Tag Archives: AWS IoT

AWS Online Tech Talks – June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2018/

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

 

Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.

 

AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.
Compute

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.

 

Containers
June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.

 

Databases

June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.
DevOps

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.

 

Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new

 

AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.
IoT

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.

 

Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.

 

Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.

 

Mobile
June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.

 

Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.

 

Serverless

June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.

 

Storage

June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-1-click-use-simple-devices-to-trigger-lambda-functions/

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera ControlTim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

Jeff;

 

Amazon Sumerian – Now Generally Available

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-sumerian-now-generally-available/

We announced Amazon Sumerian at AWS re:Invent 2017. As you can see from Tara‘s blog post (Presenting Amazon Sumerian: An Easy Way to Create VR, AR, and 3D Experiences), Sumerian does not require any specialized programming or 3D graphics expertise. You can build VR, AR, and 3D experiences for a wide variety of popular hardware platforms including mobile devices, head-mounted displays, digital signs, and web browsers.

I’m happy to announce that Sumerian is now generally available. You can create realistic virtual environments and scenes without having to acquire or master specialized tools for 3D modeling, animation, lighting, audio editing, or programming. Once built, you can deploy your finished creation across multiple platforms without having to write custom code or deal with specialized deployment systems and processes.

Sumerian gives you a web-based editor that you can use to quickly and easily create realistic, professional-quality scenes. There’s a visual scripting tool that lets you build logic to control how objects and characters (Sumerian Hosts) respond to user actions. Sumerian also lets you create rich, natural interactions powered by AWS services such as Amazon Lex, Polly, AWS Lambda, AWS IoT, and Amazon DynamoDB.

Sumerian was designed to work on multiple platforms. The VR and AR apps that you create in Sumerian will run in browsers that supports WebGL or WebVR and on popular devices such as the Oculus Rift, HTC Vive, and those powered by iOS or Android.

During the preview period, we have been working with a broad spectrum of customers to put Sumerian to the test and to create proof of concept (PoC) projects designed to highlight an equally broad spectrum of use cases, including employee education, training simulations, field service productivity, virtual concierge, design and creative, and brand engagement. Fidelity Labs (the internal R&D unit of Fidelity Investments), was the first to use a Sumerian host to create an engaging VR experience. Cora (the host) lives within a virtual chart room. She can display stock quotes, pull up company charts, and answer questions about a company’s performance. This PoC uses Amazon Polly to implement text to speech and Amazon Lex for conversational chatbot functionality. Read their blog post and watch the video inside to see Cora in action:

Now that Sumerian is generally available, you have the power to create engaging AR, VR, and 3D experiences of your own. To learn more, visit the Amazon Sumerian home page and then spend some quality time with our extensive collection of Sumerian Tutorials.

Jeff;

 

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PTGaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PTBuilding API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PTAccelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PTLearn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.

 

 

 

 

Serverless Architectures with AWS Lambda: Overview and Best Practices

Post Syndicated from Andrew Baird original https://aws.amazon.com/blogs/architecture/serverless-architectures-with-aws-lambda-overview-and-best-practices/

For some organizations, the idea of “going serverless” can be daunting. But with an understanding of best practices – and the right tools — many serverless applications can be fully functional with only a few lines of code and little else.

Examples of fully-serverless-application use cases include:

  • Web or mobile backends – Create fully-serverless, mobile applications or websites by creating user-facing content in a native mobile application or static web content in an S3 bucket. Then have your front-end content integrate with Amazon API Gateway as a backend service API. Lambda functions will then execute the business logic you’ve written for each of the API Gateway methods in your backend API.
  • Chatbots and virtual assistants – Build new serverless ways to interact with your customers, like customer support assistants and bots ready to engage customers on your company-run social media pages. The Amazon Alexa Skills Kit (ASK) and Amazon Lex have the ability to apply natural-language understanding to user-voice and freeform-text input so that a Lambda function you write can intelligently respond and engage with them.
  • Internet of Things (IoT) backends – AWS IoT has direct-integration for device messages to be routed to and processed by Lambda functions. That means you can implement serverless backends for highly secure, scalable IoT applications for uses like connected consumer appliances and intelligent manufacturing facilities.

Using AWS Lambda as the logic layer of a serverless application can enable faster development speed and greater experimentation – and innovation — than in a traditional, server-based environment.

We recently published the “Serverless Architectures with AWS Lambda: Overview and Best Practices” whitepaper to provide the guidance and best practices you need to write better Lambda functions and build better serverless architectures.

Once you’ve finished reading the whitepaper, below are a couple additional resources I recommend as your next step:

  1. If you would like to better understand some of the architecture pattern possibilities for serverless applications: Thirty Serverless Architectures in 30 Minutes (re:Invent 2017 video)
  2. If you’re ready to get hands-on and build a sample serverless application: AWS Serverless Workshops (GitHub Repository)
  3. If you’ve already built a serverless application and you’d like to ensure your application has been Well Architected: The Serverless Application Lens: AWS Well Architected Framework (Whitepaper)

About the Author

 

Andrew Baird is a Sr. Solutions Architect for AWS. Prior to becoming a Solutions Architect, Andrew was a developer, including time as an SDE with Amazon.com. He has worked on large-scale distributed systems, public-facing APIs, and operations automation.

