Amazon EMR (Elastic MapReduce) is a managed big data processing platform on AWS.
It allows you to run big data frameworks like:
Apache Hadoop
Apache Spark
HBase
Hive
Presto
Flink
Hue
Trino
EMR lets you process, analyze, and transform large amounts of data using distributed computing on clusters.
Think of EMR as:
“A fast, managed, scalable Hadoop/Spark cluster provided by AWS.”
Why Use AWS EMR?
Process large-scale data (Big Data)
Datasets from:
Logs
IoT devices
Social media
Business analytics
EMR processes TBs → PBs of data fast
Run Hadoop / Spark without managing servers
No need to install & configure:
Hadoop NameNode
DataNodes
Spark cluster
Hive Metastore
Cost-efficient big data processing
Run EMR on:
EC2 instances
Spot instances (very cheap)
EMR Serverless (pay per job)
Auto scaling
Adds nodes when workload increases
Removes nodes when workload decreases
Run complex analytics
Streaming jobs (Spark Streaming)
Machine learning (Spark MLlib)
ETL workflows (Hive, Spark, Presto)
Advantages of AWS EMR
Fully Managed Big Data Cluster
No need to set up or manage:
Hadoop cluster
Spark cluster
HBase master
YARN Resource Manager
AWS does it automatically
Very Cost-Effective
Supports EC2 Spot Instances → up to 80% cheaper
Clusters can be stopped anytime
EMR Serverless → pay only when jobs run
Scalable Big Data Processing
EMR scales automatically:
Add more nodes for big data
Remove nodes for small workloads
High Performance
EMR runs optimized versions of Hadoop/Spark:
Faster than self-managed clusters
Uses EMRFS (faster S3 connector)
Integration with AWS Data Services Supports:
S3 (primary data store)
Glue (Data Catalog)
Athena (SQL queries)
DynamoDB
Redshift
CloudWatch logs
Supports Multiple Big Data Frameworks You can run:
Hadoop
Spark
HBase
Hive
Pig
Presto
Flink
All on the same cluster.
Flexible Cluster Types
EMR on EC2
EMR on EKS
EMR Serverless
Disadvantages of AWS EMR
Cluster Management Still Required
Even though it’s managed, you still must handle:
Node sizing
Application configuration
Performance monitoring
Not fully hands-off
Costs Can Increase
If clusters run for long periods, cost increases—even if idle.
Spot instances can terminate unexpectedly.
Complex for Beginners
Hadoop/Spark require experience in:
Distributed computing
ETL processing
Cluster tuning
EMR is easy to deploy but hard to master.
High Learning Curve Need to know:
HDFS and S3 interaction
YARN
Spark jobs
Hive queries
EMR bootstrap actions
Performance Depends on Cluster Design
If instance types are wrong (CPU/RAM mismatch), job slows down.
Temporary HDFS Storage
If cluster shuts down → HDFS data is lost (unless stored in S3).
Research Breakthrough Possible @S-Logix
