Tag Archives: Resource

New AWS Auto Scaling – Unified Scaling For Your Cloud Applications

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-auto-scaling-unified-scaling-for-your-cloud-applications/

I’ve been talking about scalability for servers and other cloud resources for a very long time! Back in 2006, I wrote “This is the new world of scalable, on-demand web services. Pay for what you need and use, and not a byte more.” Shortly after we launched Amazon Elastic Compute Cloud (EC2), we made it easy for you to do this with the simultaneous launch of Elastic Load Balancing, EC2 Auto Scaling, and Amazon CloudWatch. Since then we have added Auto Scaling to other AWS services including ECS, Spot Fleets, DynamoDB, Aurora, AppStream 2.0, and EMR. We have also added features such as target tracking to make it easier for you to scale based on the metric that is most appropriate for your application.

Introducing AWS Auto Scaling
Today we are making it easier for you to use the Auto Scaling features of multiple AWS services from a single user interface with the introduction of AWS Auto Scaling. This new service unifies and builds on our existing, service-specific, scaling features. It operates on any desired EC2 Auto Scaling groups, EC2 Spot Fleets, ECS tasks, DynamoDB tables, DynamoDB Global Secondary Indexes, and Aurora Replicas that are part of your application, as described by an AWS CloudFormation stack or in AWS Elastic Beanstalk (we’re also exploring some other ways to flag a set of resources as an application for use with AWS Auto Scaling).

You no longer need to set up alarms and scaling actions for each resource and each service. Instead, you simply point AWS Auto Scaling at your application and select the services and resources of interest. Then you select the desired scaling option for each one, and AWS Auto Scaling will do the rest, helping you to discover the scalable resources and then creating a scaling plan that addresses the resources of interest.

If you have tried to use any of our Auto Scaling options in the past, you undoubtedly understand the trade-offs involved in choosing scaling thresholds. AWS Auto Scaling gives you a variety of scaling options: You can optimize for availability, keeping plenty of resources in reserve in order to meet sudden spikes in demand. You can optimize for costs, running close to the line and accepting the possibility that you will tax your resources if that spike arrives. Alternatively, you can aim for the middle, with a generous but not excessive level of spare capacity. In addition to optimizing for availability, cost, or a blend of both, you can also set a custom scaling threshold. In each case, AWS Auto Scaling will create scaling policies on your behalf, including appropriate upper and lower bounds for each resource.

AWS Auto Scaling in Action
I will use AWS Auto Scaling on a simple CloudFormation stack consisting of an Auto Scaling group of EC2 instances and a pair of DynamoDB tables. I start by removing the existing Scaling Policies from my Auto Scaling group:

Then I open up the new Auto Scaling Console and selecting the stack:

Behind the scenes, Elastic Beanstalk applications are always launched via a CloudFormation stack. In the screen shot above, awseb-e-sdwttqizbp-stack is an Elastic Beanstalk application that I launched.

I can click on any stack to learn more about it before proceeding:

I select the desired stack and click on Next to proceed. Then I enter a name for my scaling plan and choose the resources that I’d like it to include:

I choose the scaling strategy for each type of resource:

After I have selected the desired strategies, I click Next to proceed. Then I review the proposed scaling plan, and click Create scaling plan to move ahead:

The scaling plan is created and in effect within a few minutes:

I can click on the plan to learn more:

I can also inspect each scaling policy:

I tested my new policy by applying a load to the initial EC2 instance, and watched the scale out activity take place:

I also took a look at the CloudWatch metrics for the EC2 Auto Scaling group:

Available Now
We are launching AWS Auto Scaling today in the US East (Northern Virginia), US East (Ohio), US West (Oregon), EU (Ireland), and Asia Pacific (Singapore) Regions today, with more to follow. There’s no charge for AWS Auto Scaling; you pay only for the CloudWatch Alarms that it creates and any AWS resources that you consume.

As is often the case with our new services, this is just the first step on what we hope to be a long and interesting journey! We have a long roadmap, and we’ll be adding new features and options throughout 2018 in response to your feedback.

Jeff;

Analyzing the Linux boot process (opensource.com)

Post Syndicated from corbet original https://lwn.net/Articles/744528/rss

Alison Chaiken looks
in detail at how the kernel boots
on opensource.com.
Besides starting buggy spyware, what function does early boot
firmware serve? The job of a bootloader is to make available to a newly
powered processor the resources it needs to run a general-purpose operating
system like Linux. At power-on, there not only is no virtual memory, but no
DRAM until its controller is brought up.

Raspbery Pi-newood Derby

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/pinewood-derby/

Andre Miron’s Pinewood Derby Instant Replay System (sorry, not sorry for the pun in the title) uses a Raspberry Pi to monitor the finishing line and play back a slow-motion instant replay, putting an end to “No, I won!” squabbles once and for all.

Raspberry Pi Based Pinewood Derby Instant Replay Demo

This is the same system I demo in this video (https://youtu.be/-QyMxKfBaAE), but on our actual track with real pinewood derby cars. Glad to report that it works great!

Pinewood Derby

For those unfamiliar with the term, the Pinewood Derby is a racing event for Cub Scouts in the USA. Cub Scouts, often with the help of a guardian, build race cars out of wood according to rules regarding weight, size, materials, etc.

Pinewood derby race car

The Cubs then race their cars in heats, with the winners advancing to district and council races.

Who won?

Andre’s Instant Replay System registers the race cars as they cross the finishing line, and it plays back slow-motion video of the crossing on a monitor. As he explains on YouTube:

The Pi is recording a constant stream of video, and when the replay is triggered, it records another half-second of video, then takes the last second and a half and saves it in slow motion (recording is done at 90 fps), before replaying.

The build also uses an attached Arduino, connected to GPIO pin 5, to trigger the recording and playback as it registers the passing cars via a voltage splitter. Additionally, the system announces the finishing places on a rather attractive-looking display above the finishing line.

Pinewood derby race car Raspberry Pi

The result? No more debate about whose car crossed the line first in neck-and-neck races.

Build your own

Andre takes us through the physical setup of the build in the video below, and you’ll find the complete code pasted in the description of the video here. Thanks, Andre!

Raspberry Pi based Pinewood Derby Instant Replay System

See the system on our actual track here: https://youtu.be/B3lcQHWGq88 Raspberry Pi based instant replay system, triggered by Arduino Pinewood Derby Timer. The Pi uses GPIO pin 5 attached to a voltage splitter on Arduino output 11 (and ground-ground) to detect when a car crosses the finish line, which triggers the replay.

Digital making in your club

If you’re a member of an various after-school association such as the Scouts or Guides, then using the Raspberry Pi and our free project resources, or visiting a Code Club or CoderDojo, are excellent ways to work towards various badges and awards. So talk to your club leader to discover all the ways in which you can incorporate digital making into your club!

