ETL

1. Data Extraction: 

• The first step in ETL involves extracting data from multiple sources such as databases, files, APIs, and streaming platforms.

2. Data Transformation:

• Once extracted, the data undergoes transformation to clean, validate, and structure it into a consistent format suitable for analysis.

3. Data Loading:

• Transformed data is then loaded into a target destination, typically a data warehouse, data lake, or database, where it can be stored and analyzed.

4. Schema Mapping:

• ETL processes often involve mapping source data schemas to target data schemas to ensure compatibility and consistency during transformation and loading.

5. Batch Processing:

• ETL workflows commonly operate in batch mode, processing data in predefined intervals or batches to manage resources efficiently and ensure data integrity.

6. Real-time Processing:

• In addition to batch processing, some ETL workflows support real-time data processing, enabling near-instantaneous data ingestion, transformation, and analysis.

7. Data Quality Assurance:

• ETL processes include mechanisms for data quality assurance, such as validation rules, error handling, and data profiling, to ensure the accuracy and reliability of processed data.

8. Scalability and Performance:

• ETL platforms are designed to scale horizontally to handle large volumes of data and perform complex transformations efficiently across distributed computing resources.

9. Metadata Management:

• ETL tools often provide metadata management capabilities to catalog and document data sources, transformations, and loading processes for improved governance and traceability.

10. Automation and Orchestration:

• Many ETL workflows are automated and orchestrated using specialized ETL tools and platforms, which offer features for scheduling, monitoring, and managing complex data workflows.