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What Is AWS EMR? Here's Everything You Need To Know

Discover what AWS EMR is, how it works, the benefits and limitations of the service, and when you should use it as part of your big data strategy.

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According to Statista, the mass volume of data created, stored, copied, and consumed in 2020 was over 64 zettabytes (ZB), or about 64 trillion gigabytes (GB). This is expected to rise to 181 ZB by the year 2025. 

A large portion of this data is likely to be significant to your business. It can provide you with new insights that help you improve your product, communicate with consumers, and perform risk analysis. However, you’ll need the right tools to extract, sort, process, and analyze it. 

That’s where tools like Amazon’s Elastic MapReduce (EMR) come in. In this guide, we’ll discuss what EMR is, how it works, and how it may benefit you. You’ll then be able to decide if it’s worth integrating as part of your big data strategy. 

Table of Contents

What Is Amazon EMR?

Companies seeking to gain more insight and value from their data often struggle to capture, store, and analyze all of it. As data grows, it comes from more sources and becomes increasingly diverse. Thus, it needs to be securely accessed to be analyzed by different applications and lines of business. 

AWS EMR can help solve these issues. EMR is a managed cluster platform that assists organizations in running Big Data frameworks on AWS to analyze and process large sets of data more efficiently. 

AWS EMR

By using these frameworks along with related opensource projects such as Apache Hive and Apache Pig, you can process and sort data for business intelligence and analytics purposes. 

Additionally, you can use AWS EMR to transform and move large sets of data into and out of other AWS data stores and databases such as Amazon Simple Storage Services (Amazon S3) and Amazon DynamoDB.

How Does Amazon EMR Work?

Organizations put all their data into a data lake and analyze that data with their choice of open-source distributed processing frameworks such as:

  • Apache Spark
  • Apache Hadoop
  • Apache Storm
  • Presto

By far, the most popular storage infrastructure for a data lake is Amazon S3. EMR allows you to store data in Amazon S3 and run compute as you need to process that data. EMR clusters can be launched in minutes. You don’t have to worry about node provisioning, cluster setup, Hadoop configuration, or cluster tuning. 

Once the processing is done, you can switch off your clusters. You can also automatically resize clusters to accommodate peaks and scale them down without impacting your Amazon S3 data lake storage. 

Additionally, you can run multiple clusters in parallel, allowing them to share the same data set. EMR will monitor your clusters, retry failed tasks, and automatically replace poorly performing instances. 

If you use Amazon Cloudwatch along with EMR, you can collect and track metrics, logs, and audits. This approach also allows you to set alarms and automatically react to changes.   

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When To Use AWS EMR

In the past, users have found operating conventional data processing frameworks like Apache Spark to be quite challenging — especially when used in conjunction with other frameworks like Hadoop

It could be complex, expensive, and time-consuming. Organizations were required to purchase and integrate hardware (servers, computers, etc.), then install and manage software. Of course, software and hardware would require constant upgrades, further adding to expenses and complexity. 

Various lines of business would often timeshare centralized cluster resources. Consequently, this led to under-utilization during idle periods and missed SLA during peak. 

As your data grew, the size of your infrastructure would grow along with it. Because storage and compute are tied together, increasing storage means scaling expensive compute requirements. 

AWS EMR makes deploying distributed data processing frameworks easy and cost-effective. Furthermore, it decouples compute and storage. This allows both to grow independently, leading to better resource utilization.

With EMR, you pay a per-second rate only for the cluster resources you use. Customer support is available 24/7 on your normal AWS support belt at a fraction of what other commercial distributed processing frameworks vendors would charge. 

With spot pricing, you can lower your bill up to 90%. IDC recently found that the return of investment of EMR versus on-premise is 342% over five years.   

Benefits And Limitations Of AWS EMR

AWS EMR is nearly unbeatable, especially when coupled with some of Amazon’s other web-based services. Nevertheless, while its benefits may be self-evident and many, it does have its limitations. In this section of the guide, we’ll summarize some of Amazon EMR’s pros and cons. 

Pros:

  • Cost reduction of physical infrastructure - EMR eliminates the need for organizations to purchase and maintain physical servers. Instead, Amazon EMR charges you on a per-second basis for the features you use. 
  • Time-saving - Because EMR eliminates the need to provision and configure in-house servers for Big Data computational tasks, it can save time for system administrators. Amazon EMR will handle most of these operational details for you. This means your company will spend less time configuring manual administrative tasks. Furthermore, because AWS EMR will automatically scale both compute and storage resources for you, you won’t have to spend time manually provisioning these elements.   
  • Optimal resource utilization - EMR decouples storage and compute. This allows you to automatically increase and decrease Amazon Elastic Compute Cloud (EC2) instances and clusters when needed. You can then release resources as soon as you're done.  
  • Excellent customer support - Amazon EMR includes 24/7 customer service as a standard. 

Other benefits include fast spin-up times for EC2 instances. Essentially, this is an EMR service that can be run on AWS Virtual Private Cloud (VPC). This allows for increased data security.  

Cons:

  • Complicated interface - This seems to be a reoccurring complaint with most AWS products. The interface can be incomprehensible for beginners. Organizations will often have to opt to pay for training or hire certified professionals to help migrate their resources and configure Amazon EMR. Online documentation and tutorials are also quite limited. Initially, you may have to spend some time getting acquainted with the service and all its intricacies. 
  • Exclusive to Amazon cloud storage - You cannot use Amazon EMR to analyze or mine data stored with other cloud storage platforms. If you are already storing your data with another cloud provider, you’ll have to move it to one of Amazon’s cloud storage or database solutions. 

AWS EMR’s other limitations are service-based. For instance, Amazon EMR studio is only available in certain regions such as East US, West US, Asia-Pacific, Canada, and EU. You can only set a single Amazon VPC with a maximum of five subnets for an EMR studio. However, you can create multiple EMR studios and associate them with different VPCs and subnets.

Understanding Your AWS Costs

AWS EMR can help you change your rigid in-house cluster infrastructure and provide you with hassle-free Hadoop management. It can also significantly cut the time of data processing. However, as with most AWS products, its pricing can be a little incomprehensible.

Amazon charges you a per-second rate that is also tied to the number of clusters you are running. Additionally, you’ll need to pay for the EC2 server and Amazon’s Elastic Block Stores (EBS). If you’re running a large relational database, you’ll need to consider the cost of using the AWS Database Migration Service to move and host your data.    

This is only just the tip of the iceberg. To get the most out of EMR, you’ll likely need to employ a host of other AWS tools such as CloudWatch and S3 (for logs). Tracking and managing these costs can be quite daunting. 

With CloudZero, however, you gain complete insight into your AWS cloud spend. CloudZero’s cost intelligence platform maps costs to your products, features, services, dev teams, and more. The platform also automatically detects cost issues and alerts you before they run for days or weeks.

With cloud cost intelligence, you’ll be able to drill into cost data from a high level down to the individual components that drive your cloud spend — and see exactly how services drive your cloud costs and why. Request a demo today to see CloudZero in action.

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