Tag Archives: Uncategorized

Hunting for life on Mars assisted by high-altitude balloons

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/eclipse-high-altitude-balloons/

Will bacteria-laden high-altitude balloons help us find life on Mars? Today’s eclipse should bring us closer to an answer.

NASA Bacteria Balloons Raspberry Pi HAB Life on Mars

image c/o NASA / Ames Research Center / Tristan Caro

The Eclipse Ballooning Project

Having learned of the Eclipse Ballooning Project set to take place today across the USA, a team at NASA couldn’t miss the opportunity to harness the high-flying project for their own experiments.

NASA Bacteria Balloons Raspberry Pi HAB Life on Mars

The Eclipse Ballooning Project invited students across the USA to aid in the launch of 50+ high-altitude balloons during today’s eclipse. Each balloon is equipped with its own Raspberry Pi and camera for data collection and live video-streaming.

High-altitude ballooning, or HAB as it’s often referred to, has become a popular activity within the Raspberry Pi community. The lightweight nature of the device allows for high ascent, and its Camera Module enables instant visual content collection.

Life on Mars

image c/o Montana State University

The Eclipse Ballooning Project team, headed by Angela Des Jardins of Montana State University, was contacted by Jim Green, Director of Planetary Science at NASA, who hoped to piggyback on the project to run tests on bacteria in the Mars-like conditions the balloons would encounter near space.

Into the stratosphere

At around -35 degrees Fahrenheit, with thinner air and harsher ultraviolet radiation, the conditions in the upper part of the earth’s stratosphere are comparable to those on the surface of Mars. And during the eclipse, the moon will block some UV rays, making the environment in our stratosphere even more similar to the martian oneideal for NASA’s experiment.

So the students taking part in the Eclipse Ballooning Project could help the scientists out, NASA sent them some small metal tags.

NASA Bacteria Balloons Raspberry Pi HAB Life on Mars

These tags contain samples of a kind of bacterium known as Paenibacillus xerothermodurans. Upon their return to ground, the bacteria will be tested to see whether and how the high-altitude conditions affected them.

Life on Mars

Paenibacillus xerothermodurans is one of the most resilient bacterial species we know. The team at NASA wants to discover how the bacteria react to their flight in order to learn more about whether life on Mars could possibly exist. If the low temperature, UV rays, and air conditions cause the bacteria to mutate or indeed die, we can be pretty sure that the existence of living organisms on the surface of Mars is very unlikely.

Life on Mars

What happens to the bacteria on the spacecraft and rovers we send to space? This experiment should provide some answers.

The eclipse

If you’re in the US, you might have a chance to witness the full solar eclipse today. And if you’re planning to watch, please make sure to take all precautionary measures. In a nutshell, don’t look directly at the sun. Not today, not ever.

If you’re in the UK, you can observe a partial eclipse, if the clouds decide to vanish. And again, take note of safety measures so you don’t damage your eyes.

Life on Mars

You can also watch a live-stream of the eclipse via the NASA website.

If you’ve created an eclipse-viewing Raspberry Pi project, make sure to share it with us. And while we’re talking about eclipses and balloons, check here for our coverage of the 2015 balloon launches coinciding with the UK’s partial eclipse.

The post Hunting for life on Mars assisted by high-altitude balloons appeared first on Raspberry Pi.

Michael Reeves and the ridiculous Subscriber Robot

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/michael-reeves-subscriber-robot/

At the beginning of his new build’s video, YouTuber Michael Reeves discusses a revelation he had about why some people don’t subscribe to his channel:

The real reason some people don’t subscribe is that when you hit this button, that’s all, that’s it, it’s done. It’s not special, it’s not enjoyable. So how do we make subscribing a fun, enjoyable process? Well, we do it by slowly chipping away at the content creator’s psyche every time someone subscribes.

His fix? The ‘fun’ interactive Subscriber Robot that is the subject of the video.

Be aware that Michael uses a couple of mild swears in this video, so maybe don’t watch it with a child.

The Subscriber Robot

Just showing that subscriber dedication My Patreon Page: https://www.patreon.com/michaelreeves Personal Site: https://michaelreeves.us/ Twitter: https://twitter.com/michaelreeves08 Song: Summer Salt – Sweet To Me

Who is Michael Reeves?

Software developer and student Michael Reeves started his YouTube account a mere four months ago, with the premiere of his robot that shines lasers into your eyes – now he has 110k+ subscribers. At only 19, Michael co-owns and manages a company together with friends, and is set on his career path in software and computing. So when he is not making videos, he works a nine-to-five job “to pay for college and, y’know, live”.

The Subscriber Robot

Michael shot to YouTube fame with the aforementioned laser robot built around an Arduino. But by now he has also be released videos for a few Raspberry Pi-based contraptions.

Michael Reeves Raspberry Pi Subscriber Robot

Michael, talking us through the details of one of the worst ideas ever made

His Subscriber Robot uses a series of Python scripts running on a Raspberry Pi to check for new subscribers to Michael’s channel via the YouTube API. When it identifies one, the Pi uses a relay to make the ceiling lights in Michael’s office flash ten times a second while ear-splitting noise is emitted by a 102-decibel-rated buzzer. Needless to say, this buzzer is not recommended for home use, work use, or any use whatsoever! Moreover, the Raspberry Pi also connects to a speaker that announces the name of the new subscriber, so Michael knows who to thank.

Michael Reeves Raspberry Pi Subscriber Robot

Subscriber Robot: EEH! EEH! EEH! MoistPretzels has subscribed.
Michael: Thank you, MoistPretzels…

Given that Michael has gained a whopping 30,000 followers in the ten days since the release of this video, it’s fair to assume he is currently curled up in a ball on the office floor, quietly crying to himself.

If you think Michael only makes videos about ridiculous builds, you’re mistaken. He also uses YouTube to provide educational content, because he believes that “it’s super important for people to teach themselves how to program”. For example, he has just released a new C# beginners tutorial, the third in the series.

Support Michael

If you’d like to help Michael in his mission to fill the world with both tutorials and ridiculous robot builds, make sure to subscribe to his channel. You can also follow him on Twitter and support him on Patreon.

You may also want to check out the Useless Duck Company and Simone Giertz if you’re in the mood for more impractical, yet highly amusing, robot builds.

Good luck with your channel, Michael! We are looking forward to, and slightly dreading, more videos from one of our favourite new YouTubers.

The post Michael Reeves and the ridiculous Subscriber Robot appeared first on Raspberry Pi.

Announcing Dedicated IP Pools

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/ses/announcing-dedicated-ip-pools/

The Amazon SES team is pleased to announce that you can now create groups of dedicated IP addresses, called dedicated IP pools, for your email sending activities.

Prior to the availability of this feature, if you leased several dedicated IP addresses to use with Amazon SES, there was no way to specify which dedicated IP address to use for a specific email. Dedicated IP pools solve this problem by allowing you to send emails from specific IP addresses.

This post includes information and procedures related to dedicated IP pools.

What are dedicated IP pools?

In order to understand dedicated IP pools, you should first be familiar with the concept of dedicated IP addresses. Customers who send large volumes of email will typically lease one or more dedicated IP addresses to use when sending mail from Amazon SES. To learn more, see our blog post about dedicated IP addresses.

If you lease several dedicated IP addresses for use with Amazon SES, you can organize these addresses into groups, called pools. You can then associate each pool with a configuration set. When you send an email that specifies a configuration set, that email will be sent from the IP addresses in the associated pool.

When should I use dedicated IP pools?

Dedicated IP pools are especially useful for customers who send several different types of email using Amazon SES. For example, if you use Amazon SES to send both marketing emails and transactional emails, you can create a pool for marketing emails and another for transactional emails.

By using dedicated IP pools, you can isolate the sender reputations for each of these types of communications. Using dedicated IP pools gives you complete control over the sender reputations of the dedicated IP addresses you lease from Amazon SES.

How do I create and use dedicated IP pools?

There are two basic steps for creating and using dedicated IP pools. First, create a dedicated IP pool in the Amazon SES console and associate it with a configuration set. Next, when you send email, be sure to specify the configuration set associated with the IP pool you want to use.

For step-by-step procedures, see Creating Dedicated IP Pools in the Amazon SES Developer Guide.

Will my email sending process change?

If you do not use dedicated IP addresses with Amazon SES, then your email sending process will not change.

If you use dedicated IP pools, your email sending process may change slightly. In most cases, you will need to specify a configuration set in the emails you send. To learn more about using configuration sets, see Specifying a Configuration Set When You Send Email in the Amazon SES Developer Guide.

Any dedicated IP addresses that you lease that are not part of a dedicated IP pool will automatically be added to a default pool. If you send email without specifying a configuration set that is associated with a pool, then that email will be sent from one of the addresses in the default pool.

Dedicated IP pools are now available in the following AWS Regions: us-west-2 (Oregon), us-east-1 (Virginia), and eu-west-1 (Ireland).

We hope you enjoy this feature. If you have any questions or comments, please leave a comment on this post, or let us know in the Amazon SES Forum.

