Data Engineering on AWS

What to expect in the course:

• Data Work
  • Learn the structure and types of data you will be working with. Define the project goals - an important step for successful implementation.
• Platform and Pipeline Design
  • Get acquainted with the platform architecture and design pipelines: for data loading, storage in S3 (Data Lake), processing in DynamoDB (NoSQL), and Redshift (Data Warehouse). Learn to build pipelines for interfaces and data streaming.
• Basics of AWS
  • Create an account in AWS, understand access and security management (IAM), get introduced to CloudWatch and the Boto3 library for working with AWS through Python.
• Data Ingestion Pipeline
  • Create an API via API Gateway, send data to Kinesis, configure IAM, and develop an ingestion pipeline in Python.
• Data Transfer to S3 (Data Lake)
  • Set up a Lambda function to receive data from Kinesis and save it to S3.
• Data Transfer to DynamoDB
  • Implement a pipeline for transferring data from Kinesis to DynamoDB - a fast NoSQL database.
• API for Data Access
  • Create an API for working with data in the database. Learn why direct access from visualization to the database is a bad practice.
• Data Visualization in Redshift
  • Send streaming data to Redshift via Kinesis Firehose, create a Redshift cluster, configure security, create tables, and set up Firehose. Connect Power BI to Redshift for data analysis.
• Batch Processing: AWS Glue, S3, and Redshift
  • Master batch data processing: set up and run Glue to write data from S3 to Redshift, understand Crawler and data catalog, and learn to debug processes.

This course will help you gain practical experience in creating streaming and batch pipelines in AWS, as well as mastering key tools for working with cloud data.