5 ways to simplify ETL using SQL

ETL (Extract, Transform, Load) is a pivotal process in data analytics, facilitating the transformation of raw data into a format suitable for analysis and feeding AI models. Traditionally managed through various programming languages like SQL, Python, and Apache Spark, SQL is increasingly favored for its simplicity, widespread use, and compatibility across data systems. SQL's intuitive syntax and accessibility make it an attractive choice for managing ETL pipelines, allowing for modularity, data federation, and automation. Moreover, SQL is integral to managing data in both traditional data warehouses and modern data lakehouses, with Starburst Galaxy enhancing this process through features like scalability, cost-effectiveness, and open architecture. These capabilities streamline ETL operations, reduce complexity, and provide a flexible, efficient framework for data integration and analysis.