Thomas and Ed become a RealLifeDoodle on the ISS

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/astro-pi-reallifedoodle/

Thanks to the very talented sooperdavid, creator of some of the wonderful animations known as RealLifeDoodles, Thomas Pesquet and Astro Pi Ed have been turned into one of the cutest videos on the internet.

space pi – Create, Discover and Share Awesome GIFs on Gfycat

Watch space pi GIF by sooperdave on Gfycat. Discover more GIFS online on Gfycat

And RealLifeDoodles aaaaare?

Thanks to the power of viral video, many will be aware of the ongoing Real Life Doodle phenomenon. Wait, you’re not aware?

Oh. Well, let me explain it to you.

Taking often comical video clips, those with a know-how and skill level that outweighs my own in spades add faces and emotions to inanimate objects, creating what the social media world refers to as a Real Life Doodle. From disappointed exercise balls to cannibalistic piles of leaves, these video clips are both cute and sometimes, though thankfully not always, a little heartbreaking.

letmegofree – Create, Discover and Share Awesome GIFs on Gfycat

Watch letmegofree GIF by sooperdave on Gfycat. Discover more reallifedoodles GIFs on Gfycat

Our own RealLifeDoodle

A few months back, when Programme Manager Dave Honess, better known to many as SpaceDave, sent me these Astro Pi videos for me to upload to YouTube, a small plan hatched in my brain. For in the midst of the video, and pointed out to me by SpaceDave – “I kind of love the way he just lets the unit drop out of shot” – was the most adorable sight as poor Ed drifted off into the great unknown of the ISS. Finding that I have this odd ability to consider many inanimate objects as ‘cute’, I wanted to see whether we could turn poor Ed into a RealLifeDoodle.

Heading to the Reddit RealLifeDoodle subreddit, I sent moderator sooperdavid a private message, asking if he’d be so kind as to bring our beloved Ed to life.

Yesterday, our dream came true!

Astro Pi

Unless you’re new to the world of the Raspberry Pi blog (in which case, welcome!), you’ll probably know about the Astro Pi Challenge. But for those who are unaware, let me break it down for you.

Raspberry Pi RealLifeDoodle

In 2015, two weeks before British ESA Astronaut Tim Peake journeyed to the International Space Station, two Raspberry Pis were sent up to await his arrival. Clad in 6063-grade aluminium flight cases and fitted with their own Sense HATs and camera modules, the Astro Pis Ed and Izzy were ready to receive the winning codes from school children in the UK. The following year, this time maintained by French ESA Astronaut Thomas Pesquet, children from every ESA member country got involved to send even more code to the ISS.

Get involved

Will there be another Astro Pi Challenge? Well, I just asked SpaceDave and he didn’t say no! So why not get yourself into training now and try out some of our space-themed free resources, including our 3D-print your own Astro Pi case tutorial? You can also follow the adventures of Ed and Izzy in our brilliant Story of Astro Pi cartoons.

Raspberry Pi RealLifeDoodle

And if you’re quick, there’s still time to take part in tomorrow’s Moonhack! Check out their website for more information and help the team at Code Club Australia beat their own world record!

The post Thomas and Ed become a RealLifeDoodle on the ISS appeared first on Raspberry Pi.

New – AWS SAM Local (Beta) – Build and Test Serverless Applications Locally

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/new-aws-sam-local-beta-build-and-test-serverless-applications-locally/

Today we’re releasing a beta of a new tool, SAM Local, that makes it easy to build and test your serverless applications locally. In this post we’ll use SAM local to build, debug, and deploy a quick application that allows us to vote on tabs or spaces by curling an endpoint. AWS introduced Serverless Application Model (SAM) last year to make it easier for developers to deploy serverless applications. If you’re not already familiar with SAM my colleague Orr wrote a great post on how to use SAM that you can read in about 5 minutes. At it’s core, SAM is a powerful open source specification built on AWS CloudFormation that makes it easy to keep your serverless infrastructure as code – and they have the cutest mascot.

SAM Local takes all the good parts of SAM and brings them to your local machine.

There are a couple of ways to install SAM Local but the easiest is through NPM. A quick npm install -g aws-sam-local should get us going but if you want the latest version you can always install straight from the source: go get github.com/awslabs/aws-sam-local (this will create a binary named aws-sam-local, not sam).

I like to vote on things so let’s write a quick SAM application to vote on Spaces versus Tabs. We’ll use a very simple, but powerful, architecture of API Gateway fronting a Lambda function and we’ll store our results in DynamoDB. In the end a user should be able to curl our API curl https://SOMEURL/ -d '{"vote": "spaces"}' and get back the number of votes.

Let’s start by writing a simple SAM template.yaml:

AWSTemplateFormatVersion : '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  VotesTable:
    Type: "AWS::Serverless::SimpleTable"
  VoteSpacesTabs:
    Type: "AWS::Serverless::Function"
    Properties:
      Runtime: python3.6
      Handler: lambda_function.lambda_handler
      Policies: AmazonDynamoDBFullAccess
      Environment:
        Variables:
          TABLE_NAME: !Ref VotesTable
      Events:
        Vote:
          Type: Api
          Properties:
            Path: /
            Method: post

So we create a [dynamo_i] table that we expose to our Lambda function through an environment variable called TABLE_NAME.

To test that this template is valid I’ll go ahead and call sam validate to make sure I haven’t fat-fingered anything. It returns Valid! so let’s go ahead and get to work on our Lambda function.

import os
import os
import json
import boto3
votes_table = boto3.resource('dynamodb').Table(os.getenv('TABLE_NAME'))

def lambda_handler(event, context):
    print(event)
    if event['httpMethod'] == 'GET':
        resp = votes_table.scan()
        return {'body': json.dumps({item['id']: int(item['votes']) for item in resp['Items']})}
    elif event['httpMethod'] == 'POST':
        try:
            body = json.loads(event['body'])
        except:
            return {'statusCode': 400, 'body': 'malformed json input'}
        if 'vote' not in body:
            return {'statusCode': 400, 'body': 'missing vote in request body'}
        if body['vote'] not in ['spaces', 'tabs']:
            return {'statusCode': 400, 'body': 'vote value must be "spaces" or "tabs"'}

        resp = votes_table.update_item(
            Key={'id': body['vote']},
            UpdateExpression='ADD votes :incr',
            ExpressionAttributeValues={':incr': 1},
            ReturnValues='ALL_NEW'
        )
        return {'body': "{} now has {} votes".format(body['vote'], resp['Attributes']['votes'])}

So let’s test this locally. I’ll need to create a real DynamoDB database to talk to and I’ll need to provide the name of that database through the enviornment variable TABLE_NAME. I could do that with an env.json file or I can just pass it on the command line. First, I can call:
$ echo '{"httpMethod": "POST", "body": "{\"vote\": \"spaces\"}"}' |\
TABLE_NAME="vote-spaces-tabs" sam local invoke "VoteSpacesTabs"

to test the Lambda – it returns the number of votes for spaces so theoritically everything is working. Typing all of that out is a pain so I could generate a sample event with sam local generate-event api and pass that in to the local invocation. Far easier than all of that is just running our API locally. Let’s do that: sam local start-api. Now I can curl my local endpoints to test everything out.
I’ll run the command: $ curl -d '{"vote": "tabs"}' http://127.0.0.1:3000/ and it returns: “tabs now has 12 votes”. Now, of course I did not write this function perfectly on my first try. I edited and saved several times. One of the benefits of hot-reloading is that as I change the function I don’t have to do any additional work to test the new function. This makes iterative development vastly easier.

Let’s say we don’t want to deal with accessing a real DynamoDB database over the network though. What are our options? Well we can download DynamoDB Local and launch it with java -Djava.library.path=./DynamoDBLocal_lib -jar DynamoDBLocal.jar -sharedDb. Then we can have our Lambda function use the AWS_SAM_LOCAL environment variable to make some decisions about how to behave. Let’s modify our function a bit:

import os
import json
import boto3
if os.getenv("AWS_SAM_LOCAL"):
    votes_table = boto3.resource(
        'dynamodb',
        endpoint_url="http://docker.for.mac.localhost:8000/"
    ).Table("spaces-tabs-votes")
else:
    votes_table = boto3.resource('dynamodb').Table(os.getenv('TABLE_NAME'))

Now we’re using a local endpoint to connect to our local database which makes working without wifi a little easier.

SAM local even supports interactive debugging! In Java and Node.js I can just pass the -d flag and a port to immediately enable the debugger. For Python I could use a library like import epdb; epdb.serve() and connect that way. Then we can call sam local invoke -d 8080 "VoteSpacesTabs" and our function will pause execution waiting for you to step through with the debugger.

Alright, I think we’ve got everything working so let’s deploy this!

First I’ll call the sam package command which is just an alias for aws cloudformation package and then I’ll use the result of that command to sam deploy.

$ sam package --template-file template.yaml --s3-bucket MYAWESOMEBUCKET --output-template-file package.yaml
Uploading to 144e47a4a08f8338faae894afe7563c3  90570 / 90570.0  (100.00%)
Successfully packaged artifacts and wrote output template to file package.yaml.
Execute the following command to deploy the packaged template
aws cloudformation deploy --template-file package.yaml --stack-name 
$ sam deploy --template-file package.yaml --stack-name VoteForSpaces --capabilities CAPABILITY_IAM
Waiting for changeset to be created..
Waiting for stack create/update to complete
Successfully created/updated stack - VoteForSpaces

Which brings us to our API:
.

