How can you use ELT as a data integration pattern?

1 What is ELT?

ELT is a data integration pattern that extracts raw data from various sources, loads it into a target data store, and transforms it there using the native capabilities of the data store. Unlike ETL, which transforms the data before loading it into the target, ELT postpones the transformation until the data is needed for analysis or consumption. This way, ELT preserves the original data and reduces the processing time and resources required for data integration.

2 How does ELT work?

ELT works by leveraging the power and scalability of modern data stores, such as cloud-based data warehouses or data lakes, to perform data transformation. These data stores can handle large volumes and varieties of data, and support various data formats and schemas. They also offer built-in tools and functions for data manipulation, cleansing, and enrichment. Therefore, ELT can use these features to transform the data on-demand, without relying on external tools or pipelines.

3 What are the benefits of ELT?

ELT offers several advantages for data integration, such as flexibility, simplicity, and performance. You can load any type of data into the target data store without worrying about structure, format, or quality. ELT simplifies the process by removing the need for intermediate data staging, transformation tools, and pipelines. Additionally, ELT improves performance by reducing the data movement and processing time. Instead of transforming the data before loading it, ELT loads it quickly and then transforms it only when necessary for analysis or consumption. This approach enables ELT to handle large volumes and velocities of data more efficiently.

4 What are the drawbacks of ELT?

ELT has some drawbacks for data integration, such as complexity, quality, and security. Complexity is increased as the responsibility for data transformation shifts from the data integration layer to the target data store. Quality can be compromised as raw and unvalidated data is loaded into the target data store, which can introduce errors and inconsistencies. Security risks can arise as sensitive and confidential data is exposed without filtering, masking, or encryption. To mitigate these risks, a good understanding of the target data store's capabilities and limitations is needed, along with complex queries and scripts to transform the data. Additionally, careful data governance and monitoring must be applied to ensure quality standards are met, and strict security and compliance policies must be in place to protect the data.

5 How can you use ELT as a data integration pattern?

ELT is a data integration pattern that can be adapted to different scenarios and use cases, depending on the data sources, target data store, and analysis and consumption requirements. To use ELT as a data integration pattern, you should first identify and connect to your data sources, and extract the necessary data. Choose a target data store that can handle the volume and variety of your data, and supports the formats and schemas needed. Then load the extracted data into the target data store without any transformation or validation. After that, you can transform the loaded data using native capabilities of the target data store, such as SQL queries or functions. Finally, analyze and consume the transformed data with tools like dashboards or reports, while taking advantage of features like indexing or partitioning for optimization.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?