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Moving from On-Premises ETL to Cloud-Driven ELT

November 12, 2021

Yesterday’s data pipelines were designed to accommodate predictable, slow-moving, and easily categorized data from on-premises business applications. They rely on extract, transform, and load (ETL) processes to capture data from various sources, transform it into a useful format, and load it into a target destination such as a data warehouse. These legacy pipelines work fine for structured data sources from enterprise applications, but they are no longer adequate for the diversity of data types and ingestion styles that characterize the modern data landscape.

Today’s modern pipelines are designed to extract and load the data first and then transform the data once it reaches its intended destination—a cycle known as ELT. Modern ELT systems move transformation workloads to the cloud, enabling much greater scalability and elasticity.

With an ELT pipeline, you can load many types of raw data into a cloud-based repository such as a cloud data platform. The platform improves the speed at which you can ingest, transform, and share data across your organization.

Read this white paper to learn how you can maximize the value of your data pipelines by using the right type of transformation method for each situation and workload.

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