I’m going to hop over into the production stage and add some rate limiting in case you guys start voting a lot – but otherwise we’ve taken our local work and deployed it to the cloud without much effort at all. I always enjoy it when things work on the first deploy!

You can vote now and watch the results live! http://spaces-or-tabs.s3-website-us-east-1.amazonaws.com/

We hope that SAM Local makes it easier for you to test, debug, and deploy your serverless apps. We have a CONTRIBUTING.md guide and we welcome pull requests. Please tweet at us to let us know what cool things you build. You can see our What’s New post here and the documentation is live here.

Randall

Automating Blue/Green Deployments of Infrastructure and Application Code using AMIs, AWS Developer Tools, & Amazon EC2 Systems Manager

Post Syndicated from Ramesh Adabala original https://aws.amazon.com/blogs/devops/bluegreen-infrastructure-application-deployment-blog/

Previous DevOps blog posts have covered the following use cases for infrastructure and application deployment automation:

An AMI provides the information required to launch an instance, which is a virtual server in the cloud. You can use one AMI to launch as many instances as you need. It is security best practice to customize and harden your base AMI with required operating system updates and, if you are using AWS native services for continuous security monitoring and operations, you are strongly encouraged to bake into the base AMI agents such as those for Amazon EC2 Systems Manager (SSM), Amazon Inspector, CodeDeploy, and CloudWatch Logs. A customized and hardened AMI is often referred to as a “golden AMI.” The use of golden AMIs to create EC2 instances in your AWS environment allows for fast and stable application deployment and scaling, secure application stack upgrades, and versioning.

In this post, using the DevOps automation capabilities of Systems Manager, AWS developer tools (CodePipeLine, CodeDeploy, CodeCommit, CodeBuild), I will show you how to use AWS CodePipeline to orchestrate the end-to-end blue/green deployments of a golden AMI and application code. Systems Manager Automation is a powerful security feature for enterprises that want to mature their DevSecOps practices.

Here are the high-level phases and primary services covered in this use case:

 

You can access the source code for the sample used in this post here: https://github.com/awslabs/automating-governance-sample/tree/master/Bluegreen-AMI-Application-Deployment-blog.

This sample will create a pipeline in AWS CodePipeline with the building blocks to support the blue/green deployments of infrastructure and application. The sample includes a custom Lambda step in the pipeline to execute Systems Manager Automation to build a golden AMI and update the Auto Scaling group with the golden AMI ID for every rollout of new application code. This guarantees that every new application deployment is on a fully patched and customized AMI in a continuous integration and deployment model. This enables the automation of hardened AMI deployment with every new version of application deployment.

 

 

We will build and run this sample in three parts.

Part 1: Setting up the AWS developer tools and deploying a base web application

Part 1 of the AWS CloudFormation template creates the initial Java-based web application environment in a VPC. It also creates all the required components of Systems Manager Automation, CodeCommit, CodeBuild, and CodeDeploy to support the blue/green deployments of the infrastructure and application resulting from ongoing code releases.

Part 1 of the AWS CloudFormation stack creates these resources:

After Part 1 of the AWS CloudFormation stack creation is complete, go to the Outputs tab and click the Elastic Load Balancing link. You will see the following home page for the base web application:

Make sure you have all the outputs from the Part 1 stack handy. You need to supply them as parameters in Part 3 of the stack.

Part 2: Setting up your CodeCommit repository

In this part, you will commit and push your sample application code into the CodeCommit repository created in Part 1. To access the initial git commands to clone the empty repository to your local machine, click Connect to go to the AWS CodeCommit console. Make sure you have the IAM permissions required to access AWS CodeCommit from command line interface (CLI).

After you’ve cloned the repository locally, download the sample application files from the part2 folder of the Git repository and place the files directly into your local repository. Do not include the aws-codedeploy-sample-tomcat folder. Go to the local directory and type the following commands to commit and push the files to the CodeCommit repository:

git add .
git commit -a -m "add all files from the AWS Java Tomcat CodeDeploy application"
git push

After all the files are pushed successfully, the repository should look like this:

 

Part 3: Setting up CodePipeline to enable blue/green deployments     

Part 3 of the AWS CloudFormation template creates the pipeline in AWS CodePipeline and all the required components.

a) Source: The pipeline is triggered by any change to the CodeCommit repository.

b) BuildGoldenAMI: This Lambda step executes the Systems Manager Automation document to build the golden AMI. After the golden AMI is successfully created, a new launch configuration with the new AMI details will be updated into the Auto Scaling group of the application deployment group. You can watch the progress of the automation in the EC2 console from the Systems Manager –> Automations menu.

c) Build: This step uses the application build spec file to build the application build artifact. Here are the CodeBuild execution steps and their status:

d) Deploy: This step clones the Auto Scaling group, launches the new instances with the new AMI, deploys the application changes, reroutes the traffic from the elastic load balancer to the new instances and terminates the old Auto Scaling group. You can see the execution steps and their status in the CodeDeploy console.

After the CodePipeline execution is complete, you can access the application by clicking the Elastic Load Balancing link. You can find it in the output of Part 1 of the AWS CloudFormation template. Any consecutive commits to the application code in the CodeCommit repository trigger the pipelines and deploy the infrastructure and code with an updated AMI and code.

 

If you have feedback about this post, add it to the Comments section below. If you have questions about implementing the example used in this post, open a thread on the Developer Tools forum.


About the author

 

Ramesh Adabala is a Solutions Architect in Southeast Enterprise Solution Architecture team at Amazon Web Services.

Deploy a Data Warehouse Quickly with Amazon Redshift, Amazon RDS for PostgreSQL and Tableau Server

Post Syndicated from Jorge A. Lopez original https://aws.amazon.com/blogs/big-data/deploy-a-data-warehouse-quickly-with-amazon-redshift-amazon-rds-for-postgresql-and-tableau-server/

One of the benefits of a data warehouse environment using both Amazon Redshift and Amazon RDS for PostgreSQL is that you can leverage the advantages of each service. Amazon Redshift is a high performance, petabyte-scale data warehouse service optimized for the online analytical processing (OLAP) queries typical of analytic reporting and business intelligence applications. On the other hand, a service like RDS excels at transactional OLTP workloads such as inserting, deleting, or updating rows.

In the recent JOIN Amazon Redshift AND Amazon RDS PostgreSQL WITH dblink post, we showed how you can deploy such an environment. Now, you can deploy a similar architecture using the Modern Data Warehouse on AWS Quick Start. The Quick Start is an automated deployment that uses AWS CloudFormation templates to launch, configure, and run the services required to deploy a data warehousing environment on AWS, based on Amazon Redshift and RDS for PostgreSQL.

The Quick Start also includes an instance of Tableau Server, running on Amazon EC2. This gives you the ability to host and serve analytic dashboards, workbooks and visualizations, supported by a trial license. You can play with the sample data source and dashboard, or create your own analyses by uploading your own data sets.

For more information about the Modern Data Warehouse on AWS Quick Start, download the full deployment guide. If you’re ready to get started, use one of the buttons below:

Option 1: Deploy Quick Start into a new VPC on AWS

Option 2: Deploy Quick Start into an existing VPC

If you have questions, please leave a comment below.


Next Steps

You can also join us for the webinar Unlock Insights and Reduce Costs by Modernizing Your Data Warehouse on AWS on Tuesday, August 22, 2017. Pearson, the education and publishing company, will present best practices and lessons learned during their journey to Amazon Redshift and Tableau.

Огромен /_ _ _/ се задава

Post Syndicated from nellyo original https://nellyo.wordpress.com/2017/08/05/k_pavlov/

Огромен /_ _ _/ се задава

Ако целият този Огромен /_ _ _/,
който ни предстои…
Искам да кажа –
ако този Огромен /_ _ _/,
успея да го разчленя на отделни буквички…
Може би…
Може би ще успеем да се спасим.
А?

Ами след като разчленя на отделни
буквички –
/_ _ _/, имам предвид –
ако всяка отделна буквичка
от Огромния /_ _ _/,
изведнъж…
А?

Някои думи не изговарям нарочно –
убивам ги чрез премълчаване.
Или въобще не ги раждам.
Най-много да ги обознача с тирета.
Ето така: /_ _ _/.
Страх ме е, че ако бъдат произнесени,
може да се материализират в /_ _ _/.
Затова често ме обвиняват в неяснота.
Не знаят, че го правя за тяхно добро.
Имам предвид доброто на обвинителите ми.
И всички нас имам предвид.
Нещо като магия срещу /_ _ _/.
Защото, ако оживеят някои думички
като, например, /_ _ _/…
Край.


Зад тези тирета се крие думата “ужас”.

Константин Павлов

Повече от Константин Павлов

Filed under: Uncategorized

Turbocharge your Apache Hive queries on Amazon EMR using LLAP

Post Syndicated from Jigar Mistry original https://aws.amazon.com/blogs/big-data/turbocharge-your-apache-hive-queries-on-amazon-emr-using-llap/

Apache Hive is one of the most popular tools for analyzing large datasets stored in a Hadoop cluster using SQL. Data analysts and scientists use Hive to query, summarize, explore, and analyze big data.

