What is ETL?

ETL (extract, transform, load) is a data pipeline system designed by data engineers. The data is extracted from multiple sources, transformed from its raw form into a proper format to be consistent with the data from other sources, and loaded into the target data warehouse. From here, it can be used for further data analysis and modeling to solve various business problems.

What happens during the "Extract" phase?

During the extract phase, data is systematically pulled from various disparate source systems. These sources typically include relational databases (SQL), Customer Relationship Management (CRM) software, Application Programming Interfaces (APIs), or flat files like CSV and JSON. The primary objective is to retrieve all required raw data efficiently without degrading the performance of the operational source systems.

Why is the "Transform" phase necessary?

Raw data originating from different systems inherently contains structural inconsistencies, missing values, duplicates, or incompatible formats. The transform phase applies specific programmatic rules to clean, filter, standardize, and aggregate this data. For instance, if one system records dates as "DD/MM/YYYY" and another as "MM-DD-YYYY," the transformation process converts all dates into a single, unified format, ensuring absolute data consistency for accurate analysis.

Where does the data go during the "Load" phase?

In the load phase, the fully transformed and standardized data is written into a centralized target destination. This destination is most commonly a Data Warehouse or a Data Mart. The database architecture of this target destination is specifically optimized to execute complex analytical queries quickly, allowing business intelligence tools to read the data efficiently.

What programming languages and tools are standard for executing ETL?

ETL processes are predominantly constructed using Python and SQL.

• Python: Widely used due to its extensive data manipulation capabilities. The Pandas library is the standard tool for transforming structured data in memory. For orchestrating and scheduling the ETL steps, engineers frequently use frameworks like Apache Airflow.
• SQL: Essential for extracting data from relational databases and performing transformations directly within the database engine.
• Scala and Java: Utilized when the data volume is massive, requiring distributed computing frameworks like Apache Spark to process the data across multiple servers.

What is the theoretical difference between ETL and ELT?

Both processes handle data integration, but the sequence of operations differs based on computing resource allocation. In traditional ETL, data is extracted and then transformed on a separate, dedicated processing server before being loaded into the destination. In ELT (Extract, Load, Transform), the raw data is loaded directly into the target system first. The transformation phase occurs entirely within the destination Data Warehouse, utilizing the warehouse's own computing power to process the data.