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AWS Data Services

This Repo includes a set of scripts and instructions designed to help you quickly set up and load key AWS data services for demo and learning purposes as a companion to my LinkedIn Learning AWS courses on these topics. The goal is to get a 'Hello World' implementation set up quickly. These samples use the AWS Query Editors for RDS and Redshift. I recommend using the included Jupyter Notebook to quickly connect to EMR. Examples often includes simple scripts (awscli scripts or AWS SDK node.js scripts). Samples include the following AWS Data Services and more:

Database (SQL)

  • AWS RDS Aurora and MySQL
    • Creates, load and SQL queries for Northwind database tables
    • For RDS Aurora Serverless use AWS RDS Query Editor
  • AWS Redshift and Redshift Spectrum
    • Creates, loads and SQL DW queries for Customers star schema source database tables
    • use AWS Redshift Query Editor

Database (NoSQL) or Streams

  • AWS DynamoDB
    • Creates, adds and NoSQL query for Music JSON data into table
    • use AWS DynamoDB console
  • AWS Kinesis
    • including Kinesis Analytics
    • use AWS Kinesis console

Data Lake

  • AWS EMR with Spark
    • Creates and run test CalcPi PySpark job on cluster
    • use AWS EMR Jupyter Notebook
  • AWS Athena
    • Creates, loads and SQL queries for ElbLogs (AWS sample) using service
    • use AWS Athena Console

AWS Machine Learning

Includes information about using machine learning servers and cloud services, including the following:

  • Amazon Machine Learning AMI (image)
    • on EC2 instances
    • GPUs optional
  • AWS SageMaker
    • using managed Juptyer Notebook instances
  • Databricks AWS Community Edition
    • implements managed Spark
    • on AWS
    • showing MxNet and other algorithms
  • AWS EMR
    • with Apache Spark with Spark (and SparkML)

NOTES on the examples:

  • All are set to run in the 'us-east-1' AWS region.
  • All were prepared on an OSX laptop.
  • Machine Learning 'Hello World' neural network (NN algorithm) example is...
    • mnist (probablistic image classification -> into 10 classes using images from handwritten digits) --OR--
    • fashion mnist (probablistic image classification -> into 10 classes using from grey-scale images of types of clothing)