AWS Online Tech Talks – April & Early May 2018

Post Syndicated from Betsy Chernoff original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-april-early-may-2018/

We have several upcoming tech talks in the month of April and early May. Come join us to learn about AWS services and solution offerings. We’ll have AWS experts online to help answer questions in real-time. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

April & early May — 2018 Schedule

Compute

April 30, 2018 | 01:00 PM – 01:45 PM PTBest Practices for Running Amazon EC2 Spot Instances with Amazon EMR (300) – Learn about the best practices for scaling big data workloads as well as process, store, and analyze big data securely and cost effectively with Amazon EMR and Amazon EC2 Spot Instances.

May 1, 2018 | 01:00 PM – 01:45 PM PTHow to Bring Microsoft Apps to AWS (300) – Learn more about how to save significant money by bringing your Microsoft workloads to AWS.

May 2, 2018 | 01:00 PM – 01:45 PM PTDeep Dive on Amazon EC2 Accelerated Computing (300) – Get a technical deep dive on how AWS’ GPU and FGPA-based compute services can help you to optimize and accelerate your ML/DL and HPC workloads in the cloud.

Containers

April 23, 2018 | 11:00 AM – 11:45 AM PTNew Features for Building Powerful Containerized Microservices on AWS (300) – Learn about how this new feature works and how you can start using it to build and run modern, containerized applications on AWS.

Databases

April 23, 2018 | 01:00 PM – 01:45 PM PTElastiCache: Deep Dive Best Practices and Usage Patterns (200) – Learn about Redis-compatible in-memory data store and cache with Amazon ElastiCache.

April 25, 2018 | 01:00 PM – 01:45 PM PTIntro to Open Source Databases on AWS (200) – Learn how to tap the benefits of open source databases on AWS without the administrative hassle.

DevOps

April 25, 2018 | 09:00 AM – 09:45 AM PTDebug your Container and Serverless Applications with AWS X-Ray in 5 Minutes (300) – Learn how AWS X-Ray makes debugging your Container and Serverless applications fun.

Enterprise & Hybrid

April 23, 2018 | 09:00 AM – 09:45 AM PTAn Overview of Best Practices of Large-Scale Migrations (300) – Learn about the tools and best practices on how to migrate to AWS at scale.

April 24, 2018 | 11:00 AM – 11:45 AM PTDeploy your Desktops and Apps on AWS (300) – Learn how to deploy your desktops and apps on AWS with Amazon WorkSpaces and Amazon AppStream 2.0

IoT

May 2, 2018 | 11:00 AM – 11:45 AM PTHow to Easily and Securely Connect Devices to AWS IoT (200) – Learn how to easily and securely connect devices to the cloud and reliably scale to billions of devices and trillions of messages with AWS IoT.

Machine Learning

April 24, 2018 | 09:00 AM – 09:45 AM PT Automate for Efficiency with Amazon Transcribe and Amazon Translate (200) – Learn how you can increase the efficiency and reach your operations with Amazon Translate and Amazon Transcribe.

April 26, 2018 | 09:00 AM – 09:45 AM PT Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sagemaker (200) – Learn more about developing machine learning applications for the IoT edge.

Mobile

April 30, 2018 | 11:00 AM – 11:45 AM PTOffline GraphQL Apps with AWS AppSync (300) – Come learn how to enable real-time and offline data in your applications with GraphQL using AWS AppSync.

Networking

May 2, 2018 | 09:00 AM – 09:45 AM PT Taking Serverless to the Edge (300) – Learn how to run your code closer to your end users in a serverless fashion. Also, David Von Lehman from Aerobatic will discuss how they used [email protected] to reduce latency and cloud costs for their customer’s websites.

Security, Identity & Compliance

April 30, 2018 | 09:00 AM – 09:45 AM PTAmazon GuardDuty – Let’s Attack My Account! (300) – Amazon GuardDuty Test Drive – Practical steps on generating test findings.

May 3, 2018 | 09:00 AM – 09:45 AM PTProtect Your Game Servers from DDoS Attacks (200) – Learn how to use the new AWS Shield Advanced for EC2 to protect your internet-facing game servers against network layer DDoS attacks and application layer attacks of all kinds.

Serverless

April 24, 2018 | 01:00 PM – 01:45 PM PTTips and Tricks for Building and Deploying Serverless Apps In Minutes (200) – Learn how to build and deploy apps in minutes.

Storage

May 1, 2018 | 11:00 AM – 11:45 AM PTBuilding Data Lakes That Cost Less and Deliver Results Faster (300) – Learn how Amazon S3 Select And Amazon Glacier Select increase application performance by up to 400% and reduce total cost of ownership by extending your data lake into cost-effective archive storage.

May 3, 2018 | 11:00 AM – 11:45 AM PTIntegrating On-Premises Vendors with AWS for Backup (300) – Learn how to work with AWS and technology partners to build backup & restore solutions for your on-premises, hybrid, and cloud native environments.

New – Machine Learning Inference at the Edge Using AWS Greengrass

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-machine-learning-inference-at-the-edge-using-aws-greengrass/

What happens when you combine the Internet of Things, Machine Learning, and Edge Computing? Before I tell you, let’s review each one and discuss what AWS has to offer.

