Building Batch Data Analytics Solutions on AWS
Course Duration
1 Day
Audience
Employees of federal, state and local governments; and businesses working with the government.
Prerequisites
Students with a minimum one year of experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.
Course Description
This course focuses on designing and deploying batch data analytics pipelines on AWS. Students learn to use services such as AWS Glue, Amazon EMR, Amazon S3, and AWS Lake Formation to ingest, catalog, transform, and analyze large datasets at scale.
Learning Objectives
- Compare the features and benefits of data warehouses, data lakes, and modern data architectures
- Design and implement a batch data analytics solution
- Identify and apply appropriate techniques, including compression, to optimize data storage
- Select and deploy appropriate options to ingest, transform, and store data
- Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
- Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
- Secure data at rest and in transit
- Monitor analytics workloads to identify and remediate problems
- Apply cost management best practices
Course Outline
- 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 Apache 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