Mastering AWS Elastic MapReduce (EMR) for Data Engineers
 
Course Description
Mastering AWS Elastic MapReduce (EMR) for Data Engineers is a comprehensive, hands-on training program designed to teach data engineers, cloud architects, and analytics professionals how to build, manage, and optimize scalable big data processing pipelines using Amazon EMR. This course provides deep practical experience with Spark, Hadoop, Hive, Presto, and other distributed processing frameworks running on EMR.
You’ll learn how to design EMR clusters, optimize performance, integrate with data lakes, automate workflows, and secure your big data environment using AWS-native tools. Through guided labs and real-world projects, you will gain the knowledge needed to process large datasets, tune EMR clusters for cost and performance, and design production-ready big data pipelines on AWS.
 
Whether you're working with batch analytics, ETL pipelines, machine learning preprocessing, or real-time workloads, this course equips you with the skills required to deliver efficient and reliable data engineering solutions using EMR.
 
What You’ll Learn
 
EMR Foundations
Understanding EMR architecture, components, and the Hadoop ecosystem
Working with Spark, Hive, Presto, and other EMR-supported frameworks
Building and configuring EMR clusters for different data workloads
 
Data Processing & Pipelines
Running distributed Spark jobs on EMR
Implementing ETL/ELT workflows using EMR and AWS Glue
Integrating EMR with S3 data lakes, Lake Formation, and Athena
Using EMR notebooks for interactive development
 
Optimization & Cost Efficiency
Autoscaling strategies for EMR clusters
Spot vs. on-demand vs. reserved instances for EMR workloads
Performance tuning for Spark, Hive, and HDFS
Choosing cluster modes (transient vs. long-running)
 
Security & Governance
Securing EMR with IAM roles, KMS encryption, and Kerberos
Managing data access policies and fine-grained permissions
Private networking, VPC configuration, and secure connectivity
 
Automation & DevOps Integration
Using EMR Studio and EMR APIs for workflow automation
Integrating EMR with Step Functions, Airflow, and Lambda
Monitoring, logging, and troubleshooting EMR workloads
 
Real-World Big Data Scenarios
Designing production-grade data pipelines
Handling large-scale batch and streaming workloads
Building scalable machine learning data prep pipelines
 
Who This Course Is For
Data engineers and cloud engineers
Big data practitioners working with Spark or Hadoop
Architects designing large-scale data platforms on AWS
Anyone building ETL/ELT pipelines or analytics solutions on EMR