Internet of Things (IoT) – Devices that connect the physical world and the digital one. The devices, often equipped with one or more types of sensors, can be found in factories, vehicles, mines, fields, homes, and so forth. Important AWS services include AWS IoT Core, AWS IoT Analytics, AWS IoT Device Management, and Amazon FreeRTOS, along with others that you can find on the AWS IoT page.

Machine Learning (ML) – Systems that can be trained using an at-scale dataset and statistical algorithms, and used to make inferences from fresh data. At Amazon we use machine learning to drive the recommendations that you see when you shop, to optimize the paths in our fulfillment centers, fly drones, and much more. We support leading open source machine learning frameworks such as TensorFlow and MXNet, and make ML accessible and easy to use through Amazon SageMaker. We also provide Amazon Rekognition for images and for video, Amazon Lex for chatbots, and a wide array of language services for text analysis, translation, speech recognition, and text to speech.

Edge Computing – The power to have compute resources and decision-making capabilities in disparate locations, often with intermittent or no connectivity to the cloud. AWS Greengrass builds on AWS IoT, giving you the ability to run Lambda functions and keep device state in sync even when not connected to the Internet.

ML Inference at the Edge
Today I would like to toss all three of these important new technologies into a blender! You can now perform Machine Learning inference at the edge using AWS Greengrass. This allows you to use the power of the AWS cloud (including fast, powerful instances equipped with GPUs) to build, train, and test your ML models before deploying them to small, low-powered, intermittently-connected IoT devices running in those factories, vehicles, mines, fields, and homes that I mentioned.

Here are a few of the many ways that you can put Greengrass ML Inference to use:

Precision Farming – With an ever-growing world population and unpredictable weather that can affect crop yields, the opportunity to use technology to increase yields is immense. Intelligent devices that are literally in the field can process images of soil, plants, pests, and crops, taking local corrective action and sending status reports to the cloud.

Physical Security – Smart devices (including the AWS DeepLens) can process images and scenes locally, looking for objects, watching for changes, and even detecting faces. When something of interest or concern arises, the device can pass the image or the video to the cloud and use Amazon Rekognition to take a closer look.

Industrial Maintenance – Smart, local monitoring can increase operational efficiency and reduce unplanned downtime. The monitors can run inference operations on power consumption, noise levels, and vibration to flag anomalies, predict failures, detect faulty equipment.

Greengrass ML Inference Overview
There are several different aspects to this new AWS feature. Let’s take a look at each one:

Machine Learning ModelsPrecompiled TensorFlow and MXNet libraries, optimized for production use on the NVIDIA Jetson TX2 and Intel Atom devices, and development use on 32-bit Raspberry Pi devices. The optimized libraries can take advantage of GPU and FPGA hardware accelerators at the edge in order to provide fast, local inferences.

Model Building and Training – The ability to use Amazon SageMaker and other cloud-based ML tools to build, train, and test your models before deploying them to your IoT devices. To learn more about SageMaker, read Amazon SageMaker – Accelerated Machine Learning.

Model Deployment – SageMaker models can (if you give them the proper IAM permissions) be referenced directly from your Greengrass groups. You can also make use of models stored in S3 buckets. You can add a new machine learning resource to a group with a couple of clicks:

These new features are available now and you can start using them today! To learn more read Perform Machine Learning Inference.

Jeff;

 

EU Compliance Update: AWS’s 2017 C5 Assessment

Post Syndicated from Oliver Bell original https://aws.amazon.com/blogs/security/eu-compliance-update-awss-2017-c5-assessment/

C5 logo

AWS has completed its 2017 assessment against the Cloud Computing Compliance Controls Catalog (C5) information security and compliance program. Bundesamt für Sicherheit in der Informationstechnik (BSI)—Germany’s national cybersecurity authority—established C5 to define a reference standard for German cloud security requirements. With C5 (as well as with IT-Grundschutz), customers in German member states can use the work performed under this BSI audit to comply with stringent local requirements and operate secure workloads in the AWS Cloud.

Continuing our commitment to Germany and the AWS European Regions, AWS has added 16 services to this year’s scope:

The English version of the C5 report is available through AWS Artifact. The German version of the report will be available through AWS Artifact in the coming weeks.

– Oliver

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;

Now Available: New Digital Training to Help You Learn About AWS Big Data Services

Post Syndicated from Sara Snedeker original https://aws.amazon.com/blogs/big-data/now-available-new-digital-training-to-help-you-learn-about-aws-big-data-services/

AWS Training and Certification recently released free digital training courses that will make it easier for you to build your cloud skills and learn about using AWS Big Data services. This training includes courses like Introduction to Amazon EMR and Introduction to Amazon Athena.

You can get free and unlimited access to more than 100 new digital training courses built by AWS experts at aws.training. It’s easy to access training related to big data. Just choose the Analytics category on our Find Training page to browse through the list of courses. You can also use the keyword filter to search for training for specific AWS offerings.

Recommended training

Just getting started, or looking to learn about a new service? Check out the following digital training courses:

Introduction to Amazon EMR (15 minutes)
Covers the available tools that can be used with Amazon EMR and the process of creating a cluster. It includes a demonstration of how to create an EMR cluster.

