ETL and SQL: How They Work Together (with Examples)

ETL processes allow for the effective extraction, transformation, and loading of data into a target system for data integration and management. SQL allows for interaction with relational databases to generate reports, perform complex calculations, and gather insights. As data environments have grown more complicated, more organizations have begun to rely on integrating SQL with ETL to maximize their data’s potential. 

ETL is a data management process that stands for. This process extracts data from multiple disparate sources. It then transforms the data into the chosen format for analysis and loads it into the final destination, such as a data warehouse or lake.

• Extract: Data is gathered from databases, APIs, flat files, etc., and compiled in its raw form for the next step. 
• Transform:The extracted data is then cleaned, filtered, and converted into the desired format for analysis. 
• Load: Lastly, the transformed data is loaded into the final system for analysis and reporting. 

• Increased visibility: Organizations can achieve a unified view of data by combining disparate forms into one convenient location.
• Improved data quality: Data is cleansed and standardized during the ETL process. Redundant and inconsistent data is also addressed, improving data quality overall. 
• Scalable: ETL is a scalable process that can extract, transform, and load large quantities of data. 
• Enhanced decision-making: Organizations can leverage the unified view of this high-quality, consistent data to gather business intelligence and inform decisions. 

ETL is a complex process, but fortunately, there are tools that can be used to automate it. At a general level, these tools offer functionalities that correlate with each stage of ETL:

• Extraction: Connectors for various data sources and data extraction capabilities.
• Transformation: Built-in transformation functions and the ability to apply custom rules.
• Aggregating: Summarizing data from multiple sources into a consolidated format.
• Merging: Combining data from multiple sources into a unified data set. 
• Loading: Moving data into final destinations, such as warehouses, lakes, or databases.
• Integration: Combining data from different sources to ensure consistency and usability.