ETL and ELT are two different approaches to manipulate data and get information into a data warehouse.
What is ETL?
ETL stands for extract, transform, and load. ETL tools are typically server-based data integration solutions for moving and manipulating data from its sources to a target data warehouse. When ETL tools first emerged four decades ago, the servers that databases ran on did not have the computing power of today. So, ETL solutions were developed to alleviate the data processing workload. They typically provided additional database and application connectivity and data manipulation functions that were previously limited in database engines.
What is ELT?
ELT stands for extract, load, and transform. Instead of using the older ETL method, today some take an ELT approach. With ELT, data transformation happens in the target data warehouse rather than requiring a middle-tier ETL server. This approach takes advantage of today’s database engines that support massively parallel processing (MPP) as well as its availability within cloud-based data platforms such as Snowflake, Amazon Redshift and Microsoft Azure SQL Data Warehouse.
ETL vs. ELT?
ETL will move the data from the source to staging in the data warehouse. ELT leverages the data warehouse to perform basic transformations, alleviating the need for data staging. ELT is better suited to performing more sophisticated data transformations, as it relies on the MPP of the underlying database to do the work.
Benefits of using ELT
With more companies making the transition to cloud-based data warehouses, ELT is gaining relative popularity. With ELT, data professionals work directly inside the warehouse for faster productivity, increased scalability, and fewer errors. The infrastructure and architecture are far simpler than on-premises data warehouses and can be scaled up and down as needed. The ELT process reduces waste, improves speed and removes annoying bottlenecks.
Data Warehouse Automation and ELT
ELT can be further improved with data warehouse automation software such as WhereScape. WhereScape automates the full data warehouse lifecycle and can save months or years of development time compared to manual coding. All work done with WhereScape is automatically documented, which decreases human error and improves efficiency.