The post Raspbery Pi-newood Derby appeared first on Raspberry Pi.

Announcing our new beta for the AWS Certified Security – Specialty exam

Post Syndicated from Janna Pellegrino original https://aws.amazon.com/blogs/architecture/announcing-our-new-beta-for-the-aws-certified-security-specialty-exam/

Take the AWS Certified Security – Specialty beta exam for the chance to be among the first to hold this new AWS Certification. This beta exam allows experienced cloud security professionals to demonstrate and validate their expertise. Register today – this beta exam will only be available from January 15 to March 2!

About the exam

This beta exam validates that the successful candidate can effectively demonstrate knowledge of how to secure the AWS platform. The exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection.

The exam validates:

  • Familiarity with regional- and country-specific security and compliance regulations and meta issues that these regulations embody.
  • An understanding of specialized data classifications and AWS data protection mechanisms.
  • An understanding of data encryption methods and AWS mechanisms to implement them.
  • An understanding of secure Internet protocols and AWS mechanisms to implement them.
  • A working knowledge of AWS security services and features of services to provide a secure production environment.
  • Competency gained from two or more years of production deployment experience using AWS security services and features.
  • Ability to make tradeoff decisions with regard to cost, security, and deployment complexity given a set of application requirements.
  • An understanding of security operations and risk.

Learn more and register >>

Who is eligible

The beta is open to anyone who currently holds an Associate or Cloud Practitioner certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

How to prepare

We have training and other resources to help you prepare for the beta exam:

AWS Security Fundamentals Digital| 3 Hours
This course introduces you to fundamental cloud computing and AWS security concepts, including AWS access control and management, governance, logging, and encryption methods. It also covers security-related compliance protocols and risk management strategies, as well as procedures related to auditing your AWS security infrastructure.

Security Operations on AWS Classroom | 3 Days
This course demonstrates how to efficiently use AWS security services to stay secure and compliant in the AWS Cloud. The course focuses on the AWS-recommended security best practices that you can implement to enhance the security of your data and systems in the cloud. The course highlights the security features of AWS key services including compute, storage, networking, and database services.

Online resources for Cloud Security and Compliance

Review documentation, whitepapers, and articles & tutorials related to cloud security and compliance.

Learn more and register >>

Please contact us if you have questions about exam registration.

Good luck!

Continuous Deployment to Kubernetes using AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, Amazon ECR and AWS Lambda

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/devops/continuous-deployment-to-kubernetes-using-aws-codepipeline-aws-codecommit-aws-codebuild-amazon-ecr-and-aws-lambda/

Thank you to my colleague Omar Lari for this blog on how to create a continuous deployment pipeline for Kubernetes!


You can use Kubernetes and AWS together to create a fully managed, continuous deployment pipeline for container based applications. This approach takes advantage of Kubernetes’ open-source system to manage your containerized applications, and the AWS developer tools to manage your source code, builds, and pipelines.

This post describes how to create a continuous deployment architecture for containerized applications. It uses AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, and AWS Lambda to deploy containerized applications into a Kubernetes cluster. In this environment, developers can remain focused on developing code without worrying about how it will be deployed, and development managers can be satisfied that the latest changes are always deployed.

What is Continuous Deployment?

There are many articles, posts and even conferences dedicated to the practice of continuous deployment. For the purposes of this post, I will summarize continuous delivery into the following points:

  • Code is more frequently released into production environments
  • More frequent releases allow for smaller, incremental changes reducing risk and enabling simplified roll backs if needed
  • Deployment is automated and requires minimal user intervention

For a more information, see “Practicing Continuous Integration and Continuous Delivery on AWS”.

How can you use continuous deployment with AWS and Kubernetes?

You can leverage AWS services that support continuous deployment to automatically take your code from a source code repository to production in a Kubernetes cluster with minimal user intervention. To do this, you can create a pipeline that will build and deploy committed code changes as long as they meet the requirements of each stage of the pipeline.

To create the pipeline, you will use the following services:

  • AWS CodePipeline. AWS CodePipeline is a continuous delivery service that models, visualizes, and automates the steps required to release software. You define stages in a pipeline to retrieve code from a source code repository, build that source code into a releasable artifact, test the artifact, and deploy it to production. Only code that successfully passes through all these stages will be deployed. In addition, you can optionally add other requirements to your pipeline, such as manual approvals, to help ensure that only approved changes are deployed to production.
  • AWS CodeCommit. AWS CodeCommit is a secure, scalable, and managed source control service that hosts private Git repositories. You can privately store and manage assets such as your source code in the cloud and configure your pipeline to automatically retrieve and process changes committed to your repository.
  • AWS CodeBuild. AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces artifacts that are ready to deploy. You can use AWS CodeBuild to both build your artifacts, and to test those artifacts before they are deployed.
  • AWS Lambda. AWS Lambda is a compute service that lets you run code without provisioning or managing servers. You can invoke a Lambda function in your pipeline to prepare the built and tested artifact for deployment by Kubernetes to the Kubernetes cluster.
  • Kubernetes. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It provides a platform for running, deploying, and managing containers at scale.

An Example of Continuous Deployment to Kubernetes:

The following example illustrates leveraging AWS developer tools to continuously deploy to a Kubernetes cluster:

  1. Developers commit code to an AWS CodeCommit repository and create pull requests to review proposed changes to the production code. When the pull request is merged into the master branch in the AWS CodeCommit repository, AWS CodePipeline automatically detects the changes to the branch and starts processing the code changes through the pipeline.
  2. AWS CodeBuild packages the code changes as well as any dependencies and builds a Docker image. Optionally, another pipeline stage tests the code and the package, also using AWS CodeBuild.
  3. The Docker image is pushed to Amazon ECR after a successful build and/or test stage.
  4. AWS CodePipeline invokes an AWS Lambda function that includes the Kubernetes Python client as part of the function’s resources. The Lambda function performs a string replacement on the tag used for the Docker image in the Kubernetes deployment file to match the Docker image tag applied in the build, one that matches the image in Amazon ECR.
  5. After the deployment manifest update is completed, AWS Lambda invokes the Kubernetes API to update the image in the Kubernetes application deployment.
  6. Kubernetes performs a rolling update of the pods in the application deployment to match the docker image specified in Amazon ECR.
    The pipeline is now live and responds to changes to the master branch of the CodeCommit repository. This pipeline is also fully extensible, you can add steps for performing testing or adding a step to deploy into a staging environment before the code ships into the production cluster.

An example pipeline in AWS CodePipeline that supports this architecture can be seen below:

Conclusion

We are excited to see how you leverage this pipeline to help ease your developer experience as you develop applications in Kubernetes.

