the soggiest:

What about the global Weather Station community?

Even soggier Bavaria

Here things get slightly trickier. Although we have a shiny Oracle database full of all participating schools’ sensor readings, some of the data needs careful interpretation. Many kits are used as part of the school curriculum and do not always record genuine outdoor conditions. Nevertheless, it appears that Adalbert Stifter Gymnasium in Bavaria, Germany, had an even wetter 2017 than my home did:


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Where the wind blows

The records Robert-Dannemann Schule in Westerstede, Germany, is a good example of data which was most likely collected while testing and investigating the weather station sensors, rather than in genuine external conditions. Unless this school’s Weather Station was transported to a planet which suffers from extreme hurricanes, it wasn’t actually subjected to wind speeds above 1000km/h in November. Dismissing these and all similarly suspect records, I decided to award the ‘Windiest location of the year’ prize to CEIP Noalla-Telleiro, Spain.


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This school is right on the coast, and is subject to some strong and squally weather systems.

Weather Station at CEIP Noalla - Telleiro

Weather Station at CEIP Noalla-Telleiro

They’ve mounted their wind vane and anemometer nice and high, so I can see how they were able to record such high wind velocities.

A couple of Weather Stations have recently been commissioned in equally exposed places — it will be interesting to see whether they will record even higher speeds during 2018.

Highs and lows

After careful analysis and a few disqualifications (a couple of Weather Stations in contention for this category were housed indoors), the ‘Hottest location’ award went to High School of Chalastra in Thessaloniki, Greece. There were a couple of Weather Stations (the one at The Marwadi Education Foundation in India, for example) that reported higher average temperatures than Chalastra’s 24.54 ºC. However, they had uploaded far fewer readings and their data coverage of 2017 was only partial.


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At the other end of the thermometer, the location with the coldest average temperature is École de la Rose Sauvage in Calgary, Canada, with a very chilly 9.9 ºC.

Ecole de la Rose sauvage Weather Station

Weather Station at École de la Rose Sauvage

I suspect this school has a good chance of retaining the title: their lowest 2017 temperature of -24 ºC is likely to be beaten in 2018 due to extreme weather currently bringing a freezing start to the year in that part of the world.


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Analyse your own Weather Station data

If you have an Oracle Raspberry Pi Weather Station and would like to perform an annual review of your local data, you can use this Python script as a starting point. It will display a monthly summary of the temperature and rainfall for 2017, and you should be able to customise the code to focus on other sensor data or on a particular time of year. We’d love to see your results, so please share your findings with [email protected], and we’ll send you some limited-edition Weather Station stickers.

The post 2017 Weather Station round-up appeared first on Raspberry Pi.

The problematic Wannacry North Korea attribution

Post Syndicated from Robert Graham original http://blog.erratasec.com/2018/01/the-problematic-wannacry-north-korea.html

Last month, the US government officially “attributed” the Wannacry ransomware worm to North Korea. This attribution has three flaws, which are a good lesson for attribution in general.

It was an accident

The most important fact about Wannacry is that it was an accident. We’ve had 30 years of experience with Internet worms teaching us that worms are always accidents. While launching worms may be intentional, their effects cannot be predicted. While they appear to have targets, like Slammer against South Korea, or Witty against the Pentagon, further analysis shows this was just a random effect that was impossible to predict ahead of time. Only in hindsight are these effects explainable.
We should hold those causing accidents accountable, too, but it’s a different accountability. The U.S. has caused more civilian deaths in its War on Terror than the terrorists caused triggering that war. But we hold these to be morally different: the terrorists targeted the innocent, whereas the U.S. takes great pains to avoid civilian casualties. 
Since we are talking about blaming those responsible for accidents, we also must include the NSA in that mix. The NSA created, then allowed the release of, weaponized exploits. That’s like accidentally dropping a load of unexploded bombs near a village. When those bombs are then used, those having lost the weapons are held guilty along with those using them. Yes, while we should blame the hacker who added ETERNAL BLUE to their ransomware, we should also blame the NSA for losing control of ETERNAL BLUE.

A country and its assets are different

Was it North Korea, or hackers affilliated with North Korea? These aren’t the same.

It’s hard for North Korea to have hackers of its own. It doesn’t have citizens who grow up with computers to pick from. Moreover, an internal hacking corps would create tainted citizens exposed to dangerous outside ideas. Update: Some people have pointed out that Kim Il-sung University in the capital does have some contact with the outside world, with academics granted limited Internet access, so I guess some tainting is allowed. Still, what we know of North Korea hacking efforts largley comes from hackers they employ outside North Korea. It was the Lazurus Group, outside North Korea, that did Wannacry.
Instead, North Korea develops external hacking “assets”, supporting several external hacking groups in China, Japan, and South Korea. This is similar to how intelligence agencies develop human “assets” in foreign countries. While these assets do things for their handlers, they also have normal day jobs, and do many things that are wholly independent and even sometimes against their handler’s interests.
For example, this Muckrock FOIA dump shows how “CIA assets” independently worked for Castro and assassinated a Panamanian president. That they also worked for the CIA does not make the CIA responsible for the Panamanian assassination.
That CIA/intelligence assets work this way is well-known and uncontroversial. The fact that countries use hacker assets like this is the controversial part. These hackers do act independently, yet we refuse to consider this when we want to “attribute” attacks.

Attribution is political

We have far better attribution for the nPetya attacks. It was less accidental (they clearly desired to disrupt Ukraine), and the hackers were much closer to the Russian government (Russian citizens). Yet, the Trump administration isn’t fighting Russia, they are fighting North Korea, so they don’t officially attribute nPetya to Russia, but do attribute Wannacry to North Korea.
Trump is in conflict with North Korea. He is looking for ways to escalate the conflict. Attributing Wannacry helps achieve his political objectives.
That it was blatantly politics is demonstrated by the way it was released to the press. It wasn’t released in the normal way, where the administration can stand behind it, and get challenged on the particulars. Instead, it was pre-released through the normal system of “anonymous government officials” to the NYTimes, and then backed up with op-ed in the Wall Street Journal. The government leaks information like this when it’s weak, not when its strong.

