In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.
In this course, you will learn to:
Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.
We suggest the AWS Hadoop Fundamentals course for those that need a refresher on Apache Hadoop.
We recommend that attendees of this course have:
This course is intended for:
This course includes presentations, interactive demos, practice labs, discussions, and class exercises.
Module A: Overview of Data Analytics and the Data Pipeline
Module 1: Introduction to Amazon EMR
Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage
Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR
Module 4: Processing and Analyzing Batch Data with Amazon EMR and Hive
Module 5: Serverless Data Processing
Module 6: Security and Monitoring of Amazon EMR Clusters
Module 7: Designing Batch Data Analytics Solutions
Module B: Developing Modern Data Architectures on AWS