The Basics of ETL, ELT, and Data Pipelines

Posted by Nick Christou on Oct 21, 2019 12:22:15 PM

The decrease in storage costs and increase in computing power has led to the rise of big data and transformed the world into a data-driven space. Most businesses now rely on data to find out what their clients want, weigh customer satisfaction, and make decisions. The large volumes of data systems generate every day have, in turn, created the need for more efficient data processing channels. Even small companies deploy tech stacks with dozens of applications all capturing data. Mid-market and enterprise companies can find themselves with hundreds of applications and quickly become fragmented by division and location, making it hard to measure if the business is moving in the right direction. This is causing a massive paradigm shift; on-premise servers and one-size-fits most applications are being replaced with cloud-based applications unified into high-performance cloud based data warehouses. In order to effectively and repetitively get data out of multiple systems and into a central warehouse, companies must setup data pipeline tools to extract, transform and load data into their new home. The two most common solutions are ETL and ELT. If you're new to this scope, this might sound like a lot of technical jargon, which is why we went out of our way to break it down for you.

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Topics: data analytics, data pipelines, data extraction

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