You’ll find an AWS CloudFormation template with everything necessary to spin up your own continuous deployment pipeline at the CodeSuite – Continuous Deployment Reference Architecture for Kubernetes repo on GitHub. The repository details exactly how the pipeline is provisioned and how you can use it to deploy your own applications. If you have any questions, feedback, or suggestions, please let us know!

Validate Your IT Security Expertise with the New AWS Certified Security – Specialty Beta Exam

Post Syndicated from Sara Snedeker original https://aws.amazon.com/blogs/security/validate-your-it-security-expertise-with-the-new-aws-certified-security-specialty-beta-exam/

AWS Training and Certification image

If you are an experienced cloud security professional, you can demonstrate and validate your expertise with the new AWS Certified Security – Specialty beta exam. This exam allows you to demonstrate your knowledge of incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. Register today – this beta exam will be available only from January 15 to March 2, 2018.

By taking this exam, you can validate your:

  • Familiarity with region-specific and country-specific security and compliance regulations and meta issues that these regulations include.
  • Understanding of data encryption methods and secure internet protocols, and the AWS mechanisms to implement them.
  • Working knowledge of AWS security services to provide a secure production environment.
  • Ability to make trade-off decisions with regard to cost, security, and deployment complexity when given a set of application requirements.

See the full list of security knowledge you can validate by taking this beta exam.

Who is eligible?

The beta exam is open to anyone who currently holds an AWS Associate or Cloud Practitioner certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

How to prepare

You can take the following courses and use AWS cloud security resources and compliance resources to prepare for this exam.

AWS Security Fundamentals (digital, 3 hours)
This digital course introduces you to fundamental cloud computing and AWS security concepts, including AWS access control and management, governance, logging, and encryption methods. It also covers security-related compliance protocols and risk management strategies, as well as procedures related to auditing your AWS security infrastructure.

Security Operations on AWS (classroom, 3 days)
This instructor-led course demonstrates how to efficiently use AWS security services to help stay secure and compliant in the AWS Cloud. The course focuses on the AWS-recommended security best practices that you can implement to enhance the security of your AWS resources. The course highlights the security features of AWS compute, storage, networking, and database services.

If you have questions about this new beta exam, contact us.

Good luck with the exam!

– Sara

Create SLUG! It’s just like Snake, but with a slug

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/slug-snake/

Recreate Snake, the favourite mobile phone game from the late nineties, using a slug*, a Raspberry Pi, a Sense HAT, and our free resource!

Raspberry Pi Sense HAT Slug free resource

*A virtual slug. Not a real slug. Please leave the real slugs out in nature.

Snake SLUG!

Move aside, Angry Birds! On your bike, Pokémon Go! When it comes to the cream of the crop of mobile phone games, Snake holds the top spot.

Snake Nokia Game

I could while away the hours…

You may still have an old Nokia 3310 lost in the depths of a drawer somewhere — the drawer that won’t open all the way because something inside is jammed at an odd angle. So it will be far easier to grab your Pi and Sense HAT, or use the free Sense HAT emulator (online or on Raspbian), and code Snake SLUG yourself. In doing so, you can introduce the smaller residents of your household to the best reptile-focused game ever made…now with added mollusc.

The resource

To try out the game for yourself, head to our resource page, where you’ll find the online Sense HAT emulator embedded and ready to roll.

Raspberry Pi Sense HAT Slug free resource

It’ll look just like this, and you can use your computer’s arrow keys to direct your slug toward her tasty treats.

From there, you’ll be taken on a step-by-step journey from zero to SLUG glory while coding your own versionof the game in Python. On the way, you’ll learn to work with two-dimensional lists and to use the Sense HAT’s pixel display and joystick input. And by completing the resource, you’ll expand your understanding of applying abstraction and decomposition to solve more complex problems, in line with our Digital Making Curriculum.

The Sense HAT

The Raspberry Pi Sense HAT was originally designed and made as part of the Astro Pi mission in December 2015. With an 8×8 RGB LED matrix, a joystick, and a plethora of on-board sensors including an accelerometer, gyroscope, and magnetometer, it’s a great add-on for your digital making toolkit, and excellent for projects involving data collection and evaluation.

You can find more of our free Sense HAT tutorials here, including for making Flappy Bird Astronaut, a marble maze, and Pong.

The post Create SLUG! It’s just like Snake, but with a slug appeared first on Raspberry Pi.

AWS IoT, Greengrass, and Machine Learning for Connected Vehicles at CES

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-greengrass-and-machine-learning-for-connected-vehicles-at-ces/

Last week I attended a talk given by Bryan Mistele, president of Seattle-based INRIX. Bryan’s talk provided a glimpse into the future of transportation, centering around four principle attributes, often abbreviated as ACES:

Autonomous – Cars and trucks are gaining the ability to scan and to make sense of their environments and to navigate without human input.

Connected – Vehicles of all types have the ability to take advantage of bidirectional connections (either full-time or intermittent) to other cars and to cloud-based resources. They can upload road and performance data, communicate with each other to run in packs, and take advantage of traffic and weather data.

Electric – Continued development of battery and motor technology, will make electrics vehicles more convenient, cost-effective, and environmentally friendly.

Shared – Ride-sharing services will change usage from an ownership model to an as-a-service model (sound familiar?).

Individually and in combination, these emerging attributes mean that the cars and trucks we will see and use in the decade to come will be markedly different than those of the past.

On the Road with AWS
AWS customers are already using our AWS IoT, edge computing, Amazon Machine Learning, and Alexa products to bring this future to life – vehicle manufacturers, their tier 1 suppliers, and AutoTech startups all use AWS for their ACES initiatives. AWS Greengrass is playing an important role here, attracting design wins and helping our customers to add processing power and machine learning inferencing at the edge.

AWS customer Aptiv (formerly Delphi) talked about their Automated Mobility on Demand (AMoD) smart vehicle architecture in a AWS re:Invent session. Aptiv’s AMoD platform will use Greengrass and microservices to drive the onboard user experience, along with edge processing, monitoring, and control. Here’s an overview:

Another customer, Denso of Japan (one of the world’s largest suppliers of auto components and software) is using Greengrass and AWS IoT to support their vision of Mobility as a Service (MaaS). Here’s a video:

AWS at CES
The AWS team will be out in force at CES in Las Vegas and would love to talk to you. They’ll be running demos that show how AWS can help to bring innovation and personalization to connected and autonomous vehicles.

