AWS Data Engineering Labs

Boost your data engineering skills with this practical, hands-on course focused on AWS data engineering. Designed for those with existing AWS experience, this course dives deep into the practical application of key AWS services – no overwhelming theory, just real-world labs!

You'll gain hands-on experience with essential data engineering tools including:

• AWS Glue: Learn to create a data catalog in Glue and efficiently run ETL jobs.
• AWS Lambda: Trigger SNS notifications for S3 uploads using EventBridge and orchestrate Lambda functions with Step Functions State Machine.
• Amazon S3: Master storing and retrieving data from Kinesis Data Streams using AWS CLI.
• Amazon Kinesis: Implement Kinesis Data Stream Python Boto3 Producer & Consumer patterns.
• AWS Redshift: Build a data warehouse on S3 data.
• Amazon EMR on EC2: Run Spark transformation jobs for powerful data processing.
• Databricks: Understand PySpark basics and set up Databricks on AWS.

This course covers crucial data pipeline concepts and provides practical skills to aid in your preparation for the AWS Data Engineering certification (DEA-C01).

Prerequisites:

• Basic understanding of AWS
• Fundamental knowledge of Python, SQL, Spark, and database concepts.

Beginner-Friendly: Don't worry if you're new to data engineering or even AWS! This course is structured to be accessible, with plenty of opportunities to learn and grow. We provide practical labs and encourage you to consult the official AWS documentation for a deeper understanding.

Bonus: Includes a curated list of DEA-C01 exam questions and answers to help you ace your certification!