Data Engineering using AWS Analytics Services

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Data Engineering is all about building Data Pipelines to get data from multiple sources into Data Lake or Data Warehouse and then from Data Lake or Data Warehouse to downstream systems. As part of this course, I will walk you through how to build Data Engineering Pipelines using AWS Analytics Stack. It includes services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, QuickSight, and many more.

Here are the high-level steps which you will follow as part of the course.

  • Setup Development Environment
  • Getting Started with AWS
  • Development Life Cycle of Pyspark
  • Overview of Glue Components
  • Setup Spark History Server for Glue Jobs
  • Deep Dive into Glue Catalog
  • Exploring Glue Job APIs
  • Glue Job Bookmarks
  • Data Ingestion using Lambda Functions
  • Streaming Pipeline using Kinesis
  • Consuming Data from s3 using boto3
  • Populating GitHub Data to Dynamodb

Getting Started with AWS

  • Introduction – AWS Getting Started
  • Create s3 Bucket
  • Create IAM Group and User
  • Overview of Roles
  • Create and Attach Custom Policy
  • Configure and Validate AWS CLI

Development Lifecycle for Pyspark

  • Setup Virtual Environment and Install Pyspark
  • Getting Started with Pycharm
  • Passing Run Time Arguments
  • Accessing OS Environment Variables
  • Getting Started with Spark
  • Create Function for Spark Session
  • Setup Sample Data
  • Read data from files
  • Process data using Spark APIs
  • Write data to files
  • Validating Writing Data to Files
  • Productionizing the Code

Overview of Glue Components

  • Introduction – Overview of Glue Components
  • Create Crawler and Catalog Table
  • Analyze Data using Athena
  • Creating S3 Bucket and Role
  • Create and Run the Glue Job
  • Validate using Glue CatalogTable and Athena
  • Create and Run Glue Trigger
  • Create Glue Workflow
  • Run Glue Workflow and Validate

Using Athena to run Serverless Queries

  • Getting Started with Athena
  • Accessing Glue Catalog Tables using Athena
  • Create Athena Tables and Populating data into Athena tables
  • Create Athena Tables using query results using CTAS
  • Amazon Athena Architecture
  • Partitioned Tables in Athena
  • Running Athena Queries and Commands using AWS CLI
  • Running Athena Queries and Commands using Python boto3

Cloud Data Warehouse using AWS Redshift

  • Create Redshift Cluster using Free Tier
  • Setup Databases as part of Redshift Cluster and perform CRUD operations
  • Copy CSV or delimited data from s3 into Redshift Tables using credentials as well as iam_role
  • Copy JSON data from s3 into Redshift Tables using iam_role

Who this course is for:

  • Beginner or Intermediate Data Engineers who want to learn AWS Analytics Services for Data Engineering
  • Intermediate Application Engineers who want to explore Data Engineering using AWS Analytics Services
  • Data and Analytics Engineers who want to learn Data Engineering using AWS Analytics Services
  • Testers who want to learn Databricks to test Data Engineering applications built using AWS Analytics Services


  • Programming experience using Python
  • Data Engineering experience using Spark
  • Ability to write and interpret SQL Queries
  • This course is ideal for experienced data engineers to add AWS Analytics Services as key skills to their profile

Last Updated 10/2021

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