ETL Concepts and Fundamentals

ETL development and testing ensure seamless data transfer between systems, validating accuracy and completeness every step of the way.

ETL Concepts and Fundamentals

Starts From 21st April

Morning

10:00 AM & 11:00 AM Batches

Starts From 25th April

Evening

07:00 PM & 08:00 PM Batches

ETL Concepts and Fundamentals
Introduction to ETL
  • Definition and importance of ETL in data integration
  • Role of ETL in data warehousing and business intelligence
  • ETL process flow and components (extract, transform, load)
Data Extractions
  • Source systems and data extraction methods
  • Pull-based vs. push-based data extraction
  • Techniques for extracting data from different sources (e.g., databases, flat files, APIs)
Data Transformation
  • Data cleaning and data profiling
  • Data validation and error handling
  • Transformation techniques (e.g., filtering, sorting, aggregation, join operations)
Data Quality and Governance
  • Importance of data quality in ETL processes
  • Data quality assessment and monitoring
  • Data governance principles and best practices
ETL Tools and Technologies
ETL Tools Overview
  • Introduction to popular ETL tools (e.g., Informatica, Talend, SSIS)
  • Features and capabilities of ETL tools Selection
  • criteria for choosing the right ETL tool
Data Loading
  • Types of data loading (e.g., full load, incremental load, CDC - Change Data Capture)
  • Techniques for efficient data loading (e.g., bulk loading, parallel loading) Loading data into data warehouses and data marts
ETL Best Practices and Optimization
Performance Tuning and Optimization
  • Identifying performance bottlenecks in ETL processes
  • Techniques for optimizing ETL performance (e.g., partitioning, indexing, caching)
  • Monitoring and measuring ETL performance metrics
Error Handling and Recovery
  • Strategies for handling errors in ETL workflows
  • Implementing retry mechanisms and error logging
  • Recovery procedures and rollback mechanisms
Advanced ETL Topics
Data Warehousing Concepts
  • Overview of data warehousing architecture
  • Dimensional modeling (e.g., star schema, snowflake schema)
  • ETL's role in populating and maintaining data warehouses
ETL in Big Data Environments
  • ETL challenges and considerations in big data ecosystems (e.g., Hadoop, Spark)
  • Tools and frameworks for ETL processing in big data environments
Real-time ETL and Streaming Data
  • Real-time data integration concepts
  • Implementing ETL for streaming data sources (e.g., Kafka, Apache Flink)
ETL Testing
  • Overview of ETL testing techniques (e.g., source-to-target validation, data completeness)
  • Tools and frameworks for ETL testing (e.g., QuerySurge, Informatica Data Validation Option)
Practical Applications and Case Studies
Practical ETL Projects
  • Hands-on exercises and projects to design and implement ETL workflows
  • Data integration scenarios and use cases
Case Studies and Best Practices
  • Analyzing real-world ETL implementations and success stories
  • Best practices for designing scalable and maintainable ETL processes
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