The proper way is to release the evidence upon which the decision was made, so that the public can challenge it. Among the questions the public would ask is whether it they believe it was North Korea’s intention to cause precisely this effect, such as disabling the British NHS. Or, whether it was merely hackers “affiliated” with North Korea, or hackers carrying out North Korea’s orders. We cannot challenge the government this way because the government intentionally holds itself above such accountability.

Conclusion

We believe hacking groups tied to North Korea are responsible for Wannacry. Yet, even if that’s true, we still have three attribution problems. We still don’t know if that was intentional, in pursuit of some political goal, or an accident. We still don’t know if it was at the direction of North Korea, or whether their hacker assets acted independently. We still don’t know if the government has answers to these questions, or whether it’s exploiting this doubt to achieve political support for actions against North Korea.

Top 8 Best Practices for High-Performance ETL Processing Using Amazon Redshift

Post Syndicated from Thiyagarajan Arumugam original https://aws.amazon.com/blogs/big-data/top-8-best-practices-for-high-performance-etl-processing-using-amazon-redshift/

An ETL (Extract, Transform, Load) process enables you to load data from source systems into your data warehouse. This is typically executed as a batch or near-real-time ingest process to keep the data warehouse current and provide up-to-date analytical data to end users.

Amazon Redshift is a fast, petabyte-scale data warehouse that enables you easily to make data-driven decisions. With Amazon Redshift, you can get insights into your big data in a cost-effective fashion using standard SQL. You can set up any type of data model, from star and snowflake schemas, to simple de-normalized tables for running any analytical queries.

To operate a robust ETL platform and deliver data to Amazon Redshift in a timely manner, design your ETL processes to take account of Amazon Redshift’s architecture. When migrating from a legacy data warehouse to Amazon Redshift, it is tempting to adopt a lift-and-shift approach, but this can result in performance and scale issues long term. This post guides you through the following best practices for ensuring optimal, consistent runtimes for your ETL processes:

  • COPY data from multiple, evenly sized files.
  • Use workload management to improve ETL runtimes.
  • Perform table maintenance regularly.
  • Perform multiple steps in a single transaction.
  • Loading data in bulk.
  • Use UNLOAD to extract large result sets.
  • Use Amazon Redshift Spectrum for ad hoc ETL processing.
  • Monitor daily ETL health using diagnostic queries.

1. COPY data from multiple, evenly sized files

Amazon Redshift is an MPP (massively parallel processing) database, where all the compute nodes divide and parallelize the work of ingesting data. Each node is further subdivided into slices, with each slice having one or more dedicated cores, equally dividing the processing capacity. The number of slices per node depends on the node type of the cluster. For example, each DS2.XLARGE compute node has two slices, whereas each DS2.8XLARGE compute node has 16 slices.

When you load data into Amazon Redshift, you should aim to have each slice do an equal amount of work. When you load the data from a single large file or from files split into uneven sizes, some slices do more work than others. As a result, the process runs only as fast as the slowest, or most heavily loaded, slice. In the example shown below, a single large file is loaded into a two-node cluster, resulting in only one of the nodes, “Compute-0”, performing all the data ingestion:

When splitting your data files, ensure that they are of approximately equal size – between 1 MB and 1 GB after compression. The number of files should be a multiple of the number of slices in your cluster. Also, I strongly recommend that you individually compress the load files using gzip, lzop, or bzip2 to efficiently load large datasets.

When loading multiple files into a single table, use a single COPY command for the table, rather than multiple COPY commands. Amazon Redshift automatically parallelizes the data ingestion. Using a single COPY command to bulk load data into a table ensures optimal use of cluster resources, and quickest possible throughput.

2. Use workload management to improve ETL runtimes

Use Amazon Redshift’s workload management (WLM) to define multiple queues dedicated to different workloads (for example, ETL versus reporting) and to manage the runtimes of queries. As you migrate more workloads into Amazon Redshift, your ETL runtimes can become inconsistent if WLM is not appropriately set up.

I recommend limiting the overall concurrency of WLM across all queues to around 15 or less. This WLM guide helps you organize and monitor the different queues for your Amazon Redshift cluster.

When managing different workloads on your Amazon Redshift cluster, consider the following for the queue setup:

  • Create a queue dedicated to your ETL processes. Configure this queue with a small number of slots (5 or fewer). Amazon Redshift is designed for analytics queries, rather than transaction processing. The cost of COMMIT is relatively high, and excessive use of COMMIT can result in queries waiting for access to the commit queue. Because ETL is a commit-intensive process, having a separate queue with a small number of slots helps mitigate this issue.
  • Claim extra memory available in a queue. When executing an ETL query, you can take advantage of the wlm_query_slot_count to claim the extra memory available in a particular queue. For example, a typical ETL process might involve COPYing raw data into a staging table so that downstream ETL jobs can run transformations that calculate daily, weekly, and monthly aggregates. To speed up the COPY process (so that the downstream tasks can start in parallel sooner), the wlm_query_slot_count can be increased for this step.
  • Create a separate queue for reporting queries. Configure query monitoring rules on this queue to further manage long-running and expensive queries.
  • Take advantage of the dynamic memory parameters. They swap the memory from your ETL to your reporting queue after the ETL job has completed.

3. Perform table maintenance regularly

Amazon Redshift is a columnar database, which enables fast transformations for aggregating data. Performing regular table maintenance ensures that transformation ETLs are predictable and performant. To get the best performance from your Amazon Redshift database, you must ensure that database tables regularly are VACUUMed and ANALYZEd. The Analyze & Vacuum schema utility helps you automate the table maintenance task and have VACUUM & ANALYZE executed in a regular fashion.

  • Use VACUUM to sort tables and remove deleted blocks

During a typical ETL refresh process, tables receive new incoming records using COPY, and unneeded data (cold data) is removed using DELETE. New rows are added to the unsorted region in a table. Deleted rows are simply marked for deletion.

DELETE does not automatically reclaim the space occupied by the deleted rows. Adding and removing large numbers of rows can therefore cause the unsorted region and the number of deleted blocks to grow. This can degrade the performance of queries executed against these tables.

After an ETL process completes, perform VACUUM to ensure that user queries execute in a consistent manner. The complete list of tables that need VACUUMing can be found using the Amazon Redshift Util’s table_info script.

