ETL vs ELT in Data Processing & Warehousing

Data warehousing has traditionally relied on the ETL process—Extract, Transform, Load. In this method, raw data is first pulled out (Extract) from sources like web server logs. This data then undergoes modification (Transform) to fit a structured database format. Finally, the transformed data gets loaded (Load) into the data warehouse. This approach has challenges, especially with the growing data size, making the Transform stage a bottleneck where you may be going through every line of those web server logs.

Enter ELT— a new perspective that flips ETL on its head. ELT stands for Extract, Load, Transform. Unlike ETL, ELT first extracts the data and loads it directly into the data warehouse, bypassing the initial transformation step. The transformation then occurs within the data warehouse itself, taking advantage of powerful, distributed systems like Hadoop clusters. There's things like Spark SQL that let you also do queries in a very SQL-like data warehouse-like manner, on a data warehouse that is actually distributed.

The Differences

• Scalability: ELT leverages distributed databases like Hive and Spark, which can scale horizontally to handle bigger data sets.
• Processing Power: Using the cluster’s processing power, ELT efficiently transforms data after loading, not before.
• Flexibility: Instead of relying on massive, monolithic databases, ELT adapts well to modern, cloud-based technologies.

The Technical Twist

The transformation in ELT happens within the data warehouse system, often using newer, distributed database technologies built on top of Hadoop clusters. This setup enables high-speed querying and data transformation capabilities. Tools like Spark SQL are becoming increasingly relevant, allowing SQL-like queries on these distributed databases.