Personalized In-Vehicle Experience – This demo shows how AWS AI and Machine Learning can be used to create a highly personalized and branded in-vehicle experience. It makes use of Amazon Lex, Polly, and Amazon Rekognition, but the design is flexible and can be used with other services as well. The demo encompasses driver registration, login and startup (including facial recognition), voice assistance for contextual guidance, personalized e-commerce, and vehicle control. Here’s the architecture for the voice assistance:

Connected Vehicle Solution – This demo shows how a connected vehicle can combine local and cloud intelligence, using edge computing and machine learning at the edge. It handles intermittent connections and uses AWS DeepLens to train a model that responds to distracted drivers. Here’s the overall architecture, as described in our Connected Vehicle Solution:

Digital Content Delivery – This demo will show how a customer uses a web-based 3D configurator to build and personalize their vehicle. It will also show high resolution (4K) 3D image and an optional immersive AR/VR experience, both designed for use within a dealership.

Autonomous Driving – This demo will showcase the AWS services that can be used to build autonomous vehicles. There’s a 1/16th scale model vehicle powered and driven by Greengrass and an overview of a new AWS Autonomous Toolkit. As part of the demo, attendees drive the car, training a model via Amazon SageMaker for subsequent on-board inferencing, powered by Greengrass ML Inferencing.

To speak to one of my colleagues or to set up a time to see the demos, check out the Visit AWS at CES 2018 page.

Some Resources
If you are interested in this topic and want to learn more, the AWS for Automotive page is a great starting point, with discussions on connected vehicles & mobility, autonomous vehicle development, and digital customer engagement.

When you are ready to start building a connected vehicle, the AWS Connected Vehicle Solution contains a reference architecture that combines local computing, sophisticated event rules, and cloud-based data processing and storage. You can use this solution to accelerate your own connected vehicle projects.

Jeff;

RuTracker Reveals Innovative Plan For Users to Subvert ISP Blocking

Post Syndicated from Andy original https://torrentfreak.com/rutracker-reveals-innovative-plan-for-users-to-subvert-isp-blocking-180110/

As Russia’s largest torrent site and one that earned itself a mention in TF’s list of most popular torrent sites 2018, RuTracker is continuously under fire.

The site has an extremely dedicated following but Russia’s telecoms watchdog, spurred on by copyright holders brandishing court rulings, does everything in its power to ensure that people can’t access the site easily.

As a result, RuTracker’s main domains are blocked by all ISPs, meaning that people have to resort to VPNs or the many dozens of proxy and mirror sites that have been set up to facilitate access to the popular tracker.

While all of these methods used to work just fine, new legislation that came into force during October means that mirror and proxy sites can be added to block lists without copyright holders having to return to court. And, following legislation introduced in November, local VPN services are forbidden from providing access to blocked sites.

While RuTracker has always insisted that web blockades have little effect on the numbers of people sharing content, direct traffic to their main domains has definitely suffered. To solve this problem and go some way towards mitigating VPN and proxy bans, the site has just come up with a new plan to keep the torrents flowing.

The scheme was quietly announced, not on RuTracker’s main forum, but to a smaller set of users on local site Leprosorium. The idea was that a quieter launch there would allow for controlled testing before a release to the masses. The project is called My.RuTracker and here’s how it works.

Instead of blocked users fruitlessly trying to find public circumvention methods that once seen are immediately blocked, they are invited to register their own domains. These can be single use, for the person who registers them, but it’s envisioned that they’ll be shared out between friends, family, and online groups, to better make use of the resource.

Once domains are registered, users are invited to contact a special user account on the RuTracker site (operated by the site’s operators) which will provide them with precise technical details on how to set up their domain (.ru domains are not allowed) to gain access to RuTracker.

“In response, after a while (usually every other day), a list of NS-addresses will be sent to the registrar’s domain settings. Under this scheme, the user domain will be redirected to the RuTracker site via a dynamic IP address: this will avoid blocking the torrent tracker for a particular IP address,” the scheme envisages.

According to local news resource Tjournal, 62 personal mirrors were launched following the initial appeal, with the operators of RuTracker now planning to publicly announce the project to their community. As more are added, the site will keep track of traffic from each of the personal “mirrors” for balancing the load on the site.

At least in theory, this seems like a pretty innovative scheme. Currently, the authorities rely on the scale and public awareness of a particular proxy or mirror in order to earmark it for blocking. This much more decentralized plan, in which only small numbers of people should know each domain, seems like a much more robust system – at least until the authorities and indeed the law catches up.

And so the cat-and-mouse game continues.

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

Tech Companies Meet EC to Discuss Removal of Pirate & Illegal Content

Post Syndicated from Andy original https://torrentfreak.com/tech-companies-meet-ec-to-discuss-removal-of-pirate-illegal-content-180109/

Thousands perhaps millions of pieces of illegal content flood onto the Internet every single day, a problem that’s only increasing with each passing year.

In the early days of the Internet very little was done to combat the problem but with the rise of social media and millions of citizens using it to publish whatever they like – not least terrorist propaganda and racist speech – governments around the world are beginning to take notice.

Of course, running parallel is the multi-billion dollar issue of intellectual property infringement. Eighteen years on from the first wave of mass online piracy and the majority of popular movies, TV shows, games, software and books are still available to download.

Over the past couple of years and increasingly in recent months, there have been clear signs that the EU in particular wishes to collectively mitigate the spread of all illegal content – from ISIS videos to pirated Hollywood movies – with assistance from major tech companies.

Google, YouTube, Facebook and Twitter are all expected to do their part, with the looming stick of legislation behind the collaborative carrots, should they fail to come up with a solution.

To that end, five EU Commissioners – Dimitris Avramopoulos, Elżbieta Bieńkowska, Věra Jourová, Julian King and Mariya Gabriel – will meet today in Brussels with representatives of several online platforms to discuss progress made in dealing with the spread of the aforementioned material.

In a joint statement together with EC Vice-President Andrus Ansip, the Commissioners describe all illegal content as a threat to security, safety, and fundamental rights, demanding a “collective response – from all actors, including the internet industry.”

They note that online platforms have committed significant resources towards removing violent and extremist content, including via automated removal, but more needs to be done to tackle the issue.

“This is starting to achieve results. However, even if tens of thousands of pieces of illegal content have been taken down, there are still hundreds of thousands more out there,” the Commissioners writes.

“And removal needs to be speedy: the longer illegal material stays online, the greater its reach, the more it can spread and grow. Building on the current voluntary approach, more efforts and progress have to be made.”

The Commission says it is relying on online platforms such as Google and Facebook to “step up and speed up their efforts to tackle these threats quickly and comprehensively.” This should include closer cooperation with law enforcement, sharing of information with other online players, plus action to ensure that once taken down, illegal content does not simply reappear.

While it’s clear that that the EC would prefer to work collaboratively with the platforms to find a solution to the illegal content problem, as expected there’s the veiled threat of them being compelled by law to do so, should they fall short of their responsibilities.

“We will continue to promote cooperation with social media companies to detect and remove terrorist and other illegal content online, and if necessary, propose legislation to complement the existing regulatory framework,” the EC warns.