With the introduction of Hive LLAP (Low Latency Analytical Processing), the notion of Hive being just a batch processing tool has changed. LLAP uses long-lived daemons with intelligent in-memory caching to circumvent batch-oriented latency and provide sub-second query response times.

This post provides an overview of Hive LLAP, including its architecture and common use cases for boosting query performance. You will learn how to install and configure Hive LLAP on an Amazon EMR cluster and run queries on LLAP daemons.

What is Hive LLAP?

Hive LLAP was introduced in Apache Hive 2.0, which provides very fast processing of queries. It uses persistent daemons that are deployed on a Hadoop YARN cluster using Apache Slider. These daemons are long-running and provide functionality such as I/O with DataNode, in-memory caching, query processing, and fine-grained access control. And since the daemons are always running in the cluster, it saves substantial overhead of launching new YARN containers for every new Hive session, thereby avoiding long startup times.

When Hive is configured in hybrid execution mode, small and short queries execute directly on LLAP daemons. Heavy lifting (like large shuffles in the reduce stage) is performed in YARN containers that belong to the application. Resources (CPU, memory, etc.) are obtained in a traditional fashion using YARN. After the resources are obtained, the execution engine can decide which resources are to be allocated to LLAP, or it can launch Apache Tez processors in separate YARN containers. You can also configure Hive to run all the processing workloads on LLAP daemons for querying small datasets at lightning fast speeds.

LLAP daemons are launched under YARN management to ensure that the nodes don’t get overloaded with the compute resources of these daemons. You can use scheduling queues to make sure that there is enough compute capacity for other YARN applications to run.

Why use Hive LLAP?

With many options available in the market (Presto, Spark SQL, etc.) for doing interactive SQL  over data that is stored in Amazon S3 and HDFS, there are several reasons why using Hive and LLAP might be a good choice:

  • For those who are heavily invested in the Hive ecosystem and have external BI tools that connect to Hive over JDBC/ODBC connections, LLAP plugs in to their existing architecture without a steep learning curve.
  • It’s compatible with existing Hive SQL and other Hive tools, like HiveServer2, and JDBC drivers for Hive.
  • It has native support for security features with authentication and authorization (SQL standards-based authorization) using HiveServer2.
  • LLAP daemons are aware about of the columns and records that are being processed which enables you to enforce fine-grained access control.
  • It can use Hive’s vectorization capabilities to speed up queries, and Hive has better support for Parquet file format when vectorization is enabled.
  • It can take advantage of a number of Hive optimizations like merging multiple small files for query results, automatically determining the number of reducers for joins and groupbys, etc.
  • It’s optional and modular so it can be turned on or off depending on the compute and resource requirements of the cluster. This lets you to run other YARN applications concurrently without reserving a cluster specifically for LLAP.

How do you install Hive LLAP in Amazon EMR?

To install and configure LLAP on an EMR cluster, use the following bootstrap action (BA):

s3://aws-bigdata-blog/artifacts/Turbocharge_Apache_Hive_on_EMR/configure-Hive-LLAP.sh

This BA downloads and installs Apache Slider on the cluster and configures LLAP so that it works with EMR Hive. For LLAP to work, the EMR cluster must have Hive, Tez, and Apache Zookeeper installed.

You can pass the following arguments to the BA.

Argument Definition Default value
--instances Number of instances of LLAP daemon Number of core/task nodes of the cluster
--cache Cache size per instance 20% of physical memory of the node
--executors Number of executors per instance Number of CPU cores of the node
--iothreads Number of IO threads per instance Number of CPU cores of the node
--size Container size per instance 50% of physical memory of the node
--xmx Working memory size 50% of container size
--log-level Log levels for the LLAP instance INFO

LLAP example

This section describes how you can try the faster Hive queries with LLAP using the TPC-DS testbench for Hive on Amazon EMR.

Use the following AWS command line interface (AWS CLI) command to launch a 1+3 nodes m4.xlarge EMR 5.6.0 cluster with the bootstrap action to install LLAP:

aws emr create-cluster --release-label emr-5.6.0 \
--applications Name=Hadoop Name=Hive Name=Hue Name=ZooKeeper Name=Tez \
--bootstrap-actions '[{"Path":"s3://aws-bigdata-blog/artifacts/Turbocharge_Apache_Hive_on_EMR/configure-Hive-LLAP.sh","Name":"Custom action"}]' \ 
--ec2-attributes '{"KeyName":"<YOUR-KEY-PAIR>","InstanceProfile":"EMR_EC2_DefaultRole","SubnetId":"subnet-xxxxxxxx","EmrManagedSlaveSecurityGroup":"sg-xxxxxxxx","EmrManagedMasterSecurityGroup":"sg-xxxxxxxx"}' 
--service-role EMR_DefaultRole \
--enable-debugging \
--log-uri 's3n://<YOUR-BUCKET/' --name 'test-hive-llap' \
--instance-groups '[{"InstanceCount":1,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB":32,"VolumeType":"gp2"},"VolumesPerInstance":1}],"EbsOptimized":true},"InstanceGroupType":"MASTER","InstanceType":"m4.xlarge","Name":"Master - 1"},{"InstanceCount":3,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB":32,"VolumeType":"gp2"},"VolumesPerInstance":1}],"EbsOptimized":true},"InstanceGroupType":"CORE","InstanceType":"m4.xlarge","Name":"Core - 2"}]' 
--region us-east-1

After the cluster is launched, log in to the master node using SSH, and do the following:

  1. Open the hive-tpcds folder:
    cd /home/hadoop/hive-tpcds/
  2. Start Hive CLI using the testbench configuration, create the required tables, and run the sample query:

    hive –i testbench.settings
    hive> source create_tables.sql;
    hive> source query55.sql;

    This sample query runs on a 40 GB dataset that is stored on Amazon S3. The dataset is generated using the data generation tool in the TPC-DS testbench for Hive.It results in output like the following:
  3. This screenshot shows that the query finished in about 47 seconds for LLAP mode. Now, to compare this to the execution time without LLAP, you can run the same workload using only Tez containers:
    hive> set hive.llap.execution.mode=none;
    hive> source query55.sql;


    This query finished in about 80 seconds.

The difference in query execution time is almost 1.7 times when using just YARN containers in contrast to running the query on LLAP daemons. And with every rerun of the query, you notice that the execution time substantially decreases by the virtue of in-memory caching by LLAP daemons.

Conclusion

In this post, I introduced Hive LLAP as a way to boost Hive query performance. I discussed its architecture and described several use cases for the component. I showed how you can install and configure Hive LLAP on an Amazon EMR cluster and how you can run queries on LLAP daemons.

If you have questions about using Hive LLAP on Amazon EMR or would like to share your use cases, please leave a comment below.


Additional Reading

Learn how to to automatically partition Hive external tables with AWS.


About the Author

Jigar Mistry is a Hadoop Systems Engineer with Amazon Web Services. He works with customers to provide them architectural guidance and technical support for processing large datasets in the cloud using open-source applications. In his spare time, he enjoys going for camping and exploring different restaurants in the Seattle area.

 

 

 

 

Amazon QuickSight Now Supports Amazon Athena in EU (Ireland), Count Distinct, and Week Aggregation

Post Syndicated from Luis Wang original https://aws.amazon.com/blogs/big-data/amazon-quicksight-now-supports-amazon-athena-in-eu-ireland-count-distinct-and-week-aggregation/

Today, I’m excited to share a couple of new features in Amazon QuickSight. First, with this release, we expanded connectivity options by adding Amazon Athena support in the EU (Ireland) Region. Additionally, you can now use Count Distinct on your dimensions and metrics in the visualizations and aggregate date fields by week for SPICE data sets.

Athena in Ireland

Athena is one of the most popular data sources used by QuickSight customers. It allows you to deploy a serverless BI and analytics architecture for your operational and business data. With this release, the Athena connector is now available in the EU (Ireland) Region. You can connect QuickSight to your Athena databases and tables in the region and start visualizing your data in a matter of seconds.

Count Distinct

You can now perform aggregations using Count Distinct in the visualizations, one of the top requests from users. To use Count Distinct, simply select Count Distinct as the aggregation on the visual axis or in the field well. Count Distinct is supported for both direct queries and SPICE data sets. You can apply it to strings and measures. It is available for all supported visualization types.

Date aggregation by week

Time series line charts are one of the most common ways for customers to report on business trends. In addition to Year, Month, Day and Hour, you can now aggregate date fields by WEEK and visualize your data at a weekly granularity.

Learn more

To learn more about these capabilities and start using them in your dashboards, see the QuickSight User Guide.

Stay engaged

If you have questions or suggestions, you can post them on the QuickSight Discussion Forum.

Not a QuickSight user?

To get started for FREE, see quicksight.aws.

 

AWS CloudFormation Supports Amazon Kinesis Analytics Applications

Post Syndicated from Ryan Nienhuis original https://aws.amazon.com/blogs/big-data/aws-cloudformation-supports-amazon-kinesis-analytics-applications/

You can now provision and manage resources for Amazon Kinesis Analytics applications using AWS CloudFormation.  Kinesis Analytics is the easiest way to process streaming data in real time with standard SQL, without having to learn new programming languages or processing frameworks. Kinesis Analytics enables you to query streaming data or build entire streaming applications using SQL. Using the service, you gain actionable insights and can respond to your business and customer needs promptly.