Introduction to Amazon Athena (10 minutes)
Introduces the Amazon Athena service along with an overview of its operating environment. It covers the basic steps in implementing Athena and provides a brief demonstration.

Introduction to Amazon QuickSight (10 minutes)
Discusses the benefits of using Amazon QuickSight and how the service works. It also includes a demonstration so that you can see Amazon QuickSight in action.

Introduction to Amazon Redshift (10 minutes)
Walks you through Amazon Redshift and its core features and capabilities. It also includes a quick overview of relevant use cases and a short demonstration.

Introduction to AWS Lambda (10 minutes)
Discusses the rationale for using AWS Lambda, how the service works, and how you can get started using it.

Introduction to Amazon Kinesis Analytics (10 minutes)
Discusses how Amazon Kinesis Analytics collects, processes, and analyzes streaming data in real time. It discusses how to use and monitor the service and explores some use cases.

Introduction to Amazon Kinesis Streams (15 minutes)
Covers how Amazon Kinesis Streams is used to collect, process, and analyze real-time streaming data to create valuable insights.

Introduction to AWS IoT (10 minutes)
Describes how the AWS Internet of Things (IoT) communication architecture works, and the components that make up AWS IoT. It discusses how AWS IoT works with other AWS services and reviews a case study.

Introduction to AWS Data Pipeline (10 minutes)
Covers components like tasks, task runner, and pipeline. It also discusses what a pipeline definition is, and reviews the AWS services that are compatible with AWS Data Pipeline.

Go deeper with classroom training

Want to learn more? Enroll in classroom training to learn best practices, get live feedback, and hear answers to your questions from an instructor.

Big Data on AWS (3 days)
Introduces you to cloud-based big data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS big data platform.

Data Warehousing on AWS (3 days)
Introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution, and demonstrates how to collect, store, and prepare data for the data warehouse.

Building a Serverless Data Lake (1 day)
Teaches you how to design, build, and operate a serverless data lake solution with AWS services. Includes topics such as ingesting data from any data source at large scale, storing the data securely and durably, using the right tool to process large volumes of data, and understanding the options available for analyzing the data in near-real time.

More training coming in 2018

We’re always evaluating and expanding our training portfolio, so stay tuned for more training options in the new year. You can always visit us at aws.training to explore our latest offerings.

AWS Training & Certification Update – Free Digital Training + Certified Cloud Practitioner Exam

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-training-certification-update-free-digital-training-certified-cloud-practitioner-exam/

We recently made some updates to AWS Training and Certification to make it easier for you to build your cloud skills and to learn about many of the new services that we launched at AWS re:Invent.

Free AWS Digital Training
You can now find over 100 new digital training classes at aws.training, all with unlimited access at no charge.

The courses were built by AWS experts and allow you to learn AWS at your own pace, helping you to build foundational knowledge for dozens of AWS services and solutions. You can also access some more advanced training on Machine Learning and Storage.

Here are some of the new digital training topics:

You can browse through the available topics, enroll in one that interests you, watch it, and track your progress by looking at your transcript:

AWS Certified Cloud Practitioner
Our newest certification exam, AWS Certified Cloud Practitioner, lets you validate your overall understanding of the AWS Cloud with an industry-recognized credential. It covers four domains: cloud concepts, security, technology, and billing and pricing. We recommend that you have at least six months of experience (or equivalent training) with the AWS Cloud in any role, including technical, managerial, sales, purchasing, or financial.

To help you prepare for this exam, take our new AWS Cloud Practitioner Essentials course , one of the new AWS digital training courses. This course will give you an overview of cloud concepts, AWS services, security, architecture, pricing, and support. In addition to helping you validate your overall understanding of the AWS Cloud, AWS Certified Cloud Practitioner also serves as a new prerequisite option for the Big Data Specialty and Advanced Networking Specialty certification exams.

Go For It!
I’d like to encourage you to check out aws.training and to enroll in our free digital training in order to learn more about AWS and our newest services. You can strengthen your skills, add to your knowledge base, and set a goal of earning your AWS Certified Cloud Practitioner certification in the new year.

Jeff;

AWS Updated Its ISO Certifications and Now Has 67 Services Under ISO Compliance

Post Syndicated from Chad Woolf original https://aws.amazon.com/blogs/security/aws-updated-its-iso-certifications-and-now-has-67-services-under-iso-compliance/

ISO logo

AWS has updated its certifications against ISO 9001, ISO 27001, ISO 27017, and ISO 27018 standards, bringing the total to 67 services now under ISO compliance. We added the following 29 services this cycle:

Amazon Aurora Amazon S3 Transfer Acceleration AWS [email protected]
Amazon Cloud Directory Amazon SageMaker AWS Managed Services
Amazon CloudWatch Logs Amazon Simple Notification Service AWS OpsWorks Stacks
Amazon Cognito Auto Scaling AWS Shield
Amazon Connect AWS Batch AWS Snowball Edge
Amazon Elastic Container Registry AWS CodeBuild AWS Snowmobile
Amazon Inspector AWS CodeCommit AWS Step Functions
Amazon Kinesis Data Streams AWS CodeDeploy AWS Systems Manager (formerly Amazon EC2 Systems Manager)
Amazon Macie AWS CodePipeline AWS X-Ray
Amazon QuickSight AWS IoT Core

For the complete list of services under ISO compliance, see AWS Services in Scope by Compliance Program.

