Summary of "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.