Use the following approaches to ensure that VACCUM is completed in a timely manner:

  • Use wlm_query_slot_count to claim all the memory allocated in the ETL WLM queue during the VACUUM process.
  • DROP or TRUNCATE intermediate or staging tables, thereby eliminating the need to VACUUM them.
  • If your table has a compound sort key with only one sort column, try to load your data in sort key order. This helps reduce or eliminate the need to VACUUM the table.
  • Consider using time series This helps reduce the amount of data you need to VACUUM.
  • Use ANALYZE to update database statistics

Amazon Redshift uses a cost-based query planner and optimizer using statistics about tables to make good decisions about the query plan for the SQL statements. Regular statistics collection after the ETL completion ensures that user queries run fast, and that daily ETL processes are performant. The Amazon Redshift utility table_info script provides insights into the freshness of the statistics. Keeping the statistics off (pct_stats_off) less than 20% ensures effective query plans for the SQL queries.

4. Perform multiple steps in a single transaction

ETL transformation logic often spans multiple steps. Because commits in Amazon Redshift are expensive, if each ETL step performs a commit, multiple concurrent ETL processes can take a long time to execute.

To minimize the number of commits in a process, the steps in an ETL script should be surrounded by a BEGIN…END statement so that a single commit is performed only after all the transformation logic has been executed. For example, here is an example multi-step ETL script that performs one commit at the end:

Begin
CREATE temporary staging_table;
INSERT INTO staging_table SELECT .. FROM source (transformation logic);
DELETE FROM daily_table WHERE dataset_date =?;
INSERT INTO daily_table SELECT .. FROM staging_table (daily aggregate);
DELETE FROM weekly_table WHERE weekending_date=?;
INSERT INTO weekly_table SELECT .. FROM staging_table(weekly aggregate);
Commit

5. Loading data in bulk

Amazon Redshift is designed to store and query petabyte-scale datasets. Using Amazon S3 you can stage and accumulate data from multiple source systems before executing a bulk COPY operation. The following methods allow efficient and fast transfer of these bulk datasets into Amazon Redshift:

  • Use a manifest file to ingest large datasets that span multiple files. The manifest file is a JSON file that lists all the files to be loaded into Amazon Redshift. Using a manifest file ensures that Amazon Redshift has a consistent view of the data to be loaded from S3, while also ensuring that duplicate files do not result in the same data being loaded more than one time.
  • Use temporary staging tables to hold the data for transformation. These tables are automatically dropped after the ETL session is complete. Temporary tables can be created using the CREATE TEMPORARY TABLE syntax, or by issuing a SELECT … INTO #TEMP_TABLE query. Explicitly specifying the CREATE TEMPORARY TABLE statement allows you to control the DISTRIBUTION KEY, SORT KEY, and compression settings to further improve performance.
  • User ALTER table APPEND to swap data from the staging tables to the target table. Data in the source table is moved to matching columns in the target table. Column order doesn’t matter. After data is successfully appended to the target table, the source table is empty. ALTER TABLE APPEND is much faster than a similar CREATE TABLE AS or INSERT INTO operation because it doesn’t involve copying or moving data.

6. Use UNLOAD to extract large result sets

Fetching a large number of rows using SELECT is expensive and takes a long time. When a large amount of data is fetched from the Amazon Redshift cluster, the leader node has to hold the data temporarily until the fetches are complete. Further, data is streamed out sequentially, which results in longer elapsed time. As a result, the leader node can become hot, which not only affects the SELECT that is being executed, but also throttles resources for creating execution plans and managing the overall cluster resources. Here is an example of a large SELECT statement. Notice that the leader node is doing most of the work to stream out the rows:

Use UNLOAD to extract large results sets directly to S3. After it’s in S3, the data can be shared with multiple downstream systems. By default, UNLOAD writes data in parallel to multiple files according to the number of slices in the cluster. All the compute nodes participate to quickly offload the data into S3.

If you are extracting data for use with Amazon Redshift Spectrum, you should make use of the MAXFILESIZE parameter to and keep files are 150 MB. Similar to item 1 above, having many evenly sized files ensures that Redshift Spectrum can do the maximum amount of work in parallel.

7. Use Redshift Spectrum for ad hoc ETL processing

Events such as data backfill, promotional activity, and special calendar days can trigger additional data volumes that affect the data refresh times in your Amazon Redshift cluster. To help address these spikes in data volumes and throughput, I recommend staging data in S3. After data is organized in S3, Redshift Spectrum enables you to query it directly using standard SQL. In this way, you gain the benefits of additional capacity without having to resize your cluster.

For tips on getting started with and optimizing the use of Redshift Spectrum, see the previous post, 10 Best Practices for Amazon Redshift Spectrum.

8. Monitor daily ETL health using diagnostic queries

Monitoring the health of your ETL processes on a regular basis helps identify the early onset of performance issues before they have a significant impact on your cluster. The following monitoring scripts can be used to provide insights into the health of your ETL processes:

Script Use when… Solution
commit_stats.sql – Commit queue statistics from past days, showing largest queue length and queue time first DML statements such as INSERT/UPDATE/COPY/DELETE operations take several times longer to execute when multiple of these operations are in progress Set up separate WLM queues for the ETL process and limit the concurrency to < 5.
copy_performance.sql –  Copy command statistics for the past days Daily COPY operations take longer to execute • Follow the best practices for the COPY command.
• Analyze data growth with the incoming datasets and consider cluster resize to meet the expected SLA.
table_info.sql – Table skew and unsorted statistics along with storage and key information Transformation steps take longer to execute • Set up regular VACCUM jobs to address unsorted rows and claim the deleted blocks so that transformation SQL execute optimally.
• Consider a table redesign to avoid data skewness.
v_check_transaction_locks.sql – Monitor transaction locks INSERT/UPDATE/COPY/DELETE operations on particular tables do not respond back in timely manner, compared to when run after the ETL Multiple DML statements are operating on the same target table at the same moment from different transactions. Set up ETL job dependency so that they execute serially for the same target table.
v_get_schema_priv_by_user.sql – Get the schema that the user has access to Reporting users can view intermediate tables Set up separate database groups for reporting and ETL users, and grants access to objects using GRANT.
v_generate_tbl_ddl.sql – Get the table DDL You need to create an empty table with same structure as target table for data backfill Generate DDL using this script for data backfill.
v_space_used_per_tbl.sql – monitor space used by individual tables Amazon Redshift data warehouse space growth is trending upwards more than normal

Analyze the individual tables that are growing at higher rate than normal. Consider data archival using UNLOAD to S3 and Redshift Spectrum for later analysis.

