Learn about best practices, patterns, and tools for designing and implementing data analytics using AWS.
Overview
Syllabus
Introduction
- Welcome
- Exercise files
- About using cloud services
- AWS analytics design concepts
- Files vs. databases
- Business vs. predictive analytics
- Batching vs. streaming
- Which analytics type to use
- Data hygiene and ETL
- Visualization and QuickSight
- QuickSight demo
- Setup for AWS analytics
- Query Athena using SQL query on S3
- Query DynamoDB for NoSQL
- Set up Kinesis for input streams
- Query Kinesis Analytics
- Query CloudSearch and Elasticsearch
- Query AWS IoT
- Set up EMR, RDS, and Redshift
- Query RDS with ANSI SQL
- Query Redshift for RDBMS
- Query Redshift Spectrum
- Query EMR with Apache Spark
- Set up AWS CLI for analytics
- Query Athena using the AWS CLI
- Query DynamoDB using the AWS CLI
- Code tools for analytics
- Use the AWS SDK for querying DynamoDB
- Using AWS Cloud9
- Query AWS public datasets
- Use AWS Glue for ETL
- Understanding ETL options
- Use AWS QuickSight for visualizations
- Use the AWS Marketplace for visualization tools
- Summary of tools
- Common analytics architecture patterns
- Next steps
Taught by
Lynn Langit
