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!
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➡️ 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.
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