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Amazon Web Services: Data Analytics

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Overview

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

Syllabus

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

Taught by

Lynn Langit

Reviews

4.6 rating at LinkedIn Learning based on 481 ratings

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