Today’s discussions run both in parallel and in tandem with others specifically targeted at intellectual property abuses. Late November the EC presented a set of new measures to ensure that copyright holders are well protected both online and in the physical realm.

A key aim is to focus on large-scale facilitators, such as pirate site operators, while cutting their revenue streams.

“The Commission seeks to deprive commercial-scale IP infringers of the revenue flows that make their criminal activity lucrative – this is the so-called ‘follow the money’ approach which focuses on the ‘big fish’ rather than individuals,” the Commission explained.

This presentation followed on the heels of a proposal last September which had the EC advocating the take-down-stay-down principle, with pirate content being taken down, automated filters ensuring infringement can be tackled proactively, with measures being taken against repeat infringers.

Again, the EC warned that should cooperation with Internet platforms fail to come up with results, future legislation cannot be ruled out.

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

Combine Transactional and Analytical Data Using Amazon Aurora and Amazon Redshift

Post Syndicated from Re Alvarez-Parmar original https://aws.amazon.com/blogs/big-data/combine-transactional-and-analytical-data-using-amazon-aurora-and-amazon-redshift/

A few months ago, we published a blog post about capturing data changes in an Amazon Aurora database and sending it to Amazon Athena and Amazon QuickSight for fast analysis and visualization. In this post, I want to demonstrate how easy it can be to take the data in Aurora and combine it with data in Amazon Redshift using Amazon Redshift Spectrum.

With Amazon Redshift, you can build petabyte-scale data warehouses that unify data from a variety of internal and external sources. Because Amazon Redshift is optimized for complex queries (often involving multiple joins) across large tables, it can handle large volumes of retail, inventory, and financial data without breaking a sweat.

In this post, we describe how to combine data in Aurora in Amazon Redshift. Here’s an overview of the solution:

  • Use AWS Lambda functions with Amazon Aurora to capture data changes in a table.
  • Save data in an Amazon S3
  • Query data using Amazon Redshift Spectrum.

We use the following services:

Serverless architecture for capturing and analyzing Aurora data changes

Consider a scenario in which an e-commerce web application uses Amazon Aurora for a transactional database layer. The company has a sales table that captures every single sale, along with a few corresponding data items. This information is stored as immutable data in a table. Business users want to monitor the sales data and then analyze and visualize it.

In this example, you take the changes in data in an Aurora database table and save it in Amazon S3. After the data is captured in Amazon S3, you combine it with data in your existing Amazon Redshift cluster for analysis.

By the end of this post, you will understand how to capture data events in an Aurora table and push them out to other AWS services using AWS Lambda.

The following diagram shows the flow of data as it occurs in this tutorial:

The starting point in this architecture is a database insert operation in Amazon Aurora. When the insert statement is executed, a custom trigger calls a Lambda function and forwards the inserted data. Lambda writes the data that it received from Amazon Aurora to a Kinesis data delivery stream. Kinesis Data Firehose writes the data to an Amazon S3 bucket. Once the data is in an Amazon S3 bucket, it is queried in place using Amazon Redshift Spectrum.

Creating an Aurora database

First, create a database by following these steps in the Amazon RDS console:

  1. Sign in to the AWS Management Console, and open the Amazon RDS console.
  2. Choose Launch a DB instance, and choose Next.
  3. For Engine, choose Amazon Aurora.
  4. Choose a DB instance class. This example uses a small, since this is not a production database.
  5. In Multi-AZ deployment, choose No.
  6. Configure DB instance identifier, Master username, and Master password.
  7. Launch the DB instance.

After you create the database, use MySQL Workbench to connect to the database using the CNAME from the console. For information about connecting to an Aurora database, see Connecting to an Amazon Aurora DB Cluster.

The following screenshot shows the MySQL Workbench configuration:

Next, create a table in the database by running the following SQL statement:

Create Table
CREATE TABLE Sales (
InvoiceID int NOT NULL AUTO_INCREMENT,
ItemID int NOT NULL,
Category varchar(255),
Price double(10,2), 
Quantity int not NULL,
OrderDate timestamp,
DestinationState varchar(2),
ShippingType varchar(255),
Referral varchar(255),
PRIMARY KEY (InvoiceID)
)

You can now populate the table with some sample data. To generate sample data in your table, copy and run the following script. Ensure that the highlighted (bold) variables are replaced with appropriate values.

#!/usr/bin/python
import MySQLdb
import random
import datetime

db = MySQLdb.connect(host="AURORA_CNAME",
                     user="DBUSER",
                     passwd="DBPASSWORD",
                     db="DB")

states = ("AL","AK","AZ","AR","CA","CO","CT","DE","FL","GA","HI","ID","IL","IN",
"IA","KS","KY","LA","ME","MD","MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ",
"NM","NY","NC","ND","OH","OK","OR","PA","RI","SC","SD","TN","TX","UT","VT","VA",
"WA","WV","WI","WY")

shipping_types = ("Free", "3-Day", "2-Day")

product_categories = ("Garden", "Kitchen", "Office", "Household")
referrals = ("Other", "Friend/Colleague", "Repeat Customer", "Online Ad")

for i in range(0,10):
    item_id = random.randint(1,100)
    state = states[random.randint(0,len(states)-1)]
    shipping_type = shipping_types[random.randint(0,len(shipping_types)-1)]
    product_category = product_categories[random.randint(0,len(product_categories)-1)]
    quantity = random.randint(1,4)
    referral = referrals[random.randint(0,len(referrals)-1)]
    price = random.randint(1,100)
    order_date = datetime.date(2016,random.randint(1,12),random.randint(1,30)).isoformat()

    data_order = (item_id, product_category, price, quantity, order_date, state,
    shipping_type, referral)

    add_order = ("INSERT INTO Sales "
                   "(ItemID, Category, Price, Quantity, OrderDate, DestinationState, \
                   ShippingType, Referral) "
                   "VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")

    cursor = db.cursor()
    cursor.execute(add_order, data_order)

    db.commit()

cursor.close()
db.close() 

The following screenshot shows how the table appears with the sample data:

Sending data from Amazon Aurora to Amazon S3

There are two methods available to send data from Amazon Aurora to Amazon S3:

  • Using a Lambda function
  • Using SELECT INTO OUTFILE S3

To demonstrate the ease of setting up integration between multiple AWS services, we use a Lambda function to send data to Amazon S3 using Amazon Kinesis Data Firehose.

Alternatively, you can use a SELECT INTO OUTFILE S3 statement to query data from an Amazon Aurora DB cluster and save it directly in text files that are stored in an Amazon S3 bucket. However, with this method, there is a delay between the time that the database transaction occurs and the time that the data is exported to Amazon S3 because the default file size threshold is 6 GB.

