What are the benefits and challenges of ETL vs ELT for data quality and governance?

My perspective is that ETL and ELT depends on factors such as the characteristics of the source and target systems, the volume of data, and the complexity of the transformations required. Each approach has its strengths and weaknesses, and the decision is often influenced by the specific requirements of the data integration scenario. ETL(Extract, Transform, Load) is typically associated with traditional data warehouses, where the data transformation occurs in a separate processing engine before loading into the warehouse. ELT (Extract, Load, Transform) is often associated with modern data warehouses or big data platforms that can handle massive parallel processing and perform transformations within the storage system itself.

ETL and ELT differ not only in their basic process flow but also in aspects like data movement efficiency, the role of data staging, schema transformations, handling complex transformations, dependency on the source system, tooling diversity, resource utilization in the target system, incremental loading efficiency, and support for real-time processing. Exploring these aspects provides a holistic understanding of the distinctions between the two approaches.

ETL can improve the performance of the target system. But these days there are options for cheaper storage systems (AWS) and computing systems to perform ELT at the target system.

According to my experiences here is some benefits of ETL: - ETL processes often include data cleansing and validation steps during the transformation phase. - ETL processes can significantly improve the performance of reporting and analytical queries. - ETL processes offer flexibility in performing complex transformations on the data - ETL workflows can be scaled to handle large volumes of data efficiently. - ETL workflows can be automated and scheduled to run at specific intervals, ensuring that data is kept up-to-date in the target system. - ETL processes can be designed to enforce security measures and ensure compliance with regulatory requirements.

Beyond data quality, ETL excels in metadata management, fostering a clear understanding of transformations. ETL's upfront processing aids in early error detection, preventing downstream issues. Additionally, it supports auditability, simplifying tracking and compliance efforts, enhancing overall data governance.

ETL, in contrast to ELT, may face scalability issues, struggling with large datasets. The intricate ETL process could introduce latency in delivering real-time insights. Maintenance complexities arise as ETL workflows need meticulous management, potentially impacting system responsiveness. ETL's sequential nature might limit parallel processing capabilities, affecting overall performance in dynamic, high-volume data environments.

I agree with these. In particular I find ELT allows for data exploration due to the availability of raw data. That is being able to view data before it is truncated or modified based on pre-defined rules. It also allows for identifying better if there are any incorrect transformation rules in place. Troubleshooting is easier/faster with the ELT process because of the availability of all layers of data at the target system

From an auditing perspective, ELT retains raw data at its first stage (raw layer), making it easy to review each and subsequent stages of a data pipeline therefore diagnosing data issues are easier when the results of each step are available. Not only that, but this also makes implementing data lineages in pipelines easier. From a data governance perspective, Data masking as well as encryption logic can be easily added in the raw layer to mask data that is flowing directly from the source. This can be done by designing an alternative of an ELT pipeline, which is called ETLT, whereby the data is transformed “lightly” while it’s in the staging area.

ELT has a few challenges that are harder to solve. Streaming pipelines ELT struggles to achieve near-real-time data pipelines as transforming data post-loading often requires complex, resource-heavy computations, leading to substantial delays, particularly with large data sets or advanced analytics. If you have a use case for streaming data, consider alternative technologies like Kafka or Flink. Storage Since data is loaded into the target destination and transformed after, you are keeping copies of the same information, but in a lesser form. Hence, storage cost is higher when implementing ELT pipelines. A good solution is to ensure your data models are efficient, and the data destination incurs minimal storage costs for data.

Something I've recently seen is effectively ELTL, with the final load being from a Staging area to a Production one. This allows the raw data to be explored and reviewed for troubleshooting if required (after the first load into Staging) but also allows the capabilities of the SQL engine (in this case) to be used for Transformation, then a final copy of the tidied data into to the target. Feels like the best of both worlds.