AWS maintains certifications through extensive audits of its controls to ensure that information security risks that affect the confidentiality, integrity, and availability of company and customer information are appropriately managed.

You can download copies of the AWS ISO certificates that contain AWS’s in-scope services and Regions, and use these certificates to jump-start your own certification efforts:

AWS does not increase service costs in any AWS Region as a result of updating its certifications.

To learn more about compliance in the AWS Cloud, see AWS Cloud Compliance.

– Chad

Power data ingestion into Splunk using Amazon Kinesis Data Firehose

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/big-data/power-data-ingestion-into-splunk-using-amazon-kinesis-data-firehose/

In late September, during the annual Splunk .conf, Splunk and Amazon Web Services (AWS) jointly announced that Amazon Kinesis Data Firehose now supports Splunk Enterprise and Splunk Cloud as a delivery destination. This native integration between Splunk Enterprise, Splunk Cloud, and Amazon Kinesis Data Firehose is designed to make AWS data ingestion setup seamless, while offering a secure and fault-tolerant delivery mechanism. We want to enable customers to monitor and analyze machine data from any source and use it to deliver operational intelligence and optimize IT, security, and business performance.

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

Push vs. Pull data ingestion

Presently, customers use a combination of two ingestion patterns, primarily based on data source and volume, in addition to existing company infrastructure and expertise:

  1. Pull-based approach: Using dedicated pollers running the popular Splunk Add-on for AWS to pull data from various AWS services such as Amazon CloudWatch or Amazon S3.
  2. Push-based approach: Streaming data directly from AWS to Splunk HTTP Event Collector (HEC) by using AWS Lambda. Examples of applicable data sources include CloudWatch Logs and Amazon Kinesis Data Streams.

The pull-based approach offers data delivery guarantees such as retries and checkpointing out of the box. However, it requires more ops to manage and orchestrate the dedicated pollers, which are commonly running on Amazon EC2 instances. With this setup, you pay for the infrastructure even when it’s idle.

On the other hand, the push-based approach offers a low-latency scalable data pipeline made up of serverless resources like AWS Lambda sending directly to Splunk indexers (by using Splunk HEC). This approach translates into lower operational complexity and cost. However, if you need guaranteed data delivery then you have to design your solution to handle issues such as a Splunk connection failure or Lambda execution failure. To do so, you might use, for example, AWS Lambda Dead Letter Queues.

How about getting the best of both worlds?

Let’s go over the new integration’s end-to-end solution and examine how Kinesis Data Firehose and Splunk together expand the push-based approach into a native AWS solution for applicable data sources.

By using a managed service like Kinesis Data Firehose for data ingestion into Splunk, we provide out-of-the-box reliability and scalability. One of the pain points of the old approach was the overhead of managing the data collection nodes (Splunk heavy forwarders). With the new Kinesis Data Firehose to Splunk integration, there are no forwarders to manage or set up. Data producers (1) are configured through the AWS Management Console to drop data into Kinesis Data Firehose.

You can also create your own data producers. For example, you can drop data into a Firehose delivery stream by using Amazon Kinesis Agent, or by using the Firehose API (PutRecord(), PutRecordBatch()), or by writing to a Kinesis Data Stream configured to be the data source of a Firehose delivery stream. For more details, refer to Sending Data to an Amazon Kinesis Data Firehose Delivery Stream.

You might need to transform the data before it goes into Splunk for analysis. For example, you might want to enrich it or filter or anonymize sensitive data. You can do so using AWS Lambda. In this scenario, Kinesis Data Firehose buffers data from the incoming source data, sends it to the specified Lambda function (2), and then rebuffers the transformed data to the Splunk Cluster. Kinesis Data Firehose provides the Lambda blueprints that you can use to create a Lambda function for data transformation.

Systems fail all the time. Let’s see how this integration handles outside failures to guarantee data durability. In cases when Kinesis Data Firehose can’t deliver data to the Splunk Cluster, data is automatically backed up to an S3 bucket. You can configure this feature while creating the Firehose delivery stream (3). You can choose to back up all data or only the data that’s failed during delivery to Splunk.

In addition to using S3 for data backup, this Firehose integration with Splunk supports Splunk Indexer Acknowledgments to guarantee event delivery. This feature is configured on Splunk’s HTTP Event Collector (HEC) (4). It ensures that HEC returns an acknowledgment to Kinesis Data Firehose only after data has been indexed and is available in the Splunk cluster (5).

Now let’s look at a hands-on exercise that shows how to forward VPC flow logs to Splunk.

How-to guide

To process VPC flow logs, we implement the following architecture.

Amazon Virtual Private Cloud (Amazon VPC) delivers flow log files into an Amazon CloudWatch Logs group. Using a CloudWatch Logs subscription filter, we set up real-time delivery of CloudWatch Logs to an Kinesis Data Firehose stream.

Data coming from CloudWatch Logs is compressed with gzip compression. To work with this compression, we need to configure a Lambda-based data transformation in Kinesis Data Firehose to decompress the data and deposit it back into the stream. Firehose then delivers the raw logs to the Splunk Http Event Collector (HEC).