Creating a Kinesis data delivery stream

The next step is to create a Kinesis data delivery stream, since it’s a dependency of the Lambda function.

To create a delivery stream:

  1. Open the Kinesis Data Firehose console
  2. Choose Create delivery stream.
  3. For Delivery stream name, type AuroraChangesToS3.
  4. For Source, choose Direct PUT.
  5. For Record transformation, choose Disabled.
  6. For Destination, choose Amazon S3.
  7. In the S3 bucket drop-down list, choose an existing bucket, or create a new one.
  8. Enter a prefix if needed, and choose Next.
  9. For Data compression, choose GZIP.
  10. In IAM role, choose either an existing role that has access to write to Amazon S3, or choose to generate one automatically. Choose Next.
  11. Review all the details on the screen, and choose Create delivery stream when you’re finished.

 

Creating a Lambda function

Now you can create a Lambda function that is called every time there is a change that needs to be tracked in the database table. This Lambda function passes the data to the Kinesis data delivery stream that you created earlier.

To create the Lambda function:

  1. Open the AWS Lambda console.
  2. Ensure that you are in the AWS Region where your Amazon Aurora database is located.
  3. If you have no Lambda functions yet, choose Get started now. Otherwise, choose Create function.
  4. Choose Author from scratch.
  5. Give your function a name and select Python 3.6 for Runtime
  6. Choose and existing or create a new Role, the role would need to have access to call firehose:PutRecord
  7. Choose Next on the trigger selection screen.
  8. Paste the following code in the code window. Change the stream_name variable to the Kinesis data delivery stream that you created in the previous step.
  9. Choose File -> Save in the code editor and then choose Save.
import boto3
import json

firehose = boto3.client('firehose')
stream_name = ‘AuroraChangesToS3’


def Kinesis_publish_message(event, context):
    
    firehose_data = (("%s,%s,%s,%s,%s,%s,%s,%s\n") %(event['ItemID'], 
    event['Category'], event['Price'], event['Quantity'],
    event['OrderDate'], event['DestinationState'], event['ShippingType'], 
    event['Referral']))
    
    firehose_data = {'Data': str(firehose_data)}
    print(firehose_data)
    
    firehose.put_record(DeliveryStreamName=stream_name,
    Record=firehose_data)

Note the Amazon Resource Name (ARN) of this Lambda function.

Giving Aurora permissions to invoke a Lambda function

To give Amazon Aurora permissions to invoke a Lambda function, you must attach an IAM role with appropriate permissions to the cluster. For more information, see Invoking a Lambda Function from an Amazon Aurora DB Cluster.

Once you are finished, the Amazon Aurora database has access to invoke a Lambda function.

Creating a stored procedure and a trigger in Amazon Aurora

Now, go back to MySQL Workbench, and run the following command to create a new stored procedure. When this stored procedure is called, it invokes the Lambda function you created. Change the ARN in the following code to your Lambda function’s ARN.

DROP PROCEDURE IF EXISTS CDC_TO_FIREHOSE;
DELIMITER ;;
CREATE PROCEDURE CDC_TO_FIREHOSE (IN ItemID VARCHAR(255), 
									IN Category varchar(255), 
									IN Price double(10,2),
                                    IN Quantity int(11),
                                    IN OrderDate timestamp,
                                    IN DestinationState varchar(2),
                                    IN ShippingType varchar(255),
                                    IN Referral  varchar(255)) LANGUAGE SQL 
BEGIN
  CALL mysql.lambda_async('arn:aws:lambda:us-east-1:XXXXXXXXXXXXX:function:CDCFromAuroraToKinesis', 
     CONCAT('{ "ItemID" : "', ItemID, 
            '", "Category" : "', Category,
            '", "Price" : "', Price,
            '", "Quantity" : "', Quantity, 
            '", "OrderDate" : "', OrderDate, 
            '", "DestinationState" : "', DestinationState, 
            '", "ShippingType" : "', ShippingType, 
            '", "Referral" : "', Referral, '"}')
     );
END
;;
DELIMITER ;

Create a trigger TR_Sales_CDC on the Sales table. When a new record is inserted, this trigger calls the CDC_TO_FIREHOSE stored procedure.

DROP TRIGGER IF EXISTS TR_Sales_CDC;
 
DELIMITER ;;
CREATE TRIGGER TR_Sales_CDC
  AFTER INSERT ON Sales
  FOR EACH ROW
BEGIN
  SELECT  NEW.ItemID , NEW.Category, New.Price, New.Quantity, New.OrderDate
  , New.DestinationState, New.ShippingType, New.Referral
  INTO @ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral;
  CALL  CDC_TO_FIREHOSE(@ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral);
END
;;
DELIMITER ;

If a new row is inserted in the Sales table, the Lambda function that is mentioned in the stored procedure is invoked.

Verify that data is being sent from the Lambda function to Kinesis Data Firehose to Amazon S3 successfully. You might have to insert a few records, depending on the size of your data, before new records appear in Amazon S3. This is due to Kinesis Data Firehose buffering. To learn more about Kinesis Data Firehose buffering, see the “Amazon S3” section in Amazon Kinesis Data Firehose Data Delivery.

Every time a new record is inserted in the sales table, a stored procedure is called, and it updates data in Amazon S3.

Querying data in Amazon Redshift

In this section, you use the data you produced from Amazon Aurora and consume it as-is in Amazon Redshift. In order to allow you to process your data as-is, where it is, while taking advantage of the power and flexibility of Amazon Redshift, you use Amazon Redshift Spectrum. You can use Redshift Spectrum to run complex queries on data stored in Amazon S3, with no need for loading or other data prep.

Just create a data source and issue your queries to your Amazon Redshift cluster as usual. Behind the scenes, Redshift Spectrum scales to thousands of instances on a per-query basis, ensuring that you get fast, consistent performance even as your dataset grows to beyond an exabyte! Being able to query data that is stored in Amazon S3 means that you can scale your compute and your storage independently. You have the full power of the Amazon Redshift query model and all the reporting and business intelligence tools at your disposal. Your queries can reference any combination of data stored in Amazon Redshift tables and in Amazon S3.

Redshift Spectrum supports open, common data types, including CSV/TSV, Apache Parquet, SequenceFile, and RCFile. Files can be compressed using gzip or Snappy, with other data types and compression methods in the works.

First, create an Amazon Redshift cluster. Follow the steps in Launch a Sample Amazon Redshift Cluster.

Next, create an IAM role that has access to Amazon S3 and Athena. By default, Amazon Redshift Spectrum uses the Amazon Athena data catalog. Your cluster needs authorization to access your external data catalog in AWS Glue or Athena and your data files in Amazon S3.