Customers can create CloudFormation templates that easily create or update Kinesis Analytics applications. Typically, a template is used as a way to manage code across different environments, or to prototype a new streaming data solution quickly.

We have created two sample templates using past AWS Big Data Blog posts that referenced Kinesis Analytics.

For more information about the new feature, see the AWS Cloudformation User Guide.

 

Run Common Data Science Packages on Anaconda and Oozie with Amazon EMR

Post Syndicated from John Ohle original https://aws.amazon.com/blogs/big-data/run-common-data-science-packages-on-anaconda-and-oozie-with-amazon-emr/

In the world of data science, users must often sacrifice cluster set-up time to allow for complex usability scenarios. Amazon EMR allows data scientists to spin up complex cluster configurations easily, and to be up and running with complex queries in a matter of minutes.

Data scientists often use scheduling applications such as Oozie to run jobs overnight. However, Oozie can be difficult to configure when you are trying to use popular Python packages (such as “pandas,” “numpy,” and “statsmodels”), which are not included by default.

One such popular platform that contains these types of packages (and more) is Anaconda. This post focuses on setting up an Anaconda platform on EMR, with an intent to use its packages with Oozie. I describe how to run jobs using a popular open source scheduler like Oozie.

Walkthrough

For this post, you walk through the following tasks:

  • Create an EMR cluster.
  • Download Anaconda on your master node.
  • Configure Oozie.
  • Test the steps.

Create an EMR cluster

Spin up an Amazon EMR cluster using the console or the AWS CLI. Use the latest release, and include Apache Hadoop, Apache Spark, Apache Hive, and Oozie.

To create a three-node cluster in the us-east-1 region, issue an AWS CLI command such as the following. This command must be typed as one line, as shown below. It is shown here separated for readability purposes only.

aws emr create-cluster \ 
--release-label emr-5.7.0 \ 
 --name '<YOUR-CLUSTER-NAME>' \
 --applications Name=Hadoop Name=Oozie Name=Spark Name=Hive \ 
 --ec2-attributes '{"KeyName":"<YOUR-KEY-PAIR>","SubnetId":"<YOUR-SUBNET-ID>","EmrManagedSlaveSecurityGroup":"<YOUR-EMR-SLAVE-SECURITY-GROUP>","EmrManagedMasterSecurityGroup":"<YOUR-EMR-MASTER-SECURITY-GROUP>"}' \ 
 --use-default-roles \ 
 --instance-groups '[{"InstanceCount":1,"InstanceGroupType":"MASTER","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Master - 1"},{"InstanceCount":<YOUR-CORE-INSTANCE-COUNT>,"InstanceGroupType":"CORE","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Core - 2"}]'

One-line version for reference:

aws emr create-cluster --release-label emr-5.7.0 --name '<YOUR-CLUSTER-NAME>' --applications Name=Hadoop Name=Oozie Name=Spark Name=Hive --ec2-attributes '{"KeyName":"<YOUR-KEY-PAIR>","SubnetId":"<YOUR-SUBNET-ID>","EmrManagedSlaveSecurityGroup":"<YOUR-EMR-SLAVE-SECURITY-GROUP>","EmrManagedMasterSecurityGroup":"<YOUR-EMR-MASTER-SECURITY-GROUP>"}' --use-default-roles --instance-groups '[{"InstanceCount":1,"InstanceGroupType":"MASTER","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Master - 1"},{"InstanceCount":<YOUR-CORE-INSTANCE-COUNT>,"InstanceGroupType":"CORE","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Core - 2"}]'

Download Anaconda

SSH into your EMR master node instance and download the official Anaconda installer:

wget https://repo.continuum.io/archive/Anaconda2-4.4.0-Linux-x86_64.sh

At the time of publication, Anaconda 4.4 is the most current version available. For the download link location for the latest Python 2.7 version (Python 3.6 may encounter issues), see https://www.continuum.io/downloads.  Open the context (right-click) menu for the Python 2.7 download link, choose Copy Link Location, and use this value in the previous wget command.

This post used the Anaconda 4.4 installation. If you have a later version, it is shown in the downloaded filename:  “anaconda2-<version number>-Linux-x86_64.sh”.

Run this downloaded script and follow the on-screen installer prompts.

chmod u+x Anaconda2-4.4.0-Linux-x86_64.sh
./Anaconda2-4.4.0-Linux-x86_64.sh

For an installation directory, select somewhere with enough space on your cluster, such as “/mnt/anaconda/”.

The process should take approximately 1–2 minutes to install. When prompted if you “wish the installer to prepend the Anaconda2 install location”, select the default option of [no].

After you are done, export the PATH to include this new Anaconda installation:

export PATH=/mnt/anaconda/bin:$PATH

Zip up the Anaconda installation:

cd /mnt/anaconda/
zip -r anaconda.zip .

The zip process may take 4–5 minutes to complete.

(Optional) Upload this anaconda.zip file to your S3 bucket for easier inclusion into future EMR clusters. This removes the need to repeat the previous steps for future EMR clusters.

Configure Oozie

Next, you configure Oozie to use Pyspark and the Anaconda platform.

Get the location of your Oozie sharelibupdate folder. Issue the following command and take note of the “sharelibDirNew” value:

oozie admin -sharelibupdate

For this post, this value is “hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136”.

Pass in the required Pyspark files into Oozies sharelibupdate location. The following files are required for Oozie to be able to run Pyspark commands:

  • pyspark.zip
  • py4j-0.10.4-src.zip

These are located on the EMR master instance in the location “/usr/lib/spark/python/lib/”, and must be put into the Oozie sharelib spark directory. This location is the value of the sharelibDirNew parameter value (shown above) with “/spark/” appended, that is, “hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136/spark/”.

To do this, issue the following commands:

hdfs dfs -put /usr/lib/spark/python/lib/py4j-0.10.4-src.zip hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136/spark/
hdfs dfs -put /usr/lib/spark/python/lib/pyspark.zip hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136/spark/

After you’re done, Oozie can use Pyspark in its processes.

Pass the anaconda.zip file into HDFS as follows:

hdfs dfs -put /mnt/anaconda/anaconda.zip /tmp/myLocation/anaconda.zip

(Optional) Verify that it was transferred successfully with the following command:

hdfs dfs -ls /tmp/myLocation/

On your master node, execute the following command:

export PYSPARK_PYTHON=/mnt/anaconda/bin/python

Set the PYSPARK_PYTHON environment variable on the executor nodes. Put the following configurations in your “spark-opts” values in your Oozie workflow.xml file:

–conf spark.executorEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python
–conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python

This is referenced from the Oozie job in the following line in your workflow.xml file, also included as part of your “spark-opts”:

--archives hdfs:///tmp/myLocation/anaconda.zip#anaconda_remote

Your Oozie workflow.xml file should now look something like the following:

<workflow-app name="spark-wf" xmlns="uri:oozie:workflow:0.5">
<start to="start_spark" />
<action name="start_spark">
    <spark xmlns="uri:oozie:spark-action:0.1">
        <job-tracker>${jobTracker}</job-tracker>
        <name-node>${nameNode}</name-node>
        <prepare>
            <delete path="/tmp/test/spark_oozie_test_out3"/>
        </prepare>
        <master>yarn-cluster</master>
        <mode>cluster</mode>
        <name>SparkJob</name>
        <class>clear</class>
        <jar>hdfs:///user/oozie/apps/myPysparkProgram.py</jar>
        <spark-opts>--queue default
            --conf spark.ui.view.acls=*
            --executor-memory 2G --num-executors 2 --executor-cores 2 --driver-memory 3g
            --conf spark.executorEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python
            --conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python
            --archives hdfs:///tmp/myLocation/anaconda.zip#anaconda_remote
        </spark-opts>
    </spark>
    <ok to="end"/>
    <error to="kill"/>
</action>
        <kill name="kill">
                <message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
        </kill>
        <end name="end"/>
</workflow-app>

Test steps

To test this out, you can use the following job.properties and myPysparkProgram.py file, along with the following steps:

job.properties

masterNode ip-xxx-xxx-xxx-xxx.us-east-1.compute.internal
nameNode hdfs://${masterNode}:8020
jobTracker ${masterNode}:8032
master yarn
mode cluster
queueName default
oozie.libpath ${nameNode}/user/oozie/share/lib
oozie.use.system.libpath true
oozie.wf.application.path ${nameNode}/user/oozie/apps/

Note: You can get your master node IP address (denoted as “ip-xxx-xxx-xxx-xxx” here) from the value for the sharelibDirNew parameter noted earlier.

myPysparkProgram.py

from pyspark import SparkContext, SparkConf
import numpy
import sys

conf = SparkConf().setAppName('myPysparkProgram')
sc = SparkContext(conf=conf)

rdd = sc.textFile("/user/hadoop/input.txt")

x = numpy.sum([3,4,5]) #total = 12

rdd = rdd.map(lambda line: line + str(x))
rdd.saveAsTextFile("/user/hadoop/output")

Put the “myPysparkProgram.py” into the location mentioned between the “<jar>xxxxx</jar>” tags in your workflow.xml. In this example, the location is “hdfs:///user/oozie/apps/”. Use the following command to move the “myPysparkProgram.py” file to the correct location:

hdfs dfs -put myPysparkProgram.py /user/oozie/apps/

Put the above workflow.xml file into the “/user/oozie/apps/” location in hdfs:

hdfs dfs –put workflow.xml /user/oozie/apps/

Note: The job.properties file is run locally from the EMR master node.