If delivery to the Splunk HEC fails, Firehose deposits the logs into an Amazon S3 bucket. You can then ingest the events from S3 using an alternate mechanism such as a Lambda function.

When data reaches Splunk (Enterprise or Cloud), Splunk parsing configurations (packaged in the Splunk Add-on for Kinesis Data Firehose) extract and parse all fields. They make data ready for querying and visualization using Splunk Enterprise and Splunk Cloud.

Walkthrough

Install the Splunk Add-on for Amazon Kinesis Data Firehose

The Splunk Add-on for Amazon Kinesis Data Firehose enables Splunk (be it Splunk Enterprise, Splunk App for AWS, or Splunk Enterprise Security) to use data ingested from Amazon Kinesis Data Firehose. Install the Add-on on all the indexers with an HTTP Event Collector (HEC). The Add-on is available for download from Splunkbase.

HTTP Event Collector (HEC)

Before you can use Kinesis Data Firehose to deliver data to Splunk, set up the Splunk HEC to receive the data. From Splunk web, go to the Setting menu, choose Data Inputs, and choose HTTP Event Collector. Choose Global Settings, ensure All tokens is enabled, and then choose Save. Then choose New Token to create a new HEC endpoint and token. When you create a new token, make sure that Enable indexer acknowledgment is checked.

When prompted to select a source type, select aws:cloudwatch:vpcflow.

Create an S3 backsplash bucket

To provide for situations in which Kinesis Data Firehose can’t deliver data to the Splunk Cluster, we use an S3 bucket to back up the data. You can configure this feature to back up all data or only the data that’s failed during delivery to Splunk.

Note: Bucket names are unique. Thus, you can’t use tmak-backsplash-bucket.

aws s3 create-bucket --bucket tmak-backsplash-bucket --create-bucket-configuration LocationConstraint=ap-northeast-1

Create an IAM role for the Lambda transform function

Firehose triggers an AWS Lambda function that transforms the data in the delivery stream. Let’s first create a role for the Lambda function called LambdaBasicRole.

Note: You can also set this role up when creating your Lambda function.

$ aws iam create-role --role-name LambdaBasicRole --assume-role-policy-document file://TrustPolicyForLambda.json

Here is TrustPolicyForLambda.json.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

 

After the role is created, attach the managed Lambda basic execution policy to it.

$ aws iam attach-role-policy 
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole 
  --role-name LambdaBasicRole

 

Create a Firehose Stream

On the AWS console, open the Amazon Kinesis service, go to the Firehose console, and choose Create Delivery Stream.

In the next section, you can specify whether you want to use an inline Lambda function for transformation. Because incoming CloudWatch Logs are gzip compressed, choose Enabled for Record transformation, and then choose Create new.

From the list of the available blueprint functions, choose Kinesis Data Firehose CloudWatch Logs Processor. This function unzips data and place it back into the Firehose stream in compliance with the record transformation output model.

Enter a name for the Lambda function, choose Choose an existing role, and then choose the role you created earlier. Then choose Create Function.

Go back to the Firehose Stream wizard, choose the Lambda function you just created, and then choose Next.

Select Splunk as the destination, and enter your Splunk Http Event Collector information.

Note: Amazon Kinesis Data Firehose requires the Splunk HTTP Event Collector (HEC) endpoint to be terminated with a valid CA-signed certificate matching the DNS hostname used to connect to your HEC endpoint. You receive delivery errors if you are using a self-signed certificate.

In this example, we only back up logs that fail during delivery.

To monitor your Firehose delivery stream, enable error logging. Doing this means that you can monitor record delivery errors.

Create an IAM role for the Firehose stream by choosing Create new, or Choose. Doing this brings you to a new screen. Choose Create a new IAM role, give the role a name, and then choose Allow.

If you look at the policy document, you can see that the role gives Kinesis Data Firehose permission to publish error logs to CloudWatch, execute your Lambda function, and put records into your S3 backup bucket.

You now get a chance to review and adjust the Firehose stream settings. When you are satisfied, choose Create Stream. You get a confirmation once the stream is created and active.

Create a VPC Flow Log

To send events from Amazon VPC, you need to set up a VPC flow log. If you already have a VPC flow log you want to use, you can skip to the “Publish CloudWatch to Kinesis Data Firehose” section.

On the AWS console, open the Amazon VPC service. Then choose VPC, Your VPC, and choose the VPC you want to send flow logs from. Choose Flow Logs, and then choose Create Flow Log. If you don’t have an IAM role that allows your VPC to publish logs to CloudWatch, choose Set Up Permissions and Create new role. Use the defaults when presented with the screen to create the new IAM role.

Once active, your VPC flow log should look like the following.

Publish CloudWatch to Kinesis Data Firehose

When you generate traffic to or from your VPC, the log group is created in Amazon CloudWatch. The new log group has no subscription filter, so set up a subscription filter. Setting this up establishes a real-time data feed from the log group to your Firehose delivery stream.

At present, you have to use the AWS Command Line Interface (AWS CLI) to create a CloudWatch Logs subscription to a Kinesis Data Firehose stream. However, you can use the AWS console to create subscriptions to Lambda and Amazon Elasticsearch Service.

To allow CloudWatch to publish to your Firehose stream, you need to give it permissions.

