Welcome to this in-depth beginner‘s guide on getting started with Amazon EMR! I will walk you through everything you need to know as a EMR newbie, from an overview of its distributed processing capabilities to clustering your first big data application.
What is Amazon EMR?
Amazon EMR (Elastic MapReduce) is a managed Hadoop framework that runs on Amazon EC2 and Amazon S3. It allows you to easily process vast amounts of data in a distributed, parallel fashion across a cluster of virtual servers.
EMR takes care of all the complicated Hadoop configuration and infrastructure setup so you can focus solely on your big data processing tasks. No need to worry about managing servers or clusters manually.
Some of the popular data processing engines and frameworks supported by EMR include:
- Apache Hadoop
- Apache Spark
- Apache Hive
- Apache Pig
- Apache Flink
- Presto
- Hue
- JupyterHub
So EMR gives you the power and scalability of these tools without the headache of installing, configuring and maintaining them yourself on clusters.
Why Use EMR for Big Data Processing?
Here are some of the major benefits you get by leveraging EMR instead of traditional big data platforms:
Easy to Scale – EMR makes it trivial to scale your processing capacity up and down based on workload. No need to add servers manually. Auto-scaling features lets your cluster grow or shrink automatically.
Cost Effective – You pay hourly for exactly the resources you use. No wasted spend on idle capacity. Spot instances and S3 storage minimizes costs.
Fully Managed – As a fully managed service, EMR handles all the undifferentiated heavy lifting involved with provisioning, configuring, securing and monitoring clusters.
Flexible – Supports endless open source big data tools beyond just Hadoop like Spark, Flink, HBase, Presto, Hive and more. You can process data in a way that best meets your use case.
Reliable – Process critical workloads with confidence on EMR. Built to handle even the largest data volumes with Bristlecone managed hardware.
Secure – Integrates nicely with other AWS services. Take advantage of IAM roles, KMS encryption, VPC controls and more to lock down access.
Automatable – API driven service allows for full infrastructure automation. Quickly spin up and tear down clusters on-demand.
Main Components of EMR Architecture
Now that you understand the basics of EMR, let‘s break down what‘s under the hood:
Data Store – Typically Amazon S3. Holds input data to process as well as final outputs. Integrates nicely but EMR supports many data stores.
EMR Cluster – Made up of EC2 instances running Hadoop/Spark. Master node manages task distribution. Core & task nodes for parallel processing.
Instances – Virtual servers inside the cluster. Compute optimized choices like C5 offer best price/performance.
HDFS – Hadoop Distributed File System. Stores and replicates data across cluster for processing.
Frameworks – Spark, HBase, Flink and other data processing tools. YARN resource manager handles scheduling.
Applications – Optional add-ons like Hue UI, Jupyter Notebook, Ganglia monitoring, and more.
That covers the basic building blocks. Now let‘s walk through using this technology for the first time…
Step-by-Step Guide to Your First EMR Cluster
Follow this easy step-by-step tutorial to launch your first EMR cluster for processing data files hosted in Amazon S3.
Step 1 – Upload Input Files
First, upload the data files you wish to process to S3. This will become the input source that EMR pulls from.
Step 2 – Create EMR Cluster
Go into the EMR management console and click "Create cluster". Choose Spark for the processing engine and pick an instance type like m5.xlarge with 4 core nodes.
Step 3 – Configure Software Settings
On the same screen, under software configuration, select a Spark version and make sure Hadoop, Hive and Hue are enabled as applications.
Step 4 – Choose Cluster Location
Next, pick an EC2 subnet and Availability Zone for your cluster to launch in. You can tune the networking config as desired.
Step 5 – Set Spot Pricing
Under advanced options, enable spot instances and set your max bid price per instance to save money. Review all settings then hit create cluster.
Step 6 – Monitor Cluster Provisioning
It will now take about 5 minutes for the full EMR cluster to finish provisioning. You can watch progress in the EMR console.
Step 7 – Process Data
Once live, connect to your cluster master node and begin running Spark jobs against the S3 input data. Output results back to S3.
Step 8 – Tear Down Cluster
After your work is complete, you can terminate the cluster directly from the EMR console to stop incurring charges.
And that‘s all there is to it! You just created your first ever EMR cluster running Spark on EC2 and used it to process data files from S3. Pretty easy stuff!
Now let‘s go over some power user tips…
Advanced Tips for EMR Users
Here are some pro tips for saving money, boosting performance and taking full advantage of Amazon EMR…
Spot Fleet – Use Spot Fleet with a mix or on-demand, spot and reserved instances to optimize cluster economy.
EMR Notebooks – Launch JupyterHub or Zeppelin directly inside EMR to analyze and visualize data.
EMR Debugging – Enable cluster debugging for extra monitoring metrics and troubleshooting data when issues arise.
Instance Types – Pick Graviton chips or the latest C6i/R6i instances for cutting edge ML performance.
Ganglia – Install the Ganglia dashboard to see real-time usage stats for every node in your cluster.
CLI Tools – Use EmrCLI instead of the web GUI for quick access to common management tasks right from terminal.
Bootstrapping – Bootstrap allows you to run custom scripts during spin-up to prep instances just the way you need.
EMRFS – EMRFS simplifies data access from S3 by making files appear like they exist directly on HDFS.
Auto-Termination – Set an auto-termination schedule for clusters so they automatically shut down when not actively processing data.
The more you use EMR, the more you realize what a powerful big data tool it can be on AWS!
Now let‘s go through some real world examples of companies leveraging EMR to unlock game changing analytics…
EMR Use Cases
Here are just a few examples of organizations using EMR to reimagine what‘s possible with data:
Case studies
The use cases are endless. Any company struggling under data complexity can benefit from EMR‘s scale and flexibility.
Let‘s wrap things up with a look at pricing and how to make the most of your budget…
Cost Optimization for EMR
While extremely powerful, EMR can also get quite expensive if not property optimized. Use these tips to keep your spend in check:
Savings tactics
Follow those guidelines and you‘ll have happy accountants along with happy data scientists!
Summary
And that wraps up this beginner‘s guide to using Amazon Elastic MapReduce!
We covered everything from EMR architecture overview to cluster setup steps to cost saving tactics. You now have all the fundamentals required to start building advanced big data pipelines on AWS!
For further learning check out Amazon‘s official EMR documentation or signup for a free AWS account to spin up a test cluster.
Happy data processing! Let me know if you have any other questions.