Create a sample input.txt file with some data in it. For example:

input.txt

This is a sentence.
So is this. 
This is also a sentence.

Put this file into hdfs:

hdfs dfs -put input.txt /user/hadoop/

Execute the job in Oozie with the following command. This creates an Oozie job ID.

oozie job -config job.properties -run

You can check the Oozie job state with the command:

oozie job -info <Oozie job ID>

  1. When the job is successfully finished, the results are located at:
/user/hadoop/output/part-00000
/user/hadoop/output/part-00001

  1. Run the following commands to view the output:
hdfs dfs -cat /user/hadoop/output/part-00000
hdfs dfs -cat /user/hadoop/output/part-00001

The output will be:

This is a sentence. 12
So is this 12
This is also a sentence 12

Summary

The myPysparkProgram.py has successfully imported the numpy library from the Anaconda platform and has produced some output with it. If you tried to run this using standard Python, you’d encounter the following error:

Now when your Python job runs in Oozie, any imported packages that are implicitly imported by your Pyspark script are imported into your job within Oozie directly from the Anaconda platform. Simple!

If you have questions or suggestions, please leave a comment below.


Additional Reading

Learn how to use Apache Oozie workflows to automate Apache Spark jobs on Amazon EMR.

 


About the Author

John Ohle is an AWS BigData Cloud Support Engineer II for the BigData team in Dublin. He works to provide advice and solutions to our customers on their Big Data projects and workflows on AWS. In his spare time, he likes to play music, learn, develop tools and write documentation to further help others – both colleagues and customers alike.

 

 

 

New AWS Training: Building a Serverless Data Lake

Post Syndicated from Sara Snedeker original https://aws.amazon.com/blogs/big-data/new-aws-training-building-a-serverless-data-lake/

AWS Training allows you to learn from the experts so that you can advance your knowledge with practical skills and get more out of the AWS Cloud. We are adding one of our most popular event boot camps, Building a Serverless Data Lake, to our permanent instructor-led training portfolio.

This one-day course is designed to teach you how to design, build, and operate a serverless data lake solution with AWS services. We cover topics such as ingesting data from any data source at large scale, storing the data securely and durably, enabling the capability to use the right tool to process large volumes of data, and understanding the options available for analyzing the data in near-real time.

This course is intended for solution architects, big data developers, data architects and analysts, and other hands-on data analysis practitioners.

You can explore our complete course catalog, or search for a public class near you. You can also request a private onsite training for your team by contacting AWS Training.

 

Amazon Redshift Spectrum Extends Data Warehousing Out to Exabytes—No Loading Required

Post Syndicated from Maor Kleider original https://aws.amazon.com/blogs/big-data/amazon-redshift-spectrum-extends-data-warehousing-out-to-exabytes-no-loading-required/

When we first looked into the possibility of building a cloud-based data warehouse many years ago, we were struck by the fact that our customers were storing ever-increasing amounts of data, and yet only a small fraction of that data ever made it into a data warehouse or Hadoop system for analysis. We saw that this wasn’t just a cloud-specific anomaly. It was also true in the broader industry, where the growth rate of the enterprise storage market segment greatly surpassed that of the data warehousing market segment.

We dubbed this the “dark data” problem. Our customers knew that there was untapped value in the data they collected; why else would they spend money to store it? But the systems available to them to analyze this data were simply too slow, complex, and expensive for them to use on all but a select subset of this data. They were storing it with optimistic hope that, someday, someone would find a solution.

Amazon Redshift became one of the fastest-growing AWS services because it helped solve the dark data problem. It was at least an order of magnitude less expensive and faster than most alternatives available. And Amazon Redshift was fully managed from the start—you didn’t have to worry about capacity, provisioning, patching, monitoring, backups, and a host of other DBA headaches. Many customers, including Vevo, Yelp, Redfin, and Edmunds, migrated to Amazon Redshift to improve query performance, reduce DBA overhead, and lower the cost of analytics.

And our customers’ data continues to grow at a very fast rate. Across the board, gigabytes to petabytes, the average Amazon Redshift customer doubles the data analyzed every year. That’s why we implement features that help customers handle their growing data, for example to double the query throughput or improve the compression ratios from 3x to 4x. That gives our customers some time before they have to consider throwing away data or removing it from their analytic environments. However, there is an increasing number of AWS customers who each generate a petabyte of data every day—that’s an exabyte in only three years. There wasn’t a solution for customers like that. If your data is doubling every year, it’s not long before you have to find new, disruptive approaches that transform the cost, performance, and simplicity curves for managing data.

Let’s look at the options available today. You can use Hadoop-based technologies like Apache Hive with Amazon EMR. This is actually a pretty great solution because it makes it easy and cost-effective to operate directly on data in Amazon S3 without ingestion or transformation. You can spin up clusters as you wish when you need, and size them right for that specific job you’re running. These systems are great at high scale-out processing like scans, filters, and aggregates. On the other hand, they’re not that good at complex query processing. For example, join processing requires data to be shuffled across nodes—for a large amount of data and large numbers of nodes that gets very slow. And joins are intrinsic to any meaningful analytics problem.

You can also use a columnar MPP data warehouse like Amazon Redshift. These systems make it simple to run complex analytic queries with orders of magnitude faster performance for joins and aggregations performed over large datasets. Amazon Redshift, in particular, leverages high-performance local disks, sophisticated query execution. and join-optimized data formats. Because it is just standard SQL, you can keep using your existing ETL and BI tools. But you do have to load data, and you have to provision clusters against the storage and CPU requirements you need.

Both solutions have powerful attributes, but they force you to choose which attributes you want. We see this as a “tyranny of OR.” You can have the throughput of local disks OR the scale of Amazon S3. You can have sophisticated query optimization OR high-scale data processing. You can have fast join performance with optimized formats OR a range of data processing engines that work against common data formats. But you shouldn’t have to choose. At this scale, you really can’t afford to choose. You need “all of the above.”

Redshift Spectrum

We built Redshift Spectrum to end this “tyranny of OR.” With Redshift Spectrum, Amazon Redshift customers can easily query their data in Amazon S3. Like Amazon EMR, you get the benefits of open data formats and inexpensive storage, and you can scale out to thousands of nodes to pull data, filter, project, aggregate, group, and sort. Like Amazon Athena, Redshift Spectrum is serverless and there’s nothing to provision or manage. You just pay for the resources you consume for the duration of your Redshift Spectrum query. Like Amazon Redshift itself, you get the benefits of a sophisticated query optimizer, fast access to data on local disks, and standard SQL. And like nothing else, Redshift Spectrum can execute highly sophisticated queries against an exabyte of data or more—in just minutes.

Redshift Spectrum is a built-in feature of Amazon Redshift, and your existing queries and BI tools will continue to work seamlessly. Under the covers, we manage a fleet of thousands of Redshift Spectrum nodes spread across multiple Availability Zones. These are transparently scaled and allocated to your queries based on the data that you need to process, with no provisioning or commitments. Redshift Spectrum is also highly concurrent—you can access your Amazon S3 data from any number of Amazon Redshift clusters.

The life of a Redshift Spectrum query

It all starts when Redshift Spectrum queries are submitted to the leader node of your Amazon Redshift cluster. The leader node optimizes, compiles, and pushes the query execution to the compute nodes in your Amazon Redshift cluster. Next, the compute nodes obtain the information describing the external tables from your data catalog, dynamically pruning nonrelevant partitions based on the filters and joins in your queries. The compute nodes also examine the data available locally and push down predicates to efficiently scan only the relevant objects in Amazon S3.

The Amazon Redshift compute nodes then generate multiple requests depending on the number of objects that need to be processed, and submit them concurrently to Redshift Spectrum, which pools thousands of Amazon EC2 instances per AWS Region. The Redshift Spectrum worker nodes scan, filter, and aggregate your data from Amazon S3, streaming required data for processing back to your Amazon Redshift cluster. Then, the final join and merge operations are performed locally in your cluster and the results are returned to your client.

Redshift Spectrum’s architecture offers several advantages. First, it elastically scales compute resources separately from the storage layer in Amazon S3. Second, it offers significantly higher concurrency because you can run multiple Amazon Redshift clusters and query the same data in Amazon S3. Third, Redshift Spectrum leverages the Amazon Redshift query optimizer to generate efficient query plans, even for complex queries with multi-table joins and window functions. Fourth, it operates directly on your source data in its native format (Parquet, RCFile, CSV, TSV, Sequence, Avro, RegexSerDe and more to come soon). This means that no data loading or transformation is needed. This also eliminates data duplication and associated costs. Fifth, operating on open data formats gives you the flexibility to leverage other AWS services and execution engines across your various teams to collaborate on the same data in Amazon S3. You get all of this, and because Redshift Spectrum is a feature of Amazon Redshift, you get the same level of end-to-end security, compliance, and certifications as with Amazon Redshift.

Designed for performance and cost-effectiveness

With Amazon Redshift Spectrum, you pay only for the queries you run against the data that you actually scan. We encourage you to leverage 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 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, Redshift Spectrum scans only the columns needed by your query, rather than processing entire rows. Similarly, if you compress your data using one of Redshift Spectrum’s supported compression algorithms, less data is scanned.

