Harnessing the Power of Big Data Analysis on AWS

Like a jigsaw puzzle, there are many components in the AWS big data ecosystem. Read this article and see how the components fit together to form a beautiful whole.

If you are a data engineer, wouldn’t it be great if you could easily scale your existing infrastructure on-demand to support your real-time data pipelines?

If you are a data scientist, wouldn’t it be great if you could leverage massive clusters of computers to conduct analyses on large datasets and build statistical models quickly?

Or, maybe you are playing both roles. Perhaps you are a student and you are trying to find a platform to get started in big data quickly. Whatever your current role might be, be sure to read on to learn more about AWS’s big data capabilities provided by the following services: EMR, DynamoDB, Redshift, Data Pipeline, Kinesis Streams, Machine Learning, Elasticsearch, and Kinesis Firehose. That’s a lot of ground to cover, so let’s get started!

Need a big data platform? Look no further than Amazon Web Services!

AWS is one of the largest and fastest-growing cloud infrastructure providers in the world. Over the past ten years, AWS has developed more than 70 products and services to support different and often very demanding types of cloud computing use cases.

The company dominates the global cloud industry with its market share of more than 30%. Even though Microsoft Azure and Google Cloud Platform are growing rapidly, AWS still witnessed a significant growth of 63% year over year. The brand equity that AWS has built over the years has allowed the company to gain a strong foothold in the increasingly-competitive cloud computing landscape.
AWS has been embracing the big data movement as part of their Infrastructure-as-a-Service platform strategy beginning as early as 2008 — just two years after they were officially launched as a subsidiary of Amazon.com.

In 2008, AWS announced public access to huge data sets such as the map of the human genome and the US census

It all began with an announcement to make available certain large public datasets such as the mapping of the Human Genome and the US Census data on Amazon S3 so that anyone who wants to analyze these datasets can do so easily and quickly by accessing them directly from their Amazon Ec2 instances.
Just to get an idea of how large some of these datasets are, the 1000 Genomes Project that tries to build the most comprehensive catalog of human genetic variation is over 200TB. The 1980, 1990 and 2000 US Census data are approximately 5GB, 50GB and 200GB respectively. AWS is able to leverage their cloud computing infrastructure to host these datasets with the hope of spurring innovation.
Shortly after they introduced these large public datasets, AWS started supporting big data processing by introducing the Amazon Elastic MapReduce (EMR) service in April 2009. Amazon EC2 and S3 are the basic building blocks that make Amazon EMR possible. AWS made incremental improvements and added new features to EMR, and also introduced other big data-related services over the years, which I have summarized chronologically in the following list:

Let’s take a look at the constituent services of the AWS big data infrastructure-as-a-service.

Amazon EMR: Elastic MapReduce Hadoop Service

Amazon EMR is a service that lets you create and manage large-scale distributed data processing clusters easily. Data engineers know that setting up different components in the Hadoop ecosystem and getting them to work well together often requires a lot of effort. Amazon EMR takes care of this burden and lets you focus on the dataset instead.

Amazon EMR supports Hadoop and Spark, along with interactive notebooks like Hue and Zeppelin-Sandbox, as well as machine learning frameworks like Mahout and Spark Mllib. For the full list of supported applications, see the Amazon EMR release page.

I have written a two-part series about using Apache Spark and Apache Zeppelin on Amazon EMR – See Part 1 and Part 2. Check them out. You may find my notes on IAM helpful too.

Cloud Academy offers quizzes around Amazon EMR and you can try them out (and all the Cloud Academy resources) for free.
Big Data AWS

Amazon DynamoDB: Managed NoSQL Databases

Amazon DynamoDB is a fully managed NoSQL database service that you can use to import, persist, and extract schema-less data. You can create DynamoDB tables and populate them with data from CSV files. You can also export data from DynamoDB tables to Amazon S3 buckets as a backup.
Our Database Fundamentals for AWS course gives you an introduction to Amazon DynamoDB. We also have a dedicated course focusing on Working with DynamoDB. At the same time, you should also try our hands-on lab to create and query DynamoDB tables.
amazon courses at Cloud Academy

Amazon Redshift: Industrial-Grade Data Warehousing

Amazon Redshift is a fully managed petabyte-scale data warehouse service. One petabyte is approximately 1,000 terabytes (in case you were going to look it up). You can create and provision an Amazon Redshift cluster to store large amounts of data, and perform fast queries using SQL query tools like SQL Workbench/J or Re:dash. You can also connect your business intelligence application or any other applications as long as it supports the standard PostgresSQL JDBC or OCBC drivers.
We have an introductory blog post, and as you might imagine some detailed learning resources if you would like to find out more.

