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What you will learn


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Understand the Fundamentals of Modern Data Engineering

Build and Manage Scalable Data Lakes on AWS S3

Design Star Schema Data Models with Fact & Dimension Tables

Implement Slowly Changing Dimensions (SCD1 & SCD2)

Develop ETL Pipelines Using PySpark with Data Quality Checks

Query and Explore Data Lakes with AWS Athena and Glue Catalog

Automate Workflows and Pipelines Using Apache Airflow

Create Custom Airflow Plugins to Manage EMR Spark Jobs

Apply the WAP (Write-Audit-Publish) Pattern for Production Pipelines

Implement Data Quality Frameworks and Data Contracts

Deploy and Monitor Data Pipelines on AWS EMR

Optimize Data Workflows for Cost, Performance, and Reliability

Gain Hands-On Experience with Real-World Use Cases

Prepare for Data Engineering Interviews with Confidence

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