When it comes to consolidating data from multiple applications within an organization for reporting and analytics purposes, there are two common approaches that most Business Intelligence strategies can leverage: ETL and ELT.

ETL: Extract, Transform, Load

ETL, which stands for Extract, Transform, Load, is the traditional method for loading a data warehouse or Operational Data Store (ODS). With ETL, data is extracted from the source systems and transformations are applied during the data movement phase. The data flow processing pulls and manipulates the data in stream, ultimately storing it in its destination environment.

For example, let’s say you want to merge customer records from three different systems into a single customer table. In an ETL process, relevant information would be pulled from all three source systems, and business rules would be applied during the transformation step to create a single record for insert, update, or delete operations against the destination table.

ELT: Extract, Load, Transform

ELT, which stands for Extract, Load, Transform, is a newer framework that takes advantage of advances in processing and storage. In this model, data is extracted from the source system and loaded into tables in the destination database. Often, these tables are copies of the original source system tables. The transformations then take place on the database server itself, with the final copy of the data being inserted, updated, or deleted.

Continuing with the customer example, in an ELT model, all customer data would be pulled over to destination tables first. This enables reporting to be run against the destination database instead of the OLTP application databases. By eliminating transformations during the data pull, connections to the data sources can be closed quickly once the required data is obtained. Once the data is stored in the destination tables, transformations can be run to get the data into the final format required for the reporting application.

Both ETL and ELT have their own advantages and considerations. ETL is a more established approach and is well-suited for complex transformations and data cleansing. On the other hand, ELT leverages the power of modern processing and storage capabilities, allowing for faster data loading and more flexible reporting options.

When deciding between ETL and ELT, it’s important to consider factors such as the complexity of transformations, data volume, performance requirements, and the overall architecture of your data integration solution.

In conclusion, understanding the differences between ETL and ELT is crucial for designing an effective data integration strategy in SQL Server. Whether you choose ETL or ELT, both approaches have their own strengths and can be used to consolidate data from multiple applications for reporting and analytics purposes.