$ aws iam create-role --role-name CWLtoKinesisFirehoseRole --assume-role-policy-document file://TrustPolicyForCWLToFireHose.json


Here is the content for TrustPolicyForCWLToFireHose.json.

{
  "Statement": {
    "Effect": "Allow",
    "Principal": { "Service": "logs.us-east-1.amazonaws.com" },
    "Action": "sts:AssumeRole"
  }
}

 

Attach the policy to the newly created role.

$ aws iam put-role-policy 
    --role-name CWLtoKinesisFirehoseRole 
    --policy-name Permissions-Policy-For-CWL 
    --policy-document file://PermissionPolicyForCWLToFireHose.json

Here is the content for PermissionPolicyForCWLToFireHose.json.

{
    "Statement":[
      {
        "Effect":"Allow",
        "Action":["firehose:*"],
        "Resource":["arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/ FirehoseSplunkDeliveryStream"]
      },
      {
        "Effect":"Allow",
        "Action":["iam:PassRole"],
        "Resource":["arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"]
      }
    ]
}

Finally, create a subscription filter.

$ aws logs put-subscription-filter 
   --log-group-name " /vpc/flowlog/FirehoseSplunkDemo" 
   --filter-name "Destination" 
   --filter-pattern "" 
   --destination-arn "arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/FirehoseSplunkDeliveryStream" 
   --role-arn "arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"

When you run the AWS CLI command preceding, you don’t get any acknowledgment. To validate that your CloudWatch Log Group is subscribed to your Firehose stream, check the CloudWatch console.

As soon as the subscription filter is created, the real-time log data from the log group goes into your Firehose delivery stream. Your stream then delivers it to your Splunk Enterprise or Splunk Cloud environment for querying and visualization. The screenshot following is from Splunk Enterprise.

In addition, you can monitor and view metrics associated with your delivery stream using the AWS console.

Conclusion

Although our walkthrough uses VPC Flow Logs, the pattern can be used in many other scenarios. These include ingesting data from AWS IoT, other CloudWatch logs and events, Kinesis Streams or other data sources using the Kinesis Agent or Kinesis Producer Library. We also used Lambda blueprint Kinesis Data Firehose CloudWatch Logs Processor to transform streaming records from Kinesis Data Firehose. However, you might need to use a different Lambda blueprint or disable record transformation entirely depending on your use case. For an additional use case using Kinesis Data Firehose, check out This is My Architecture Video, which discusses how to securely centralize cross-account data analytics using Kinesis and Splunk.

 


Additional Reading

If you found this post useful, be sure to check out Integrating Splunk with Amazon Kinesis Streams and Using Amazon EMR and Hunk for Rapid Response Log Analysis and Review.


About the Authors

Tarik Makota is a solutions architect with the Amazon Web Services Partner Network. He provides technical guidance, design advice and thought leadership to AWS’ most strategic software partners. His career includes work in an extremely broad software development and architecture roles across ERP, financial printing, benefit delivery and administration and financial services. He holds an M.S. in Software Development and Management from Rochester Institute of Technology.

 

 

 

Roy Arsan is a solutions architect in the Splunk Partner Integrations team. He has a background in product development, cloud architecture, and building consumer and enterprise cloud applications. More recently, he has architected Splunk solutions on major cloud providers, including an AWS Quick Start for Splunk that enables AWS users to easily deploy distributed Splunk Enterprise straight from their AWS console. He’s also the co-author of the AWS Lambda blueprints for Splunk. He holds an M.S. in Computer Science Engineering from the University of Michigan.

 

 

 

Presenting AWS IoT Analytics: Delivering IoT Analytics at Scale and Faster than Ever Before

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/launch-presenting-aws-iot-analytics/

One of the technology areas I thoroughly enjoy is the Internet of Things (IoT). Even as a child I used to infuriate my parents by taking apart the toys they would purchase for me to see how they worked and if I could somehow put them back together. It seems somehow I was destined to end up the tough and ever-changing world of technology. Therefore, it’s no wonder that I am really enjoying learning and tinkering with IoT devices and technologies. It combines my love of development and software engineering with my curiosity around circuits, controllers, and other facets of the electrical engineering discipline; even though an electrical engineer I can not claim to be.

Despite all of the information that is collected by the deployment of IoT devices and solutions, I honestly never really thought about the need to analyze, search, and process this data until I came up against a scenario where it became of the utmost importance to be able to search and query through loads of sensory data for an anomaly occurrence. Of course, I understood the importance of analytics for businesses to make accurate decisions and predictions to drive the organization’s direction. But it didn’t occur to me initially, how important it was to make analytics an integral part of my IoT solutions. Well, I learned my lesson just in time because this re:Invent a service is launching to make it easier for anyone to process and analyze IoT messages and device data.

 

Hello, AWS IoT Analytics!  AWS IoT Analytics is a fully managed service of AWS IoT that provides advanced data analysis of data collected from your IoT devices.  With the AWS IoT Analytics service, you can process messages, gather and store large amounts of device data, as well as, query your data. Also, the new AWS IoT Analytics service feature integrates with Amazon Quicksight for visualization of your data and brings the power of machine learning through integration with Jupyter Notebooks.

