๐ช๐ต๐ฎ๐ ๐ถ๐ ๐๐ง๐?
Extract, Transform, Load (ETL) is a data integration process that involves:
๐ญ. ๐๐ ๐๐ฟ๐ฎ๐ฐ๐: This step involves extracting data from various heterogeneous sources. These sources include databases, flat files, APIs, or other data storage mechanisms.
๐ฎ. ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ: Once the data is extracted, it often needs to be transformed into a format suitable for analysis or reporting. This transformation can involve various operations such as:
๐น Cleaning the data (e.g., removing duplicates or correcting errors).
๐น Enriching the data (e.g., combining it with other sources).
๐น Aggregating or summarizing data.
๐น Converting data types or formats.
๐น Applying business rules or calculations.
๐ฏ. ๐๐ผ๐ฎ๐ฑ: The final step is to load the transformed data into a target system, often a data warehouse, data mart, or another database. This system is then used for business intelligence, reporting, or further analysis.
Some everyday use cases for ETL are:
๐ญ. ๐๐ฎ๐๐ฎ ๐ช๐ฎ๐ฟ๐ฒ๐ต๐ผ๐๐๐ถ๐ป๐ด: ETL processes are fundamental to data warehousing. They pull data from various operational systems, transform it, and then load it into a data warehouse for analysis.
๐ฎ. ๐๐ฎ๐๐ฎ ๐ ๐ถ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป: When businesses change or upgrade their systems, they often need to move data from one system or format to another. ETL processes can help with this migration.
๐ฏ. ๐๐ฎ๐๐ฎ ๐๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป: Companies often have data spread across multiple systems. ETL can integrate this data to provide a unified view.
๐ฐ. ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด: For meaningful BI and reporting, data must often be cleaned, transformed, and integrated. ETL processes facilitate this.
๐ฑ. ๐๐ฎ๐๐ฎ ๐๐ฎ๐ธ๐ฒ ๐ฃ๐ผ๐ฝ๐๐น๐ฎ๐๐ถ๐ผ๐ป: ETL processes can populate data lakes with structured and unstructured data from various sources.
Some standard ๐๐ง๐ ๐๐ผ๐ผ๐น๐ are Microsoft SSIS, Talend, Oracle Data Integrator, Apache NiFi, and AWS Glue.
There is also a bit different approach nowadays, called ๐๐๐ง (๐๐ ๐๐ฟ๐ฎ๐ฐ๐, ๐๐ผ๐ฎ๐ฑ, ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ). This is a data integration approach where raw data is extracted from various sources, loaded directly into a data warehouse or big data platform, and finally transformed within that target systems
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