Amazon Redshift and Redshift Spectrum give you the best of both worlds. If you need to run frequent queries on the same data, you can normalize it, store it in Amazon Redshift, and get all of the benefits of a fully featured data warehouse for storing and querying structured data at a flat rate. At the same time, you can keep your additional data in multiple open file formats in Amazon S3, whether it is historical data or the most recent data, and extend your Amazon Redshift queries across your Amazon S3 data lake.

And that is how Amazon Redshift Spectrum scales data warehousing to exabytes—with no loading required. Redshift Spectrum ends the “tyranny of OR,” enabling you to store your data where you want, in the format you want, and have it available for fast processing using standard SQL when you need it, now and in the future.


Additional Reading

10 Best Practices for Amazon Redshift Spectrum
Amazon QuickSight Adds Support for Amazon Redshift Spectrum
Amazon Redshift Spectrum – Exabyte-Scale In-Place Queries of S3 Data

 


 

About the Author

Maor Kleider is a Senior Product Manager for Amazon Redshift, a fast, simple and cost-effective data warehouse. Maor is passionate about collaborating with customers and partners, learning about their unique big data use cases and making their experience even better. In his spare time, Maor enjoys traveling and exploring new restaurants with his family.

 

 

 

Analyze OpenFDA Data in R with Amazon S3 and Amazon Athena

Post Syndicated from Ryan Hood original https://aws.amazon.com/blogs/big-data/analyze-openfda-data-in-r-with-amazon-s3-and-amazon-athena/

One of the great benefits of Amazon S3 is the ability to host, share, or consume public data sets. This provides transparency into data to which an external data scientist or developer might not normally have access. By exposing the data to the public, you can glean many insights that would have been difficult with a data silo.

The openFDA project creates easy access to the high value, high priority, and public access data of the Food and Drug Administration (FDA). The data has been formatted and documented in consumer-friendly standards. Critical data related to drugs, devices, and food has been harmonized and can easily be called by application developers and researchers via API calls. OpenFDA has published two whitepapers that drill into the technical underpinnings of the API infrastructure as well as how to properly analyze the data in R. In addition, FDA makes openFDA data available on S3 in raw format.

In this post, I show how to use S3, Amazon EMR, and Amazon Athena to analyze the drug adverse events dataset. A drug adverse event is an undesirable experience associated with the use of a drug, including serious drug side effects, product use errors, product quality programs, and therapeutic failures.

Data considerations

Keep in mind that this data does have limitations. In addition, in the United States, these adverse events are submitted to the FDA voluntarily from consumers so there may not be reports for all events that occurred. There is no certainty that the reported event was actually due to the product. The FDA does not require that a causal relationship between a product and event be proven, and reports do not always contain the detail necessary to evaluate an event. Because of this, there is no way to identify the true number of events. The important takeaway to all this is that the information contained in this data has not been verified to produce cause and effect relationships. Despite this disclaimer, many interesting insights and value can be derived from the data to accelerate drug safety research.

Data analysis using SQL

For application developers who want to perform targeted searching and lookups, the API endpoints provided by the openFDA project are “ready to go” for software integration using a standard API powered by Elasticsearch, NodeJS, and Docker. However, for data analysis purposes, it is often easier to work with the data using SQL and statistical packages that expect a SQL table structure. For large-scale analysis, APIs often have query limits, such as 5000 records per query. This can cause extra work for data scientists who want to analyze the full dataset instead of small subsets of data.

To address the concern of requiring all the data in a single dataset, the openFDA project released the full 100 GB of harmonized data files that back the openFDA project onto S3. Athena is an interactive query service that makes it easy to analyze data in S3 using standard SQL. It’s a quick and easy way to answer your questions about adverse events and aspirin that does not require you to spin up databases or servers.

While you could point tools directly at the openFDA S3 files, you can find greatly improved performance and use of the data by following some of the preparation steps later in this post.

Architecture

This post explains how to use the following architecture to take the raw data provided by openFDA, leverage several AWS services, and derive meaning from the underlying data.

Steps:

  1. Load the openFDA /drug/event dataset into Spark and convert it to gzip to allow for streaming.
  2. Transform the data in Spark and save the results as a Parquet file in S3.
  3. Query the S3 Parquet file with Athena.
  4. Perform visualization and analysis of the data in R and Python on Amazon EC2.

Optimizing public data sets: A primer on data preparation

Those who want to jump right into preparing the files for Athena may want to skip ahead to the next section.

Transforming, or pre-processing, files is a common task for using many public data sets. Before you jump into the specific steps for transforming the openFDA data files into a format optimized for Athena, I thought it would be worthwhile to provide a quick exploration on the problem.

Making a dataset in S3 efficiently accessible with minimal transformation for the end user has two key elements:

  1. Partitioning the data into objects that contain a complete part of the data (such as data created within a specific month).
  2. Using file formats that make it easy for applications to locate subsets of data (for example, gzip, Parquet, ORC, etc.).

With these two key elements in mind, you can now apply transformations to the openFDA adverse event data to prepare it for Athena. You might find the data techniques employed in this post to be applicable to many of the questions you might want to ask of the public data sets stored in Amazon S3.

Before you get started, I encourage those who are interested in doing deeper healthcare analysis on AWS to make sure that you first read the AWS HIPAA Compliance whitepaper. This covers the information necessary for processing and storing patient health information (PHI).

Also, the adverse event analysis shown for aspirin is strictly for demonstration purposes and should not be used for any real decision or taken as anything other than a demonstration of AWS capabilities. However, there have been robust case studies published that have explored a causal relationship between aspirin and adverse reactions using OpenFDA data. If you are seeking research on aspirin or its risks, visit organizations such as the Centers for Disease Control and Prevention (CDC) or the Institute of Medicine (IOM).

Preparing data for Athena

For this walkthrough, you will start with the FDA adverse events dataset, which is stored as JSON files within zip archives on S3. You then convert it to Parquet for analysis. Why do you need to convert it? The original data download is stored in objects that are partitioned by quarter.

Here is a small sample of what you find in the adverse events (/drugs/event) section of the openFDA website.

If you were looking for events that happened in a specific quarter, this is not a bad solution. For most other scenarios, such as looking across the full history of aspirin events, it requires you to access a lot of data that you won’t need. The zip file format is not ideal for using data in place because zip readers must have random access to the file, which means the data can’t be streamed. Additionally, the zip files contain large JSON objects.

To read the data in these JSON files, a streaming JSON decoder must be used or a computer with a significant amount of RAM must decode the JSON. Opening up these files for public consumption is a great start. However, you still prepare the data with a few lines of Spark code so that the JSON can be streamed.

Step 1:  Convert the file types

Using Apache Spark on EMR, you can extract all of the zip files and pull out the events from the JSON files. To do this, use the Scala code below to deflate the zip file and create a text file. In addition, compress the JSON files with gzip to improve Spark’s performance and reduce your overall storage footprint. The Scala code can be run in either the Spark Shell or in an Apache Zeppelin notebook on your EMR cluster.

If you are unfamiliar with either Apache Zeppelin or the Spark Shell, the following posts serve as great references:

 

import scala.io.Source
import java.util.zip.ZipInputStream
import org.apache.spark.input.PortableDataStream
import org.apache.hadoop.io.compress.GzipCodec

// Input Directory
val inputFile = "s3://download.open.fda.gov/drug/event/2015q4/*.json.zip";

// Output Directory
val outputDir = "s3://{YOUR OUTPUT BUCKET HERE}/output/2015q4/";

// Extract zip files from 
val zipFiles = sc.binaryFiles(inputFile);

// Process zip file to extract the json as text file and save it
// in the output directory 
val rdd = zipFiles.flatMap((file: (String, PortableDataStream)) => {
    val zipStream = new ZipInputStream(file.2.open)
    val entry = zipStream.getNextEntry
    val iter = Source.fromInputStream(zipStream).getLines
    iter
}).map(.replaceAll("\s+","")).saveAsTextFile(outputDir, classOf[GzipCodec])

Step 2:  Transform JSON into Parquet

With just a few more lines of Scala code, you can use Spark’s abstractions to convert the JSON into a Spark DataFrame and then export the data back to S3 in Parquet format.

Spark requires the JSON to be in JSON Lines format to be parsed correctly into a DataFrame.

// Output Parquet directory
val outputDir = "s3://{YOUR OUTPUT BUCKET NAME}/output/drugevents"
// Input json file
val inputJson = "s3://{YOUR OUTPUT BUCKET NAME}/output/2015q4/*”
// Load dataframe from json file multiline 
val df = spark.read.json(sc.wholeTextFiles(inputJson).values)
// Extract results from dataframe
val results = df.select("results")
// Save it to Parquet
results.write.parquet(outputDir)

Step 3:  Create an Athena table

With the data cleanly prepared and stored in S3 using the Parquet format, you can now place an Athena table on top of it to get a better understanding of the underlying data.

