What you’ll learn
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Understand the Fundamentals of Modern Data Engineering
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Build and Manage Scalable Data Lakes on AWS S3
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Design Star Schema Data Models with Fact & Dimension Tables
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Implement Slowly Changing Dimensions (SCD1 & SCD2)
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Develop ETL Pipelines Using PySpark with Data Quality Checks
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Query and Explore Data Lakes with AWS Athena and Glue Catalog
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Automate Workflows and Pipelines Using Apache Airflow
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Create Custom Airflow Plugins to Manage EMR Spark Jobs
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Apply the WAP (Write-Audit-Publish) Pattern for Production Pipelines
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Implement Data Quality Frameworks and Data Contracts
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Deploy and Monitor Data Pipelines on AWS EMR
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Optimize Data Workflows for Cost, Performance, and Reliability
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Gain Hands-On Experience with Real-World Use Cases
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Prepare for Data Engineering Interviews with Confidence
How to Enroll Data Engineering Bootcamp – Series 1 course?
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