Amazon Web Services: Data Analytics

Learn about best practices, patterns, and tools for designing and implementing data analytics using AWS.

Introduction

• Welcome
• Exercise files
• About using cloud services

1. Analytics on AWS

• 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

2. Analytic Services

• 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

3. AWS Code Tools for Analytics

• 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

4. Advanced Analytics

• 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

Conclusion

• Next steps