Use unscanned_table_summary.sql to find unused table and archive or drop them.

top_queries.sql – Return the top 50 time consuming statements aggregated by its text ETL transformations are taking longer to execute Analyze the top transformation SQL and use EXPLAIN to find opportunities for tuning the query plan.

There are several other useful scripts available in the amazon-redshift-utils repository. The AWS Lambda Utility Runner runs a subset of these scripts on a scheduled basis, allowing you to automate much of monitoring of your ETL processes.

Example ETL process

The following ETL process reinforces some of the best practices discussed in this post. Consider the following four-step daily ETL workflow where data from an RDBMS source system is staged in S3 and then loaded into Amazon Redshift. Amazon Redshift is used to calculate daily, weekly, and monthly aggregations, which are then unloaded to S3, where they can be further processed and made available for end-user reporting using a number of different tools, including Redshift Spectrum and Amazon Athena.

Step 1:  Extract from the RDBMS source to a S3 bucket

In this ETL process, the data extract job fetches change data every 1 hour and it is staged into multiple hourly files. For example, the staged S3 folder looks like the following:

 [[email protected] ~]$ aws s3 ls s3://<<S3 Bucket>>/batch/2017/07/02/
2017-07-02 01:59:58   81900220 20170702T01.export.gz
2017-07-02 02:59:56   84926844 20170702T02.export.gz
2017-07-02 03:59:54   78990356 20170702T03.export.gz
…
2017-07-02 22:00:03   75966745 20170702T21.export.gz
2017-07-02 23:00:02   89199874 20170702T22.export.gz
2017-07-02 00:59:59   71161715 20170702T23.export.gz

Organizing the data into multiple, evenly sized files enables the COPY command to ingest this data using all available resources in the Amazon Redshift cluster. Further, the files are compressed (gzipped) to further reduce COPY times.

Step 2: Stage data to the Amazon Redshift table for cleansing

Ingesting the data can be accomplished using a JSON-based manifest file. Using the manifest file ensures that S3 eventual consistency issues can be eliminated and also provides an opportunity to dedupe any files if needed. A sample manifest20170702.json file looks like the following:

{
  "entries": [
    {"url":" s3://<<S3 Bucket>>/batch/2017/07/02/20170702T01.export.gz", "mandatory":true},
    {"url":" s3://<<S3 Bucket>>/batch/2017/07/02/20170702T02.export.gz", "mandatory":true},
    …
    {"url":" s3://<<S3 Bucket>>/batch/2017/07/02/20170702T23.export.gz", "mandatory":true}
  ]
}

The data can be ingested using the following command:

SET wlm_query_slot_count TO <<max available concurrency in the ETL queue>>;
COPY stage_tbl FROM 's3:// <<S3 Bucket>>/batch/manifest20170702.json' iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole' manifest;

Because the downstream ETL processes depend on this COPY command to complete, the wlm_query_slot_count is used to claim all the memory available to the queue. This helps the COPY command complete as quickly as possible.

Step 3: Transform data to create daily, weekly, and monthly datasets and load into target tables

Data is staged in the “stage_tbl” from where it can be transformed into the daily, weekly, and monthly aggregates and loaded into target tables. The following job illustrates a typical weekly process:

Begin
INSERT into ETL_LOG (..) values (..);
DELETE from weekly_tbl where dataset_week = <<current week>>;
INSERT into weekly_tbl (..)
  SELECT date_trunc('week', dataset_day) AS week_begin_dataset_date, SUM(C1) AS C1, SUM(C2) AS C2
	FROM   stage_tbl
GROUP BY date_trunc('week', dataset_day);
INSERT into AUDIT_LOG values (..);
COMMIT;
End;

As shown above, multiple steps are combined into one transaction to perform a single commit, reducing contention on the commit queue.

Step 4: Unload the daily dataset to populate the S3 data lake bucket

The transformed results are now unloaded into another S3 bucket, where they can be further processed and made available for end-user reporting using a number of different tools, including Redshift Spectrum and Amazon Athena.

unload ('SELECT * FROM weekly_tbl WHERE dataset_week = <<current week>>’) TO 's3:// <<S3 Bucket>>/datalake/weekly/20170526/' iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole';

Summary

Amazon Redshift lets you easily operate petabyte-scale data warehouses on the cloud. This post summarized the best practices for operating scalable ETL natively within Amazon Redshift. I demonstrated efficient ways to ingest and transform data, along with close monitoring. I also demonstrated the best practices being used in a typical sample ETL workload to transform the data into Amazon Redshift.

If you have questions or suggestions, please comment below.

 


About the Author

Thiyagarajan Arumugam is a Big Data Solutions Architect at Amazon Web Services and designs customer architectures to process data at scale. Prior to AWS, he built data warehouse solutions at Amazon.com. In his free time, he enjoys all outdoor sports and practices the Indian classical drum mridangam.

 

Detecting Drone Surveillance with Traffic Analysis

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/detecting_drone.html

This is clever:

Researchers at Ben Gurion University in Beer Sheva, Israel have built a proof-of-concept system for counter-surveillance against spy drones that demonstrates a clever, if not exactly simple, way to determine whether a certain person or object is under aerial surveillance. They first generate a recognizable pattern on whatever subject­ — a window, say — someone might want to guard from potential surveillance. Then they remotely intercept a drone’s radio signals to look for that pattern in the streaming video the drone sends back to its operator. If they spot it, they can determine that the drone is looking at their subject.

In other words, they can see what the drone sees, pulling out their recognizable pattern from the radio signal, even without breaking the drone’s encrypted video.

The details have to do with the way drone video is compressed:

The researchers’ technique takes advantage of an efficiency feature streaming video has used for years, known as “delta frames.” Instead of encoding video as a series of raw images, it’s compressed into a series of changes from the previous image in the video. That means when a streaming video shows a still object, it transmits fewer bytes of data than when it shows one that moves or changes color.