Because the openFDA data structure incorporates several layers of nesting, it can be a complex process to try to manually derive the underlying schema in a Hive-compatible format. To shorten this process, you can load the top row of the DataFrame from the previous step into a Hive table within Zeppelin and then extract the “create  table” statement from SparkSQL.

results.createOrReplaceTempView("data")

val top1 = spark.sql("select * from data tablesample(1 rows)")

top1.write.format("parquet").mode("overwrite").saveAsTable("drugevents")

val show_cmd = spark.sql("show create table drugevents”).show(1, false)

This returns a “create table” statement that you can almost paste directly into the Athena console. Make some small modifications (adding the word “external” and replacing “using with “stored as”), and then execute the code in the Athena query editor. The table is created.

For the openFDA data, the DDL returns all string fields, as the date format used in your dataset does not conform to the yyy-mm-dd hh:mm:ss[.f…] format required by Hive. For your analysis, the string format works appropriately but it would be possible to extend this code to use a Presto function to convert the strings into time stamps.

CREATE EXTERNAL TABLE  drugevents (
   companynumb  string, 
   safetyreportid  string, 
   safetyreportversion  string, 
   receiptdate  string, 
   patientagegroup  string, 
   patientdeathdate  string, 
   patientsex  string, 
   patientweight  string, 
   serious  string, 
   seriousnesscongenitalanomali  string, 
   seriousnessdeath  string, 
   seriousnessdisabling  string, 
   seriousnesshospitalization  string, 
   seriousnesslifethreatening  string, 
   seriousnessother  string, 
   actiondrug  string, 
   activesubstancename  string, 
   drugadditional  string, 
   drugadministrationroute  string, 
   drugcharacterization  string, 
   drugindication  string, 
   drugauthorizationnumb  string, 
   medicinalproduct  string, 
   drugdosageform  string, 
   drugdosagetext  string, 
   reactionoutcome  string, 
   reactionmeddrapt  string, 
   reactionmeddraversionpt  string)
STORED AS parquet
LOCATION
  's3://{YOUR TARGET BUCKET}/output/drugevents'

With the Athena table in place, you can start to explore the data by running ad hoc queries within Athena or doing more advanced statistical analysis in R.

Using SQL and R to analyze adverse events

Using the openFDA data with Athena makes it very easy to translate your questions into SQL code and perform quick analysis on the data. After you have prepared the data for Athena, you can begin to explore the relationship between aspirin and adverse drug events, as an example. One of the most common metrics to measure adverse drug events is the Proportional Reporting Ratio (PRR). It is defined as:

PRR = (m/n)/( (M-m)/(N-n) )
Where
m = #reports with drug and event
n = #reports with drug
M = #reports with event in database
N = #reports in database

Gastrointestinal haemorrhage has the highest PRR of any reaction to aspirin when viewed in aggregate. One question you may want to ask is how the PRR has trended on a yearly basis for gastrointestinal haemorrhage since 2005.

Using the following query in Athena, you can see the PRR trend of “GASTROINTESTINAL HAEMORRHAGE” reactions with “ASPIRIN” since 2005:

with drug_and_event as 
(select rpad(receiptdate, 4, 'NA') as receipt_year
    , reactionmeddrapt
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_drug_and_event 
from fda.drugevents
where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and medicinalproduct = 'ASPIRIN'
     and reactionmeddrapt= 'GASTROINTESTINAL HAEMORRHAGE'
group by reactionmeddrapt, rpad(receiptdate, 4, 'NA') 
), reports_with_drug as 
(
select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_drug 
 from fda.drugevents 
 where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and medicinalproduct = 'ASPIRIN'
group by rpad(receiptdate, 4, 'NA') 
), reports_with_event as 
(
   select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_event 
   from fda.drugevents
   where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and reactionmeddrapt= 'GASTROINTESTINAL HAEMORRHAGE'
   group by rpad(receiptdate, 4, 'NA')
), total_reports as 
(
   select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as total_reports 
   from fda.drugevents
   where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
   group by rpad(receiptdate, 4, 'NA')
)
select  drug_and_event.receipt_year, 
(1.0 * drug_and_event.reports_with_drug_and_event/reports_with_drug.reports_with_drug)/ (1.0 * (reports_with_event.reports_with_event- drug_and_event.reports_with_drug_and_event)/(total_reports.total_reports-reports_with_drug.reports_with_drug)) as prr
, drug_and_event.reports_with_drug_and_event
, reports_with_drug.reports_with_drug
, reports_with_event.reports_with_event
, total_reports.total_reports
from drug_and_event
    inner join reports_with_drug on  drug_and_event.receipt_year = reports_with_drug.receipt_year   
    inner join reports_with_event on  drug_and_event.receipt_year = reports_with_event.receipt_year
    inner join total_reports on  drug_and_event.receipt_year = total_reports.receipt_year
order by  drug_and_event.receipt_year


One nice feature of Athena is that you can quickly connect to it via R or any other tool that can use a JDBC driver to visualize the data and understand it more clearly.

With this quick R script that can be run in R Studio either locally or on an EC2 instance, you can create a visualization of the PRR and Reporting Odds Ratio (RoR) for “GASTROINTESTINAL HAEMORRHAGE” reactions from “ASPIRIN” since 2005 to better understand these trends.

# connect to ATHENA
conn <- dbConnect(drv, '<Your JDBC URL>',s3_staging_dir="<Your S3 Location>",user=Sys.getenv(c("USER_NAME"),password=Sys.getenv(c("USER_PASSWORD"))

# Declare Adverse Event
adverseEvent <- "'GASTROINTESTINAL HAEMORRHAGE'"

# Build SQL Blocks
sqlFirst <- "SELECT rpad(receiptdate, 4, 'NA') as receipt_year, count(DISTINCT safetyreportid) as event_count FROM fda.drugsflat WHERE rpad(receiptdate,4,'NA') between '2005' and '2015'"
sqlEnd <- "GROUP BY rpad(receiptdate, 4, 'NA') ORDER BY receipt_year"

# Extract Aspirin with adverse event counts
sql <- paste(sqlFirst,"AND medicinalproduct ='ASPIRIN' AND reactionmeddrapt=",adverseEvent, sqlEnd,sep=" ")
aspirinAdverseCount = dbGetQuery(conn,sql)

# Extract Aspirin counts
sql <- paste(sqlFirst,"AND medicinalproduct ='ASPIRIN'", sqlEnd,sep=" ")
aspirinCount = dbGetQuery(conn,sql)

# Extract adverse event counts
sql <- paste(sqlFirst,"AND reactionmeddrapt=",adverseEvent, sqlEnd,sep=" ")
adverseCount = dbGetQuery(conn,sql)

# All Drug Adverse event Counts
sql <- paste(sqlFirst, sqlEnd,sep=" ")
allDrugCount = dbGetQuery(conn,sql)

# Select correct rows
selAll =  allDrugCount$receipt_year == aspirinAdverseCount$receipt_year
selAspirin = aspirinCount$receipt_year == aspirinAdverseCount$receipt_year
selAdverse = adverseCount$receipt_year == aspirinAdverseCount$receipt_year

# Calculate Numbers
m <- c(aspirinAdverseCount$event_count)
n <- c(aspirinCount[selAspirin,2])
M <- c(adverseCount[selAdverse,2])
N <- c(allDrugCount[selAll,2])

# Calculate proptional reporting ratio
PRR = (m/n)/((M-m)/(N-n))

# Calculate reporting Odds Ratio
d = n-m
D = N-M
ROR = (m/d)/(M/D)

# Plot the PRR and ROR
g_range <- range(0, PRR,ROR)
g_range[2] <- g_range[2] + 3
yearLen = length(aspirinAdverseCount$receipt_year)
axis(1,1:yearLen,lab=ax)
plot(PRR, type="o", col="blue", ylim=g_range,axes=FALSE, ann=FALSE)
axis(1,1:yearLen,lab=ax)
axis(2, las=1, at=1*0:g_range[2])
box()
lines(ROR, type="o", pch=22, lty=2, col="red")

As you can see, the PRR and RoR have both remained fairly steady over this time range. With the R Script above, all you need to do is change the adverseEvent variable from GASTROINTESTINAL HAEMORRHAGE to another type of reaction to analyze and compare those trends.

Summary

In this walkthrough:

  • You used a Scala script on EMR to convert the openFDA zip files to gzip.
  • You then transformed the JSON blobs into flattened Parquet files using Spark on EMR.
  • You created an Athena DDL so that you could query these Parquet files residing in S3.
  • Finally, you pointed the R package at the Athena table to analyze the data without pulling it into a database or creating your own servers.

If you have questions or suggestions, please comment below.


Next Steps

Take your skills to the next level. Learn how to optimize Amazon S3 for an architecture commonly used to enable genomic data analysis. Also, be sure to read more about running R on Amazon Athena.

 

 

 

 

 


About the Authors

Ryan Hood is a Data Engineer for AWS. He works on big data projects leveraging the newest AWS offerings. In his spare time, he enjoys watching the Cubs win the World Series and attempting to Sous-vide anything he can find in his refrigerator.

 

 

Vikram Anand is a Data Engineer for AWS. He works on big data projects leveraging the newest AWS offerings. In his spare time, he enjoys playing soccer and watching the NFL & European Soccer leagues.

 

 

Dave Rocamora is a Solutions Architect at Amazon Web Services on the Open Data team. Dave is based in Seattle and when he is not opening data, he enjoys biking and drinking coffee outside.