What is ETL

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝗧𝗟?

Extract, Transform, Load (ETL) is a data integration process that involves:

𝟭. 𝗘𝘅𝘁𝗿𝗮𝗰𝘁: This step involves extracting data from various heterogeneous sources. These sources include databases, flat files, APIs, or other data storage mechanisms.

𝟮. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺: 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:
  🔹 Cleaning the data (e.g., removing duplicates or correcting errors).
  🔹 Enriching the data (e.g., combining it with other sources).
  🔹 Aggregating or summarizing data.
  🔹 Converting data types or formats.
  🔹 Applying business rules or calculations.

𝟯. 𝗟𝗼𝗮𝗱: 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:

𝟭. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴: 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.

𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻: 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.

𝟯. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Companies often have data spread across multiple systems. ETL can integrate this data to provide a unified view.

𝟰. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: For meaningful BI and reporting, data must often be cleaned, transformed, and integrated. ETL processes facilitate this.

𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 𝗣𝗼𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻: ETL processes can populate data lakes with structured and unstructured data from various sources.

Some standard 𝗘𝗧𝗟 𝘁𝗼𝗼𝗹𝘀 are Microsoft SSIS, Talend, Oracle Data Integrator, Apache NiFi, and AWS Glue.

There is also a bit different approach nowadays, called 𝗘𝗟𝗧 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗟𝗼𝗮𝗱, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺). 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