That compression feature can reveal key information about the content of the video to someone who’s intercepting the streaming data, security researchers have shown in recent research, even when the data is encrypted.

Research paper and video.

The EU is Working On Its Own Piracy Watch-List

Post Syndicated from Ernesto original https://torrentfreak.com/the-eu-is-working-on-its-own-piracy-watch-list-180124/

Earlier this month, the Office of the US Trade Representative (USTR) released an updated version of its “Out-of-Cycle Review of Notorious Markets,” ostensibly identifying some of the worst IP-offenders worldwide.

The annual list overview helps to guide the U.S. Government’s position towards foreign countries when it comes to copyright enforcement.

The most recent version featured traditional pirate sites such as The Pirate Bay, Rapidgator, and Gostream, but also the Russian social network VK and China-based marketplaces Alibaba and Taobao.com.

Since the list only identifies foreign sites, American services are never included. However, this restriction doesn’t apply in Europe, where the European Commission announced this week that it’s working on its own piracy watch list.

“The European Commission – on the basis of input from the stakeholders – after thorough verification of the received information – intends to publish a so called ‘Counterfeit and Piracy Watch-List’ in 2018, which will be updated regularly,” the EU’s call for submissions reads.

The EU watch list will operate in a similar fashion to the US equivalent and will be used to encourage site operators and foreign governments to take action.

“The list will identify and describe the most problematic marketplaces – with special focus on online marketplaces – in order to encourage their operators and owners as well as the responsible local authorities and governments to take the necessary actions and measures to reduce the availability of IPR infringing goods or services.”

In recent years various copyright holder groups have repeatedly complained about a lack of anti-piracy initiatives from companies such as Google and Cloudflare, so it will be interesting to see if these will be mentioned.

The same is true for online marketplaces. Responding to the US list last week, Alibaba also highlighted that several American companies suffer the same piracy and counterfeiting problems as they do, without being reprimanded.

“What about Amazon, eBay and others? USTR has no basis for comparison, because it does not ask for similar data from U.S. companies,” Alibaba noted in a rebuttal.

The EU watch list is clearly inspired by the US counterpart. It shows striking similarities with the US version of the watch list and some of the language appears to be copied (or pirated) word for word.

The EU writes, for example, that their list “will not mean to reflect findings of legal violations, nor will it reflect the European Union’s analysis of the general intellectual property rights protection and enforcement climate in the country or countries concerned.”

Just a few days earlier the USTR noted that its list “does not make findings of legal violations. Nor does it reflect the U.S. Government’s analysis of the general IP protection and enforcement climate in the countries connected with the listed markets.”

The above means that, despite branding foreign services as notorious offenders, these are mere allegations. No hard proof is to be expected in the report, nor will the EU research the matter on its own.

If the US example is followed, the watch list will be mostly an overview of copyright holder complaints, signed by the authorities. The latter is not without controversy, as China says it doubts the objectivity of USTR’s report for this very reason.

Copyright holders and other interested parties are invited to submit their contributions and comments by 31 March 2018, and the final list is expected to be released later in the year.

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

Security Breaches Don’t Affect Stock Price

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/security_breach.html

Interesting research: “Long-term market implications of data breaches, not,” by Russell Lange and Eric W. Burger.

Abstract: This report assesses the impact disclosure of data breaches has on the total returns and volatility of the affected companies’ stock, with a focus on the results relative to the performance of the firms’ peer industries, as represented through selected indices rather than the market as a whole. Financial performance is considered over a range of dates from 3 days post-breach through 6 months post-breach, in order to provide a longer-term perspective on the impact of the breach announcement.

Key findings:

  • While the difference in stock price between the sampled breached companies and their peers was negative (1.13%) in the first 3 days following announcement of a breach, by the 14th day the return difference had rebounded to + 0.05%, and on average remained positive through the period assessed.
  • For the differences in the breached companies’ betas and the beta of their peer sets, the differences in the means of 8 months pre-breach versus post-breach was not meaningful at 90, 180, and 360 day post-breach periods.

  • For the differences in the breached companies’ beta correlations against the peer indices pre- and post-breach, the difference in the means of the rolling 60 day correlation 8 months pre- breach versus post-breach was not meaningful at 90, 180, and 360 day post-breach periods.

  • In regression analysis, use of the number of accessed records, date, data sensitivity, and malicious versus accidental leak as variables failed to yield an R2 greater than 16.15% for response variables of 3, 14, 60, and 90 day return differential, excess beta differential, and rolling beta correlation differential, indicating that the financial impact on breached companies was highly idiosyncratic.

  • Based on returns, the most impacted industries at the 3 day post-breach date were U.S. Financial Services, Transportation, and Global Telecom. At the 90 day post-breach date, the three most impacted industries were U.S. Financial Services, U.S. Healthcare, and Global Telecom.

The market isn’t going to fix this. If we want better security, we need to regulate the market.

Note: The article is behind a paywall. An older version is here. A similar article is here.

timeShift(GrafanaBuzz, 1w) Issue 30

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/19/timeshiftgrafanabuzz-1w-issue-30/

Welcome to TimeShift

We’re only 6 weeks away from the next GrafanaCon and here at Grafana Labs we’re buzzing with excitement. We have some great talks lined up that you won’t want to miss.

This week’s TimeShift covers Grafana’s annotation functionality, monitoring with Prometheus, integrating Grafana with NetFlow and a peek inside Stream’s monitoring stack. Enjoy!


Latest Stable Release

Grafana 4.6.3 is now available. Latest bugfixes include:

  • Gzip: Fixes bug Gravatar images when gzip was enabled #5952
  • Alert list: Now shows alert state changes even after adding manual annotations on dashboard #99513
  • Alerting: Fixes bug where rules evaluated as firing when all conditions was false and using OR operator. #93183
  • Cloudwatch: CloudWatch no longer display metrics’ default alias #101514, thx @mtanda

Download Grafana 4.6.3 Now


From the Blogosphere

Walkthrough: Watch your Ansible deployments in Grafana!: Your graphs start spiking and your platform begins behaving abnormally. Did the config change in a deployment, causing the problem? This article covers Grafana’s new annotation functionality, and specifically, how to create deployment annotations via Ansible playbooks.