In the demo setup, I attached AmazonS3FullAccess and AmazonAthenaFullAccess. In a production environment, the IAM roles should follow the standard security of granting least privilege. For more information, see IAM Policies for Amazon Redshift Spectrum.

Attach the newly created role to the Amazon Redshift cluster. For more information, see Associate the IAM Role with Your Cluster.

Next, connect to the Amazon Redshift cluster, and create an external schema and database:

create external schema if not exists spectrum_schema
from data catalog 
database 'spectrum_db' 
region 'us-east-1'
IAM_ROLE 'arn:aws:iam::XXXXXXXXXXXX:role/RedshiftSpectrumRole'
create external database if not exists;

Don’t forget to replace the IAM role in the statement.

Then create an external table within the database:

 CREATE EXTERNAL TABLE IF NOT EXISTS spectrum_schema.ecommerce_sales(
  ItemID int,
  Category varchar,
  Price DOUBLE PRECISION,
  Quantity int,
  OrderDate TIMESTAMP,
  DestinationState varchar,
  ShippingType varchar,
  Referral varchar)
ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
LOCATION 's3://{BUCKET_NAME}/CDC/'

Query the table, and it should contain data. This is a fact table.

select top 10 * from spectrum_schema.ecommerce_sales

 

Next, create a dimension table. For this example, we create a date/time dimension table. Create the table:

CREATE TABLE date_dimension (
  d_datekey           integer       not null sortkey,
  d_dayofmonth        integer       not null,
  d_monthnum          integer       not null,
  d_dayofweek                varchar(10)   not null,
  d_prettydate        date       not null,
  d_quarter           integer       not null,
  d_half              integer       not null,
  d_year              integer       not null,
  d_season            varchar(10)   not null,
  d_fiscalyear        integer       not null)
diststyle all;

Populate the table with data:

copy date_dimension from 's3://reparmar-lab/2016dates' 
iam_role 'arn:aws:iam::XXXXXXXXXXXX:role/redshiftspectrum'
DELIMITER ','
dateformat 'auto';

The date dimension table should look like the following:

Querying data in local and external tables using Amazon Redshift

Now that you have the fact and dimension table populated with data, you can combine the two and run analysis. For example, if you want to query the total sales amount by weekday, you can run the following:

select sum(quantity*price) as total_sales, date_dimension.d_season
from spectrum_schema.ecommerce_sales 
join date_dimension on spectrum_schema.ecommerce_sales.orderdate = date_dimension.d_prettydate 
group by date_dimension.d_season

You get the following results:

Similarly, you can replace d_season with d_dayofweek to get sales figures by weekday:

With Amazon Redshift Spectrum, you pay only for the queries you run against the data that you actually scan. We encourage you to use file partitioning, columnar data formats, and data compression to significantly minimize the amount of data scanned in Amazon S3. This is important for data warehousing because it dramatically improves query performance and reduces cost.

Partitioning your data in Amazon S3 by date, time, or any other custom keys enables Amazon Redshift Spectrum to dynamically prune nonrelevant partitions to minimize the amount of data processed. If you store data in a columnar format, such as Parquet, Amazon Redshift Spectrum scans only the columns needed by your query, rather than processing entire rows. Similarly, if you compress your data using one of the supported compression algorithms in Amazon Redshift Spectrum, less data is scanned.

Analyzing and visualizing Amazon Redshift data in Amazon QuickSight

Modify the Amazon Redshift security group to allow an Amazon QuickSight connection. For more information, see Authorizing Connections from Amazon QuickSight to Amazon Redshift Clusters.

After modifying the Amazon Redshift security group, go to Amazon QuickSight. Create a new analysis, and choose Amazon Redshift as the data source.

Enter the database connection details, validate the connection, and create the data source.

Choose the schema to be analyzed. In this case, choose spectrum_schema, and then choose the ecommerce_sales table.

Next, we add a custom field for Total Sales = Price*Quantity. In the drop-down list for the ecommerce_sales table, choose Edit analysis data sets.

On the next screen, choose Edit.

In the data prep screen, choose New Field. Add a new calculated field Total Sales $, which is the product of the Price*Quantity fields. Then choose Create. Save and visualize it.

Next, to visualize total sales figures by month, create a graph with Total Sales on the x-axis and Order Data formatted as month on the y-axis.

After you’ve finished, you can use Amazon QuickSight to add different columns from your Amazon Redshift tables and perform different types of visualizations. You can build operational dashboards that continuously monitor your transactional and analytical data. You can publish these dashboards and share them with others.

Final notes

Amazon QuickSight can also read data in Amazon S3 directly. However, with the method demonstrated in this post, you have the option to manipulate, filter, and combine data from multiple sources or Amazon Redshift tables before visualizing it in Amazon QuickSight.

In this example, we dealt with data being inserted, but triggers can be activated in response to an INSERT, UPDATE, or DELETE trigger.

Keep the following in mind:

  • Be careful when invoking a Lambda function from triggers on tables that experience high write traffic. This would result in a large number of calls to your Lambda function. Although calls to the lambda_async procedure are asynchronous, triggers are synchronous.
  • A statement that results in a large number of trigger activations does not wait for the call to the AWS Lambda function to complete. But it does wait for the triggers to complete before returning control to the client.
  • Similarly, you must account for Amazon Kinesis Data Firehose limits. By default, Kinesis Data Firehose is limited to a maximum of 5,000 records/second. For more information, see Monitoring Amazon Kinesis Data Firehose.

In certain cases, it may be optimal to use AWS Database Migration Service (AWS DMS) to capture data changes in Aurora and use Amazon S3 as a target. For example, AWS DMS might be a good option if you don’t need to transform data from Amazon Aurora. The method used in this post gives you the flexibility to transform data from Aurora using Lambda before sending it to Amazon S3. Additionally, the architecture has the benefits of being serverless, whereas AWS DMS requires an Amazon EC2 instance for replication.

For design considerations while using Redshift Spectrum, see Using Amazon Redshift Spectrum to Query External Data.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Capturing Data Changes in Amazon Aurora Using AWS Lambda and 10 Best Practices for Amazon Redshift Spectrum


About the Authors

Re Alvarez-Parmar is a solutions architect for Amazon Web Services. He helps enterprises achieve success through technical guidance and thought leadership. In his spare time, he enjoys spending time with his two kids and exploring outdoors.

 

 

 

Backblaze Cloud Backup Release 5.2

Post Syndicated from Yev original https://www.backblaze.com/blog/backblaze-cloud-backup-release-5-2/

We’re pleased to start the year off the right way, with an update to Backblaze Cloud Backup, version 5.2! This is a smaller release, but does increase backup speeds, optimizes the backup client, and addresses a few minor bugs that we’re excited to lay to rest.