AWS Data Pipeline: Data Processing Workflow Automation

AWS Data Pipeline lets you define a workflow to automate the processing and moving of data from one AWS service to another on a regular basis. You can create a pipeline to launch EMR jobs that run Hive queries with data imported from Amazon RDS, or backup DynamoDB tables into Amazon S3 every end of business day, or import new data when it is available into Amazon Redshift and send an Amazon SNS notification that new data has been imported into Redshift.
Our Automated Data Management with EBS, S3, and Glacier course shows you the best methods of backing up your data resources using AWS Data Pipeline.

Amazon Kinesis Streams: Streaming Data Service

Amazon Kinesis Streams is a managed service to ingest streaming data from many different sources. It makes the data available to Amazon Kinesis Applications that would read and process the streaming data in real-time. These applications are data consumers that are developed with the Amazon Kinesis API or Amazon Kinesis Client Library (KCL). There is a pre-built library that you can use to integrate Amazon Kinesis Streams with Apache Storm. You can think of this as the AWS’s equivalent of Apache Kafka.
Our blog post from 2015 titled Amazon Kinesis: Managed Real-time Event Processing is a good starting point and I encourage you to review posts from different sources.

Amazon Machine Learning

Amazon Machine Learning is a service that makes it easy for anyone to create machine learning models to do things like classifications or predictions through the use of wizards. You do not need to have a strong grasp of machine learning to use this service, although you are strongly advised to understand what you are doing.

Try Amazon Machine Learning with our hands-on lab using the HAR (Human Activity Recognition) dataset. We also have a course that introduces you to the principles and practice of Amazon Machine Learning.

Amazon Elasticsearch: Distributed Search Engine

Elasticsearch is an open source distributed search and analytics engine that you can use to run full-text queries on large amounts of data to make sense of the data. Amazon Elasticsearch is a managed service that lets you launch Elasticsearch on AWS.

We have a couple of blog posts about our impression of Amazon Elasticsearch when it was first launched, and our comparison with Amazon CloudSearch.

Amazon Kinesis Firehose

Amazon Kinesis Firehose is a managed service that lets you create delivery streams to send streaming data to AWS services such as Amazon S3, Amazon Redshift or Amazon Elasticsearch. Amazon Kinesis Firehose simply reads and writes data, and does not do any processing to the data stream.

Conclusion

As you probably gathered from reading this post, big data isn’t confined to a single area. I included links to useful posts and other articles. Researching this article forced me to see AWS Big Data with fresh eyes. AWS never stands still and that dynamic progress inspires me.

I hope that this blog post inspires you to explore and learn more about the different big data options that are available on AWS. Check out our Analytics Fundamentals for AWS course. The course covers numerous analytics tools including Amazon EMR, Kinesis Streams and Firehose, Machine Learning, Data Pipeline and Elasticsearch.

Have any questions? Leave a comment below!

 

Avatar

Written by

Eugene Teo

Eugene Teo is a director of security at a US-based technology company. He is interested in applying machine learning techniques to solve problems in the security domain.


Related Posts

Avatar
Chandan Patra
— February 21, 2020

Elasticsearch vs. CloudSearch: AWS Cloud Search Choices

Elasticsearch vs. CloudSearch: What's the main difference? Let's compare AWS-based cloud tools: Elasticsearch vs. CloudSearch. While both services use proven technologies, Elasticsearch is more popular, open source, and has a flexible API to use for customization; in comparison, CloudS...

Read more
  • AWS
  • Azure
  • cloudsearch
  • elasticsearch
Alisha Reyes
Alisha Reyes
— February 7, 2020

New on Cloud Academy: Git Labs, CKA and CKAD Lab Challenges, AWS and Azure Learning Paths, AGILE, and Much More

We just kicked off our first Free Weekend of 2020. This means we've unlocked our Training Library for just 72 hours. Until Sunday at 11:59 pm (PST), you can get unlimited access to our industry-leading learning paths, courses, certification prep exams, and our most popular hands-on labs...

Read more
  • agile
  • AWS
  • Azure
  • Google Cloud Platform
  • Linux
  • OWASP
  • programming
  • red hat
  • scrum
Avatar
Stuart Scott
— February 6, 2020

How to Encrypt an EBS Volume

Keeping data and applications safe in the cloud is one of the most visible challenges facing cloud teams in 2020. Cloud storage services where data resides are frequently a target for hackers, not because the services are inherently weak but because they are often improperly configured....

Read more
  • AWS
  • EBS
  • Encryption
Vitaly Kuprenko
Vitaly Kuprenko
— February 4, 2020

Heroku vs. AWS: Which Cloud Solution Works Best in 2020

Heroku vs. AWS: Introduction Сloud-based platforms get more and more recognition. According to Statista, just in the third quarter of 2019, cloud market revenues reached $27.5 billion. By moving to the cloud, businesses can focus on their strategy and other processes instead of dealing...