Application Monitoring in OpenShift with Prometheus and Grafana: There are many article describing how to monitor OpenShift with Prometheus running in the same cluster, but what if you don’t have admin permissions to the cluster you need to monitor?

Spring Boot Metrics Monitoring Using Prometheus & Grafana: As the title suggests, this post walks you through how to configure Prometheus and Grafana to monitor you Spring Boot application metrics.

How to Integrate Grafana with NetFlow: Learn how to monitor NetFlow from Scrutinizer using Grafana’s SimpleJSON data source.

Stream & Go: News Feeds for Over 300 Million End Users: Stream lets you build scalable newsfeeds and activity streams via their API, which is used by more than 300 million end users. In this article, they discuss their monitoring stack and why they chose particular components and technologies.


GrafanaCon EU Tickets are Going Fast!

We’re six weeks from kicking off GrafanaCon EU! Join us for talks from Google, Bloomberg, Tinder, eBay and more! You won’t want to miss two great days of open source monitoring talks and fun in Amsterdam. Get your tickets before they sell out!

Get Your Ticket Now


Grafana Plugins

We have a couple of plugin updates to share this week that add some new features and improvements. Updating your plugins is easy. For on-prem Grafana, use the Grafana-cli tool, or update with 1 click on your Hosted Grafana.

UPDATED PLUGIN

Druid Data Source – This new update is packed with new features. Notable enhancement include:

  • Post Aggregation feature
  • Support for thetaSketch
  • Improvements to the Query editor

Update Now

UPDATED PLUGIN

Breadcrumb Panel – The Breadcrumb Panel is a small panel you can include in your dashboard that tracks other dashboards you have visited – making it easy to navigate back to a previously visited dashboard. The latest release adds support for dashboards loaded from a file.

Update Now


Upcoming Events

In between code pushes we like to speak at, sponsor and attend all kinds of conferences and meetups. We also like to make sure we mention other Grafana-related events happening all over the world. If you’re putting on just such an event, let us know and we’ll list it here.

SnowCamp 2018: Yves Brissaud – Application metrics with Prometheus and Grafana | Grenoble, France – Jan 24, 2018:
We’ll take a look at how Prometheus, Grafana and a bit of code make it possible to obtain temporal data to visualize the state of our applications as well as to help with development and debugging.

Register Now

Women Who Go Berlin: Go Workshop – Monitoring and Troubleshooting using Prometheus and Grafana | Berlin, Germany – Jan 31, 2018: In this workshop we will learn about one of the most important topics in making apps production ready: Monitoring. We will learn how to use tools you’ve probably heard a lot about – Prometheus and Grafana, and using what we learn we will troubleshoot a particularly buggy Go app.

Register Now

FOSDEM | Brussels, Belgium – Feb 3-4, 2018: FOSDEM is a free developer conference where thousands of developers of free and open source software gather to share ideas and technology. There is no need to register; all are welcome.

Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
Carl Bergquist – Quickie: Monitoring? Not OPS Problem

Why should we monitor our system? Why can’t we just rely on the operations team anymore? They use to be able to do that. What’s currently changing? Presentation content: – Why do we monitor our system – How did it use to work? – Whats changing – Why do we need to shift focus – Everyone should be on call. – Resilience is the goal (Best way of having someone care about quality is to make them responsible).

Register Now

Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
Leonard Gram – Presentation: DevOps Deconstructed

What’s a Site Reliability Engineer and how’s that role different from the DevOps engineer my boss wants to hire? I really don’t want to be on call, should I? Is Docker the right place for my code or am I better of just going straight to Serverless? And why should I care about any of it? I’ll try to answer some of these questions while looking at what DevOps really is about and how commodisation of servers through “the cloud” ties into it all. This session will be an opinionated piece from a developer who’s been on-call for the past 6 years and would like to convince you to do the same, at least once.

Register Now

Stockholm Metrics and Monitoring | Stockholm, Sweden – Feb 7, 2018:
Observability 3 ways – Logging, Metrics and Distributed Tracing

Let’s talk about often confused telemetry tools: Logging, Metrics and Distributed Tracing. We’ll show how you capture latency using each of the tools and how they work differently. Through examples and discussion, we’ll note edge cases where certain tools have advantages over others. By the end of this talk, we’ll better understand how each of Logging, Metrics and Distributed Tracing aids us in different ways to understand our applications.

Register Now

OpenNMS – Introduction to “Grafana” | Webinar – Feb 21, 2018:
IT monitoring helps detect emerging hardware damage and performance bottlenecks in the enterprise network before any consequential damage or disruption to business processes occurs. The powerful open-source OpenNMS software monitors a network, including all connected devices, and provides logging of a variety of data that can be used for analysis and planning purposes. In our next OpenNMS webinar on February 21, 2018, we introduce “Grafana” – a web-based tool for creating and displaying dashboards from various data sources, which can be perfectly combined with OpenNMS.

Register Now


Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

As we say with pie charts, use emojis wisely 😉


Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

Check out our Open Positions


How are we doing?

That wraps up our 30th issue of TimeShift. What do you think? Are there other types of content you’d like to see here? Submit a comment on this issue below, or post something at our community forum.

Follow us on Twitter, like us on Facebook, and join the Grafana Labs community.

Optimize Delivery of Trending, Personalized News Using Amazon Kinesis and Related Services

Post Syndicated from Yukinori Koide original https://aws.amazon.com/blogs/big-data/optimize-delivery-of-trending-personalized-news-using-amazon-kinesis-and-related-services/

This is a guest post by Yukinori Koide, an the head of development for the Newspass department at Gunosy.

Gunosy is a news curation application that covers a wide range of topics, such as entertainment, sports, politics, and gourmet news. The application has been installed more than 20 million times.

Gunosy aims to provide people with the content they want without the stress of dealing with a large influx of information. We analyze user attributes, such as gender and age, and past activity logs like click-through rate (CTR). We combine this information with article attributes to provide trending, personalized news articles to users.

In this post, I show you how to process user activity logs in real time using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services.

Why does Gunosy need real-time processing?