What’s New

  • Increased transmission speed of files between 30MB and 400MB+.
  • Optimized indexing to decrease system resource usage and lower the performance impact on computers that are backing up to Backblaze.
  • Adjusted external hard drive monitoring and increased the speed of indexing.
  • Changed copyright to 2018.

Release Version Number:

  • Mac — 5.2.0
  • PC — 5.2.0

Clients:
Backblaze Personal Backup
Backblaze Business Backup

Availability:
January 4, 2018

Upgrade Methods:

  • Immediately as a download from: files.backblaze.com
  • Rolling out soon when performing a “Check for Updates” (right-click on the Backblaze icon and then select “Check for Updates”).
  • Rolling out soon as a download from: https://secure.backblaze.com/update.htm.
  • Rolling out soon as the default download from: www.backblaze.com.
  • Auto-update will begin in a couple of weeks.

Cost:
This is a free update for all Backblaze Cloud Backup consumer and business customers and active trial users.

The post Backblaze Cloud Backup Release 5.2 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

[$] Future directions for PGP

Post Syndicated from jake original https://lwn.net/Articles/742542/rss

Back in October, LWN reported on a talk
about the
state of the GNU Privacy Guard (GnuPG)
project, an asymmetric public-key encryption and
signing tool that had been almost abandoned by its lead developer due to lack
of resources before receiving a significant infusion of funding and community
attention. GnuPG 2 has brought about a number of changes and
improvements but,
at the same time, several efforts are underway to significantly change the way
GnuPG and OpenPGP are used. This article will look at the current
state of GnuPG and the OpenPGP web of trust, as compared to new implementations
of the OpenPGP standard and other trust systems.

A hedgehog cam or two

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/a-hedgehog-cam-or-two/

Here we are, hauling ourselves out of the Christmas and New Year holidays and into January proper. It’s dawning on me that I have to go back to work, even though it’s still very cold and gloomy in northern Europe, and even though my duvet is lovely and warm. I found myself envying beings that hibernate, and thinking about beings that hibernate, and searching for things to do with hedgehogs. And, well, the long and the short of it is, today’s blog post is a short meditation on the hedgehog cam.

A hedgehog in a garden, photographed in infrared light by a hedgehog cam

Success! It’s a hedgehog!
Photo by Andrew Wedgbury

Hedgehog watching

Someone called Barker has installed a Raspberry Pi–based hedgehog cam in a location with a distant view of a famous Alp, and as well as providing live views by visible and infrared light for the dedicated and the insomniac, they also make a sped-up version of the previous night’s activity available. With hedgehogs usually being in hibernation during January, you mightn’t see them in any current feed — but don’t worry! You’re guaranteed a few hedgehogs on Barker’s website, because they have also thrown in some lovely GIFs of hoggy (and foxy) divas that their camera captured in the past.

A Hedgehog eating from a bowl on a patio, captured by a hedgehog cam

Nom nom nom!
GIF by Barker’s Site

Build your own hedgehog cam

For pointers on how to replicate this kind of setup, you could do worse than turn to Andrew Wedgbury’s hedgehog cam write-up. Andrew’s Twitter feed reveals that he’s a Cambridge local, and there are hints that he was behind RealVNC’s hoggy mascot for Pi Wars 2017.

RealVNC on Twitter

Another day at the office: testing our #PiWars mascot using a @Raspberry_Pi 3, #VNC Connect and @4tronix_uk Picon Zero. Name suggestions? https://t.co/iYY3xAX9Bk

Our infrared bird box and time-lapse camera resources will also set you well on the way towards your own custom wildlife camera. For a kit that wraps everything up in a weatherproof enclosure made with love, time, and serious amounts of design and testing, take a look at Naturebytes’ wildlife cam kit.

Or, if you’re thinking that a robot mascot is more dependable than real animals for the fluffiness you need in order to start your January with something like productivity and with your soul intact, you might like to put your own spin on our robot buggy.

Happy 2018

While we’re on the subject of getting to grips with the new year, do take a look at yesterday’s blog post, in which we suggest a New Year’s project that’s different from the usual resolutions. However you tackle 2018, we wish you an excellent year of creative computing.

The post A hedgehog cam or two appeared first on Raspberry Pi.

Could you write for Hello World magazine?

Post Syndicated from Dan Fisher original https://www.raspberrypi.org/blog/could-you-write-for-hello-world-magazine/

Thinking about New Year’s resolutions? Ditch the gym and tone up your author muscles instead, by writing an article for Hello World magazine. We’ll help you, you’ll expand your knowledge of a topic you care about, and you’ll be contributing something of real value to the computing education community.

Join our pool of Hello World writers in 2018

The computing and digital making magazine for educators

Hello World is our free computing magazine for educators, published in partnership with Computing At School and kindly supported by BT. We launched at the Bett Show in January 2017, and over the past twelve months, we’ve grown to a readership of 15000 subscribers. You can get your own free copy here.

Our work is sustained by wonderful educational content from around the world in every issue. We’re hugely grateful to our current pool of authors – keep it up, veterans of 2017! – and we want to provide opportunities for new voices in the community to join them. You might be a classroom teacher sharing your scheme of work, a volunteer reflecting on running an after-school club, an industry professional sharing your STEM expertise, or an academic providing insights into new research – we’d love contributions from all kinds of people in all sorts of roles.

Your article doesn’t have to be finished and complete: if you send us an outline, we will work with you to develop it into a full piece.

Like my desk, but tidier

Five reasons to write for Hello World

Here are five reasons why writing for Hello World is a great way to start 2018:

1. You’ll learn something new

Researching an article is one of the best ways to broaden your knowledge about something that interests you.

2. You’ll think more clearly

Notes in hand, you sit at your desk and wonder how to craft all this information into a coherent piece of writing. It’s a situation we’re all familiar with. Writing an article makes you examine and clarify what you really think about a subject.

Share your expertise and make more interesting projects along the way

3. You’ll make cool projects

Testing a project for a Hello World resource is a perfect opportunity to build something amazing that’s hitherto been locked away inside your brain.

4. You’ll be doing something that matters

Sharing your knowledge and experience in Hello World helps others to teach and learn computing. It helps bring the power of digital making to more and more educators and learners.

5. You’ll share with an open and supportive community

The computing education community is full of people who lend their experience to help colleagues. Contributing to Hello World is a great way to take an active part in this supportive community, and you’ll be adding to a body of free, open source learning resources that are available for everyone to use, adapt, and share. It’s also a tremendous platform to broadcast your work: the digital version alone of Hello World has been downloaded over 50000 times.

Yes! What do I do next?

Feeling inspired? Email our editorial team with your idea.

Issue 4 of Hello World is out this month! Subscribe for free today to have it delivered to your inbox or your home.

The post Could you write for Hello World magazine? appeared first on Raspberry Pi.