Business Analytics Archives - Digital Hill Valley

Hey, Data Analysts! Are you confused by the Data Engineering alphabet soup? I've got you! Here is what you need to know about ETL, ELT, and EtLT! ------------------------------------------------------------ ➡️ ETL (Extract, Transform, Load) What is it? - ETL is a data pipeline process where data is extracted from various sources, transformed into a format suitable for query and analysis, and then loaded into a destination database or data warehouse. 🟢Pros - Data is cleansed and transformed before entering the warehouse, ensuring quality and consistency. - Transformation outside the destination system can reduce the load on the data warehouse. - Mature technology with many established tools and frameworks available. 🔴Cons - Can be time-consuming as the transformation process must be completed before data loading. - Less flexible to changes in transformation logic, as it requires modifications to the ETL process and data reprocessing. - Can create bottlenecks, as data is unavailable until the entire ETL process is complete. ➡️ ELT (Extract, Load, Transform) What is it? - ELT is a data pipeline process where data is extracted from the source systems and loaded into the destination system (like a data lake or warehouse). Transformations are performed within the destination system. 🟢Pros - Faster loading times since data is transformed after it's loaded into the warehouse. - Flexibility to change transformation logic as data is stored in its raw form. - Takes advantage of the processing power of modern data warehouses and lakes. 🔴Cons - A powerful data warehouse or lake is required to handle the transformation load. - Potential security risks as raw, untransformed data is loaded into the warehouse. - Can be more expensive due to the costs associated with high-performance computing resources. ➡️ EtLT (Extract, (small transform) Load and Transform) What is it? - EtLT is a hybrid approach where data is extracted and loaded into a staging area within the data warehouse, and transformations are performed in multiple stages. 🟢Pros - Offers flexibility in handling different data transformation requirements. - Allows complex transformations to be broken down into more straightforward, manageable stages. - Can provide a balance between performance and transformation complexity. 🔴Cons - Can become complex to manage due to the multiple transformation stages. - Coordinating the staging and transformation steps may require more design and maintenance effort. - Could lead to longer data pipeline development cycles. ------------------------------------------------------------

Category: Business Analytics

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