Users need fresh and personalized news. There are two constraints to consider when delivering appropriate articles:

  • Time: Articles have freshness—that is, they lose value over time. New articles need to reach users as soon as possible.
  • Frequency (volume): Only a limited number of articles can be shown. It’s unreasonable to display all articles in the application, and users can’t read all of them anyway.

To deliver fresh articles with a high probability that the user is interested in them, it’s necessary to include not only past user activity logs and some feature values of articles, but also the most recent (real-time) user activity logs.

We optimize the delivery of articles with these two steps.

  1. Personalization: Deliver articles based on each user’s attributes, past activity logs, and feature values of each article—to account for each user’s interests.
  2. Trends analysis/identification: Optimize delivering articles using recent (real-time) user activity logs—to incorporate the latest trends from all users.

Optimizing the delivery of articles is always a cold start. Initially, we deliver articles based on past logs. We then use real-time data to optimize as quickly as possible. In addition, news has a short freshness time. Specifically, day-old news is past news, and even the news that is three hours old is past news. Therefore, shortening the time between step 1 and step 2 is important.

To tackle this issue, we chose AWS for processing streaming data because of its fully managed services, cost-effectiveness, and so on.

Solution

The following diagrams depict the architecture for optimizing article delivery by processing real-time user activity logs

There are three processing flows:

  1. Process real-time user activity logs.
  2. Store and process all user-based and article-based logs.
  3. Execute ad hoc or heavy queries.

In this post, I focus on the first processing flow and explain how it works.

Process real-time user activity logs

The following are the steps for processing user activity logs in real time using Kinesis Data Streams and Kinesis Data Analytics.

  1. The Fluentd server sends the following user activity logs to Kinesis Data Streams:
{"article_id": 12345, "user_id": 12345, "action": "click"}
{"article_id": 12345, "user_id": 12345, "action": "impression"}
...
  1. Map rows of logs to columns in Kinesis Data Analytics.

  1. Set the reference data to Kinesis Data Analytics from Amazon S3.

a. Gunosy has user attributes such as gender, age, and segment. Prepare the following CSV file (user_id, gender, segment_id) and put it in Amazon S3:

101,female,1
102,male,2
103,female,3
...

b. Add the application reference data source to Kinesis Data Analytics using the AWS CLI:

$ aws kinesisanalytics add-application-reference-data-source \
  --application-name <my-application-name> \
  --current-application-version-id <version-id> \
  --reference-data-source '{
  "TableName": "REFERENCE_DATA_SOURCE",
  "S3ReferenceDataSource": {
    "BucketARN": "arn:aws:s3:::<my-bucket-name>",
    "FileKey": "mydata.csv",
    "ReferenceRoleARN": "arn:aws:iam::<account-id>:role/..."
  },
  "ReferenceSchema": {
    "RecordFormat": {
      "RecordFormatType": "CSV",
      "MappingParameters": {
        "CSVMappingParameters": {"RecordRowDelimiter": "\n", "RecordColumnDelimiter": ","}
      }
    },
    "RecordEncoding": "UTF-8",
    "RecordColumns": [
      {"Name": "USER_ID", "Mapping": "0", "SqlType": "INTEGER"},
      {"Name": "GENDER",  "Mapping": "1", "SqlType": "VARCHAR(32)"},
      {"Name": "SEGMENT_ID", "Mapping": "2", "SqlType": "INTEGER"}
    ]
  }
}'

This application reference data source can be referred on Kinesis Data Analytics.

  1. Run a query against the source data stream on Kinesis Data Analytics with the application reference data source.

a. Define the temporary stream named TMP_SQL_STREAM.

CREATE OR REPLACE STREAM "TMP_SQL_STREAM" (
  GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER
);

b. Insert the joined source stream and application reference data source into the temporary stream.

CREATE OR REPLACE PUMP "TMP_PUMP" AS
INSERT INTO "TMP_SQL_STREAM"
SELECT STREAM
  R.GENDER, R.SEGMENT_ID, S.ARTICLE_ID, S.ACTION
FROM      "SOURCE_SQL_STREAM_001" S
LEFT JOIN "REFERENCE_DATA_SOURCE" R
  ON S.USER_ID = R.USER_ID;

c. Define the destination stream named DESTINATION_SQL_STREAM.

CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
  TIME TIMESTAMP, GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER, 
  IMPRESSION INTEGER, CLICK INTEGER
);

d. Insert the processed temporary stream, using a tumbling window, into the destination stream per minute.

CREATE OR REPLACE PUMP "STREAM_PUMP" AS
INSERT INTO "DESTINATION_SQL_STREAM"
SELECT STREAM
  ROW_TIME AS TIME,
  GENDER, SEGMENT_ID, ARTICLE_ID,
  SUM(CASE ACTION WHEN 'impression' THEN 1 ELSE 0 END) AS IMPRESSION,
  SUM(CASE ACTION WHEN 'click' THEN 1 ELSE 0 END) AS CLICK
FROM "TMP_SQL_STREAM"
GROUP BY
  GENDER, SEGMENT_ID, ARTICLE_ID,
  FLOOR("TMP_SQL_STREAM".ROWTIME TO MINUTE);

The results look like the following:

  1. Insert the results into Amazon Elasticsearch Service (Amazon ES).
  2. Batch servers get results from Amazon ES every minute. They then optimize delivering articles with other data sources using a proprietary optimization algorithm.

How to connect a stream to another stream in another AWS Region

When we built the solution, Kinesis Data Analytics was not available in the Asia Pacific (Tokyo) Region, so we used the US West (Oregon) Region. The following shows how we connected a data stream to another data stream in the other Region.

There is no need to continue containing all components in a single AWS Region, unless you have a situation where a response difference at the millisecond level is critical to the service.

Benefits

The solution provides benefits for both our company and for our users. Benefits for the company are cost savings—including development costs, operational costs, and infrastructure costs—and reducing delivery time. Users can now find articles of interest more quickly. The solution can process more than 500,000 records per minute, and it enables fast and personalized news curating for our users.

Conclusion

In this post, I showed you how we optimize trending user activities to personalize news using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services in Gunosy.

AWS gives us a quick and economical solution and a good experience.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Implement Serverless Log Analytics Using Amazon Kinesis Analytics and Joining and Enriching Streaming Data on Amazon Kinesis.