Benefits of AWS IoT Analytics

  • Helps with predictive analysis of data by providing access to pre-built analytical functions
  • Provides ability to visualize analytical output from service
  • Provides tools to clean up data
  • Can help identify patterns in the gathered data

Be In the Know: IoT Analytics Concepts

  • Channel: archives the raw, unprocessed messages and collects data from MQTT topics.
  • Pipeline: consumes messages from channels and allows message processing.
    • Activities: perform transformations on your messages including filtering attributes and invoking lambda functions advanced processing.
  • Data Store: Used as a queryable repository for processed messages. Provide ability to have multiple datastores for messages coming from different devices or locations or filtered by message attributes.
  • Data Set: Data retrieval view from a data store, can be generated by a recurring schedule. 

Getting Started with AWS IoT Analytics

First, I’ll create a channel to receive incoming messages.  This channel can be used to ingest data sent to the channel via MQTT or messages directed from the Rules Engine. To create a channel, I’ll select the Channels menu option and then click the Create a channel button.

I’ll name my channel, TaraIoTAnalyticsID and give the Channel a MQTT topic filter of Temperature. To complete the creation of my channel, I will click the Create Channel button.

Now that I have my Channel created, I need to create a Data Store to receive and store the messages received on the Channel from my IoT device. Remember you can set up multiple Data Stores for more complex solution needs, but I’ll just create one Data Store for my example. I’ll select Data Stores from menu panel and click Create a data store.

 

I’ll name my Data Store, TaraDataStoreID, and once I click the Create the data store button and I would have successfully set up a Data Store to house messages coming from my Channel.

Now that I have my Channel and my Data Store, I will need to connect the two using a Pipeline. I’ll create a simple pipeline that just connects my Channel and Data Store, but you can create a more robust pipeline to process and filter messages by adding Pipeline activities like a Lambda activity.

To create a pipeline, I’ll select the Pipelines menu option and then click the Create a pipeline button.

I will not add an Attribute for this pipeline. So I will click Next button.

As we discussed there are additional pipeline activities that I can add to my pipeline for the processing and transformation of messages but I will keep my first pipeline simple and hit the Next button.

The final step in creating my pipeline is for me to select my previously created Data Store and click Create Pipeline.

All that is left for me to take advantage of the AWS IoT Analytics service is to create an IoT rule that sends data to an AWS IoT Analytics channel.  Wow, that was a super easy process to set up analytics for IoT devices.

If I wanted to create a Data Set as a result of queries run against my data for visualization with Amazon Quicksight or integrate with Jupyter Notebooks to perform more advanced analytical functions, I can choose the Analyze menu option to bring up the screens to create data sets and access the Juypter Notebook instances.

Summary

As you can see, it was a very simple process to set up the advanced data analysis for AWS IoT. With AWS IoT Analytics, you have the ability to collect, visualize, process, query and store large amounts of data generated from your AWS IoT connected device. Additionally, you can access the AWS IoT Analytics service in a myriad of different ways; the AWS Command Line Interface (AWS CLI), the AWS IoT API, language-specific AWS SDKs, and AWS IoT Device SDKs.

AWS IoT Analytics is available today for you to dig into the analysis of your IoT data. To learn more about AWS IoT and AWS IoT Analytics go to the AWS IoT Analytics product page and/or the AWS IoT documentation.

Tara

In the Works – AWS IoT Device Defender – Secure Your IoT Fleet

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/in-the-works-aws-sepio-secure-your-iot-fleet/

Scale takes on a whole new meaning when it comes to IoT. Last year I was lucky enough to tour a gigantic factory that had, on average, one environment sensor per square meter. The sensors measured temperature, humidity, and air purity several times per second, and served as an early warning system for contaminants. I’ve heard customers express interest in deploying IoT-enabled consumer devices in the millions or tens of millions.

With powerful, long-lived devices deployed in a geographically distributed fashion, managing security challenges is crucial. However, the limited amount of local compute power and memory can sometimes limit the ability to use encryption and other forms of data protection.

To address these challenges and to allow our customers to confidently deploy IoT devices at scale, we are working on IoT Device Defender. While the details might change before release, AWS IoT Device Defender is designed to offer these benefits:

Continuous AuditingAWS IoT Device Defender monitors the policies related to your devices to ensure that the desired security settings are in place. It looks for drifts away from best practices and supports custom audit rules so that you can check for conditions that are specific to your deployment. For example, you could check to see if a compromised device has subscribed to sensor data from another device. You can run audits on a schedule or on an as-needed basis.

Real-Time Detection and AlertingAWS IoT Device Defender looks for and quickly alerts you to unusual behavior that could be coming from a compromised device. It does this by monitoring the behavior of similar devices over time, looking for unauthorized access attempts, changes in connection patterns, and changes in traffic patterns (either inbound or outbound).

Fast Investigation and Mitigation – In the event that you get an alert that something unusual is happening, AWS IoT Device Defender gives you the tools, including contextual information, to help you to investigate and mitigate the problem. Device information, device statistics, diagnostic logs, and previous alerts are all at your fingertips. You have the option to reboot the device, revoke its permissions, reset it to factory defaults, or push a security fix.

Stay Tuned
I’ll have more info (and a hands-on post) as soon as possible, so stay tuned!

Jeff;