ETL - Extract Transform Load | Summary of all the key concepts in building ETL Pipeline

ETL (Extract Transform Load) is essential for building a data warehouse.

• The extract phase involves getting data from various sources in different formats.
• real-time streaming, batch processing, and flat files are common methods for extraction.
• Complex logic is avoided in the extraction phase, with basic transformations like date calculations.
• Data format consistency is crucial to ensure uniform representation of data from multiple sources.
• data quality rules can be applied during extraction to ensure data integrity.
• staging tables are used for extraction and are typically truncated and loaded without running business queries.
• Load strategies include full and delta loads, with historical loads done initially.
• The transform phase involves applying data transformations to make raw data meaningful.
• Common transformation steps include mapping, data enrichment, joins, filters, removing duplicates, and aggregation.
• dimension tables require primary and foreign keys, attributes, load strategies, and granularity definitions.
• Fact tables contain measures and have primary and foreign keys to relate to dimension tables.
• The EDW (Enterprise data warehouse) stores processed data for business decisions, while data marts are subject-specific areas for analysis.
• The load phase involves loading data into dimension tables, fact tables, EDW, and data marts.
• The video serves as a comprehensive guide for both beginners and experienced professionals in ETL development.

Speakers

• Nitin (host)