About the Authors

Yukinori Koide is the head of development for the Newspass department at Gunosy. He is working on standardization of provisioning and deployment flow, promoting the utilization of serverless and containers for machine learning and AI services. His favorite AWS services are DynamoDB, Lambda, Kinesis, and ECS.

 

 

 

Akihiro Tsukada is a start-up solutions architect with AWS. He supports start-up companies in Japan technically at many levels, ranging from seed to later-stage.

 

 

 

 

Yuta Ishii is a solutions architect with AWS. He works with our customers to provide architectural guidance for building media & entertainment services, helping them improve the value of their services when using AWS.

 

 

 

 

 

Cloud Babble: The Jargon of Cloud Storage

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/what-is-cloud-computing/

Cloud Babble

One of the things we in the technology business are good at is coming up with names, phrases, euphemisms, and acronyms for the stuff that we create. The Cloud Storage market is no different, and we’d like to help by illuminating some of the cloud storage related terms that you might come across. We know this is just a start, so please feel free to add in your favorites in the comments section below and we’ll update this post accordingly.

Clouds

The cloud is really just a collection of purpose built servers. In a public cloud the servers are shared between multiple unrelated tenants. In a private cloud, the servers are dedicated to a single tenant or sometimes a group of related tenants. A public cloud is off-site, while a private cloud can be on-site or off-site – or on-prem or off-prem, if you prefer.

Both Sides Now: Hybrid Clouds

Speaking of on-prem and off-prem, there are Hybrid Clouds or Hybrid Data Clouds depending on what you need. Both are based on the idea that you extend your local resources (typically on-prem) to the cloud (typically off-prem) as needed. This extension is controlled by software that decides, based on rules you define, what needs to be done where.

A Hybrid Data Cloud is specific to data. For example, you can set up a rule that says all accounting files that have not been touched in the last year are automatically moved off-prem to cloud storage. The files are still available; they are just no longer stored on your local systems. The rules can be defined to fit an organization’s workflow and data retention policies.

A Hybrid Cloud is similar to a Hybrid Data Cloud except it also extends compute. For example, at the end of the quarter, you can spin up order processing application instances off-prem as needed to add to your on-prem capacity. Of course, determining where the transactional data used and created by these applications resides can be an interesting systems design challenge.

Clouds in my Coffee: Fog

Typically, public and private clouds live in large buildings called data centers. Full of servers, networking equipment, and clean air, data centers need lots of power, lots of networking bandwidth, and lots of space. This often limits where data centers are located. The further away you are from a data center, the longer it generally takes to get your data to and from there. This is known as latency. That’s where “Fog” comes in.

Fog is often referred to as clouds close to the ground. Fog, in our cloud world, is basically having a “little” data center near you. This can make data storage and even cloud based processing faster for everyone nearby. Data, and less so processing, can be transferred to/from the Fog to the Cloud when time is less a factor. Data could also be aggregated in the Fog and sent to the Cloud. For example, your electric meter could report its minute-by-minute status to the Fog for diagnostic purposes. Then once a day the aggregated data could be send to the power company’s Cloud for billing purposes.

Another term used in place of Fog is Edge, as in computing at the Edge. In either case, a given cloud (data center) usually has multiple Edges (little data centers) connected to it. The connection between the Edge and the Cloud is sometimes known as the middle-mile. The network in the middle-mile can be less robust than that required to support a stand-alone data center. For example, the middle-mile can use 1 Gbps lines, versus a data center, which would require multiple 10 Gbps lines.

Heavy Clouds No Rain: Data

We’re all aware that we are creating, processing, and storing data faster than ever before. All of this data is stored in either a structured or more likely an unstructured way. Databases and data warehouses are structured ways to store data, but a vast amount of data is unstructured – meaning the schema and data access requirements are not known until the data is queried. A large pool of unstructured data in a flat architecture can be referred to as a Data Lake.

A Data Lake is often created so we can perform some type of “big data” analysis. In an over simplified example, let’s extend the lake metaphor a bit and ask the question; “how many fish are in our lake?” To get an answer, we take a sufficient sample of our lake’s water (data), count the number of fish we find, and extrapolate based on the size of the lake to get an answer within a given confidence interval.

A Data Lake is usually found in the cloud, an excellent place to store large amounts of non-transactional data. Watch out as this can lead to our data having too much Data Gravity or being locked in the Hotel California. This could also create a Data Silo, thereby making a potential data Lift-and-Shift impossible. Let me explain:

  • Data Gravity — Generally, the more data you collect in one spot, the harder it is to move. When you store data in a public cloud, you have to pay egress and/or network charges to download the data to another public cloud or even to your own on-premise systems. Some public cloud vendors charge a lot more than others, meaning that depending on your public cloud provider, your data could financially have a lot more gravity than you expected.
  • Hotel California — This is like Data Gravity but to a lesser scale. Your data is in the Hotel California if, to paraphrase, “your data can check out any time you want, but it can never leave.” If the cost of downloading your data is limiting the things you want to do with that data, then your data is in the Hotel California. Data is generally most valuable when used, and with cloud storage that can include archived data. This assumes of course that the archived data is readily available, and affordable, to download. When considering a cloud storage project always figure in the cost of using your own data.
  • Data Silo — Over the years, businesses have suffered from organizational silos as information is not shared between different groups, but instead needs to travel up to the top of the silo before it can be transferred to another silo. If your data is “trapped” in a given cloud by the cost it takes to share such data, then you may have a Data Silo, and that’s exactly opposite of what the cloud should do.
  • Lift-and-Shift — This term is used to define the movement of data or applications from one data center to another or from on-prem to off-prem systems. The move generally occurs all at once and once everything is moved, systems are operational and data is available at the new location with few, if any, changes. If your data has too much gravity or is locked in a hotel, a data lift-and-shift may break the bank.

I Can See Clearly Now

Hopefully, the cloudy terms we’ve covered are well, less cloudy. As we mentioned in the beginning, our compilation is just a start, so please feel free to add in your favorite cloud term in the comments section below and we’ll update this post with your contributions. Keep your entries “clean,” and please no words or phrases that are really adverts for your company. Thanks.

The post Cloud Babble: The Jargon of Cloud Storage appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.