This is a set of scripts and instructions designed to help you quickly set up and load key AWS data services for demo and learning purposes. 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. Samples include the following AWS Data Services:
- 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
- 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 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
Includes simple scripts as follows:
- awscli scripts
- AWS SDK node.js scripts
Includes information about using machine learning servers and cloud services, including the following:
- AWS EC2 custom AMI for Machine Learning
- AWS SageMaker
- Databricks (Managed Spark) on AWS showing ML MxNet
- AWS EMR Spark with 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' example is
mnist(probablistic image classification -> into 10 classes using images of handwritten digits)
I have a large number of cloud and data courses on LinkedIn Learning. Below is a summary chart of my most popular courses for AWS Data topics. Just search on the course title (or my name) on the LinkedIn Learning site to view these courses.
