Introduction to Amazon EMR (Elastic MapReduce) for Beginners

Computing is at its peak these days and continues to rise. Within the last 3 decades, machines have evolved and improved a whole bunch, especially in terms of processing power and multitasking.

Can you even imagine how crazy the performance boost could be if the tasks are shared among multiple machines and executed parallelly? This is called distributed computing. It’s like teamwork for computers.

However, you might be wondering why we’re discussing this distributed computing thing. Because distributed computing and Amazon EMR (Elastic MapReduce) are highly related. That is, EMR by AWS uses distributed computing principles to process and analyze large amounts of data on the cloud.

With Amazon EMR, you can now analyze and process big data using a distributed processing framework of your choice on S3 instances.

How Amazon EMR Works?

Firstly, input the data to any data store like Amazon S3, DynamoDB, or other AWS storage platforms, as they all integrate well with the EMR.

Now, you’ll need a big data framework to process and analyze this data. With various big data frameworks to choose from, such as Apache Spark, Hadoop, Hive, and Presto, you can pick the one that suits your requirements and upload it to the chosen data store.

An EMR cluster of EC2 instances is created to parallelly process and analyze the data. You can configure the number of nodes and other details to create the cluster.

Your primary storage distributes the data and frameworks to these nodes, where the data chunks are individually processed, and the results are combined.

Once the results are out, you can terminate the cluster to release all the allocated resources.

Benefits of Amazon EMR

Businesses, either small or big, always consider adopting cost-effective solutions. Then why not an affordable Amazon EMR? When it can simplify running various big data frameworks on AWS, providing a convenient way to process and analyze your data while saving some money.

✅ Elasticity: You can guess its nature via the term ‘Elastic MapReduce’. The term says – Based on the requirements, Amazon EMR allows you to easily resize the clusters manually or automatically. For instance, you might need 200 instances to process your requests now, and this may go to 600 instances after an hour or two. So, Amazon EMR is the best when you only need scalability to adapt to quick changes in demand.

✅ Data stores: Whether it’s Amazon S3, Hadoop distributed file system, Amazon DynamoDB, or other AWS data stores, Amazon EMR seamlessly integrates with it.

✅ Data processing tools: Amazon EMR supports various big data frameworks, including Apache Spark, Hive, Hadoop, and Presto. On top of that, you can run deep learning and machine learning algorithms and tools on this framework.

✅ Cost-effective: Unlike other commercial products, Amazon EMR allows you to pay only for the resources you use on an hourly basis. Additionally, you can choose from different pricing models that align with your budget.

✅ Cluster customization: The framework lets you customize each instance of your cluster. Also, you can pair up a big data framework with a perfect cluster type. For instance, Apache Spark and Graviton2-based instances are a deadly combo for optimized performance in the EMR.

✅ Access controls: You can leverage AWS Identity and Access Management (IAM) tools to control permissions in the EMR. For example, you can allow specific users to edit the cluster while others can only view the cluster.

✅ Integration: Integrating EMR with all the other AWS services is seamless. With this, you can get the power of virtual servers, robust security, extendible capacity, and analytics capabilities in the EMR.

Use Cases of Amazon EMR

#1. Machine Learning

Analyze the data using machine learning and deep learning in Amazon EMR. For example, running various algorithms on health-related data to track multiple health metrics, such as body mass index, heart rate, blood pressure, fat percentage, etc., is crucial to develop a fitness tracker. All of this can be done on EMR instances faster and more efficiently.

#2. Perform Large Transformations

Retailers usually pull a large amount of digital data to analyze customer behavior and improve the business. Along the same line, Amazon EMR will be efficient in pulling big data and performing large transformations using Spark.

#3. Data Mining

Do you want to address a dataset that takes a long time to process? Amazon EMR is exclusive for data mining and predictive analytics of complex data sets, especially in unstructured data cases. Moreover, its cluster architecture is great for parallel processing.

#4. Research Purposes

Get your research done with this cost-effective and efficient framework called Amazon EMR. Due to its scalability, you rarely see performance issues while running large data sets on EMR. So, this framework is highly adapted in big data research and analytics labs.

#5. Real-Time Streaming

Another major Amazon EMR advantage is its support for real-time streaming. Build scalable real-time streaming data pipelines for online gaming, video streaming, traffic monitoring, and stock trading using Apache Kafka and Apache Flink on Amazon EMR.

How Is the EMR Different From Amazon Glue and Redshift?

AWS EMR vs. Glue

The two powerful AWS services – Amazon EMR and Amazon Glue have gained a loyal remark in dealing with your data.

Extracting data from various sources, transforming and loading it to the data warehouses is fast and efficient with Amazon Glue, while Amazon EMR helps you process your big data applications using Hadoop, Spark, Hive, etc.,

Basically, AWS Glue lets you collect and prepare data for analysis, and the Amazon EMR allows you to process it.

EMR vs. Redshift

Picture yourself consistently navigating through your data and querying it with ease. SQL is something you often use to do this. Along the same line, Redshift offers optimized online analytical processing services to easily query large volumes of data using SQL.

When storing data, you’ll have access to highly scalable, secure, and available Amazon EMR uses third-party storage providers like S3 and DynamoDB. In contrast, Redshift has its own data layer, allowing you to store data in columnar format.

Amazon EMR Cost Optimization Approaches

#1. Come With Formatted Data

The larger the data, the longer it takes to process. Moreover, feeding raw data directly to the cluster makes it even more complex, taking more time to find the part you intend to process.

So, the formatted data comes with metadata about columns, data type, size, and more, using which you can save time in searches and aggregations.

Also, let down your data size by leveraging data compression techniques, as it is comparatively easier to process smaller datasets.

#2. Use Affordable Storage Services

Leveraging cost-effective primary storage services cuts down your major EMR spending. Amazon s3 is a simple and affordable storage service for saving input and output data. Its pay-as-you-go model only charges for the actual storage you used.

#3. Right Instance Sizing

Using appropriate instances with the right sizes can significantly reduce your budget spent on EMR. The EC2 instances are usually charged per second, and the price scales with their size, but whether you use a .7x large cluster or a .36x large cluster, the cost of managing them is the same. So, efficiently utilizing larger machines is cost-effective compared to using multiple small machines.

#4. Spot Instances

Spot instances are a great option to buy unused EC2 resources at discounts. Compared to On-demand instances, these come cheaper but are not permanent as they can be claimed back when demand rises. So, these are flexible for fault tolerance but not suitable for long-running jobs.

#5. Auto-Scaling

Its auto-scaling feature is all you need to avoid oversized or undersized clusters. This lets you choose the right number and type of instances in your cluster based on workload, optimizing costs.

Final Words

There is no end to the cloud and big data technology, leaving you endless tools and frameworks to learn and implement. One such single platform to leverage both big data and the cloud is Amazon EMR, as it simplifies running big data frameworks to process and analyze large data.

To help you get started with the EMR, this article shows you what it is, how it benefits, its working, its use cases, and cost-effective approaches.

Next, check out everything you need to know about AWS Athena.