Read more
  • AWS
  • heroku
Alisha Reyes
Alisha Reyes
— January 31, 2020

How to Unlock Complimentary Access to Cloud Academy

Are you looking to get trained or certified on AWS, Azure, Google Cloud Platform, DevOps, Cybersecurity, Information Security, Python, Java, or another technical skill? Then you'll want to mark your calendars. Starting Friday, February 7 at 12:00 a.m. PST (3:00 a.m. EST), Cloud Acade...

Read more
  • AWS
  • Azure
  • cloud academy content
  • complimentary access
  • GCP
  • on the house
Alisha Reyes
Alisha Reyes
— January 28, 2020

Cloud Academy’s Blog Digest: Top 5 AWS Salary Report Findings, How To Become a Cybersecurity Professional, 8 Financial Benefits of Cloud Migration, and more

Now that it's 2020, how many times have you caught yourself dating a paper 2019? Don't lie. It's happened at least once or twice — or a handful of times — I'm sure. And if you're a member of the "perfect club" that hasn't made any 2020 mistakes, then we're still happy to have you in our...

Read more
  • AWS
  • aws salary
  • blog digest
  • Cloud Academy
  • Cloud Adoption
  • Cloud Migration
  • Cybersecurity
Patrick Navarro
Patrick Navarro
— January 22, 2020

Top 5 AWS Salary Report Findings

At the speed the cloud tech space is developing, it can be hard to keep track of everything that’s happening within the AWS ecosystem. Advances in technology prompt smarter functionality and innovative new products, which in turn give rise to new job roles that have a ripple effect on t...

Read more
  • AWS
  • salary
Alisha Reyes
Alisha Reyes
— January 6, 2020

New on Cloud Academy: Red Hat, Agile, OWASP Labs, Amazon SageMaker Lab, Linux Command Line Lab, SQL, Git Labs, Scrum Master, Azure Architects Lab, and Much More

Happy New Year! We hope you're ready to kick your training in overdrive in 2020 because we have a ton of new content for you. Not only do we have a bunch of new courses, hands-on labs, and lab challenges on AWS, Azure, and Google Cloud, but we also have three new courses on Red Hat, th...

Read more
  • agile
  • AWS
  • Azure
  • Google Cloud Platform
  • Linux
  • OWASP
  • programming
  • red hat
  • scrum
Alisha Reyes
Alisha Reyes
— December 24, 2019

Cloud Academy’s Blog Digest: Azure Best Practices, 6 Reasons You Should Get AWS Certified, Google Cloud Certification Prep, and more

Happy Holidays from Cloud Academy We hope you have a wonderful holiday season filled with family, friends, and plenty of food. Here at Cloud Academy, we are thankful for our amazing customer like you.  Since this time of year can be stressful, we’re sharing a few of our latest article...

Read more
  • AWS
  • azure best practices
  • blog digest
  • Cloud Academy
  • Google Cloud
Avatar
Guy Hummel
— December 12, 2019

Google Cloud Platform Certification: Preparation and Prerequisites

Google Cloud Platform (GCP) has evolved from being a niche player to a serious competitor to Amazon Web Services and Microsoft Azure. In 2019, research firm Gartner placed Google in the Leaders quadrant in its Magic Quadrant for Cloud Infrastructure as a Service for the second consecuti...

Read more
  • AWS
  • Azure
  • Google Cloud Platform
Alisha Reyes
Alisha Reyes
— December 10, 2019

New Lab Challenges: Push Your Skills to the Next Level

Build hands-on experience using real accounts on AWS, Azure, Google Cloud Platform, and more Meaningful cloud skills require more than book knowledge. Hands-on experience is required to translate knowledge into real-world results. We see this time and time again in studies about how pe...

Read more
  • AWS
  • Azure
  • Google Cloud
  • hands-on
  • labs
Alisha Reyes
Alisha Reyes
— December 5, 2019

New on Cloud Academy: AWS Solution Architect Lab Challenge, Azure Hands-on Labs, Foundation Certificate in Cyber Security, and Much More

Now that Thanksgiving is over and the craziness of Black Friday has died down, it's now time for the busiest season of the year. Whether you're a last-minute shopper or you already have your shopping done, the holidays bring so much more excitement than any other time of year. Since our...

Read more
  • AWS
  • AWS solution architect
  • AZ-203
  • Azure
  • cyber security
  • FCCS
  • Foundation Certificate in Cyber Security
  • Google Cloud Platform
  • Kubernetes