AWS Analytics Overview

AWS Analytics Overview

1. How its done?

   • Ingestion
     • Amazon DB Migration Services
       • Access data from RDBMS and load into S3 
     • Amazon Kinesis
       • Access stream of data from Mobile apps or iOT/Sensors, collect it and land into S3
       • Amazon Kinesis Analytics
         • Perform realtime analytics while the data is in transit from Source, by writing simple SQL queries
     • Amazon Snowball
       • Access large on premise dataset requiring bulk transfer
   • Storage
     • Amazon S3
   • Analysis
     • Amazon Glue (to Discover)
       • using crawlers and classifiers, to describe into Metadata catalog
     • Redshift
       •  Fully managed DWH for fast access to structured data
     • Redshift Spectrum
       • Extend SQL query to access all Unstructured Data on S3
     • Athena
       • Allows SQL based analysis and Exploration
     • EMR
       • Allows programmatic access to data leveraging power of Hadoop. This is a Managed service
   • Visualization
     • Amazon Quicksight
       • visualization tool
       • Amazon SPICE
         • its an in-memory DB layer that allows fast access of data to Analyst who has to constantly refresh data. this allows scaling out the analytical ability to 100s of Analyst at the same time

2. Key Enablers

   • Allow allocation of appropriate Compute and Storage
     • ensuring the right size is allocated
   • Support Open Data formats
     • Apace Parquet
     • ORC
     • Avero
   • Allow collaboration
     • Allow different business stakeholder s to access the same data set and run different kind and level of analytics

3. Key Focus

   • Focus on Business Outcome
   • Experimentation
   • Agile
   • Rapid Deployment