Data Integration and ETL in Data Lakes
( 25 Modules )

Module #1
Introduction to Data Lakes
Overview of data lakes, their significance, and benefits in modern data architecture

Module #2
Data Lake Architecture
Components of a data lake, data ingestion, processing, and storage layers

Module #3
Data Integration Overview
Importance of data integration, types of data integration, and challenges

Module #4
ETL vs ELT vs ETLT
Comparing and contrasting different data integration approaches

Module #5
Data Lake ETL Architecture
Designing an ETL architecture for a data lake, including data ingestion, transformation, and loading

Module #6
Data Ingestion
Data ingestion patterns, including real-time, batch, and event-driven ingestion

Module #7
Ingestion Tools and Technologies
Overview of popular data ingestion tools, such as Apache NiFi, Apache Kafka, and AWS Kinesis

Module #8
Data Transformation
Data transformation concepts, including data quality, data cleansing, and data mapping

Module #9
Transformation Tools and Technologies
Overview of popular data transformation tools, such as Apache Beam, Apache Spark, and AWS Glue

Module #10
Data Loading
Data loading patterns, including batch, real-time, and streaming data loading

Module #11
Loading Tools and Technologies
Overview of popular data loading tools, such as Apache Hive, Apache Parquet, and AWS Redshift

Module #12
Data Quality and Governance
Importance of data quality and governance in data lakes, including data validation and data cataloging

Module #13
Data Cataloging
Data cataloging concepts, including data discovery, data profiling, and data search

Module #14
Data Lineage
Tracking data lineage in data lakes, including data provenance and data pedigree

Module #15
Security and Access Control
Securing data lakes, including data encryption, access control, and authentication

Module #16
Cloud-based Data Lakes
Designing and implementing data lakes on cloud platforms, such as AWS, Azure, and GCP

Module #17
On-Premises Data Lakes
Designing and implementing data lakes on-premises, including hardware and software considerations

Module #18
ETL Best Practices
Best practices for designing and implementing ETL pipelines in data lakes

Module #19
ETL Use Cases
Real-world ETL use cases in data lakes, including customer analytics and IoT data integration

Module #20
Data Lake Migration and Upgrade
Migrating and upgrading data lakes, including planning, execution, and testing

Module #21
Data Lake Monitoring and Troubleshooting
Monitoring and troubleshooting data lakes, including performance tuning and error handling

Module #22
ETL Testing and Quality Assurance
Testing and quality assurance for ETL pipelines, including data validation and data reconciliation

Module #23
ETL Tools and Technologies Deep Dive
In-depth exploration of popular ETL tools and technologies, including Apache NiFi, Apache Beam, and AWS Glue

Module #24
Case Studies and Industry Applications
Real-world case studies and industry applications of data lakes and ETL pipelines

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Data Integration and ETL in Data Lakes career

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Data Integration and ETL in Data Lakes ( 25 Modules )

Data Integration and ETL in Data Lakes
( 25 Modules )