Vangie Beal
Last Updated April 15, 2024 10:49 pm

ETL is the acronym for “extract, transform, and load.” These three database functions are combined into one tool to pull raw data from one database and place it into another database. 

ETL can be used when an enterprise is sunsetting a data storage solution and needs to move all of that data into a new store first. It’s also beneficial for businesses that are consolidating data from multiple stores within the business, whether that’s Google Drive, a storage drive, or a database for analytics

What Does ETL Mean?

Extraction is the process of reading raw data from a database, such as Microsoft SQL Server or MySQL. In the data extraction stage, the data is collected, often from different types of data sources.

Transforming is the process of converting the extracted data from its previous form into the form it needs to be in so that it can be placed into another data store. Transformation occurs by using rules or lookup tables or by combining the data with other data. 

Loading is the process of writing the data into the target database. Ideally, once the data is loaded into the new location, it is ready to be analyzed by business intelligence (BI) solutions or analysts. 

How ETL Works

In the ETL process, data from one or more sources, or data stores, is extracted and then copied to the data warehouse. When dealing with large volumes of data and multiple source systems, the data is consolidated. ETL is used to migrate data from one database to another and is often the specific process required to load data to and from data marts and data warehouses. 

Because part of the ETL cycle is data processing, ETL takes time. Raw data that’s extracted from a data store must go through the transformation process to prepare it for any enterprise analytics cases. Therefore, it needs to be accurate and properly formatted. Rather than being a rapid data migration solution, ETL technologies should be given plenty of time to prepare data for actionable business insights. 

Also read: Top Benefits of a Data Warehouse

Why Is ETL Important?

ETL processes are helpful because they make a greater amount of data available to intelligence solutions. The more enterprise data from the more data store sources, the more comprehensive a picture is presented to enterprises, assuming the data is still clean and relevant. 

ETL tools are particularly useful for transforming and loading smaller amounts of data. Because it’s a longer process, ETL is better suited to small segments of data over a period of time, rather than big data-sized volumes in one operation. 

ETL and Business Intelligence

ETL tools are an important part of today’s business intelligence processes and systems. It is the IT process from which data from disparate sources can be put in one place, like a data warehouse, to programmatically analyze and discover business insights. Analysts have better access to the data when it’s not scattered across multiple digital locations. One of the main benefits of ETL is reducing data silos. 

ETL tools also improve the quality of the data being used for analytics. After undergoing the transformation process, data is more clean, accurate, and prepared for business intelligence operations. Performing BI operations on inaccurate or invalid data, in contrast, means risking detrimental business decisions. It can also result in ineffective customer relationship decisions, like reaching out to leads at the wrong time, and future compliance problems that come from storing inaccurate information.

ETL Software

ETL software provides integrations with applications that store data. Aside from simply extracting, transforming, and loading the data, ETL software offers additional functions, such as data visualization tool integrations and scheduling ETL processes. ETL tools may also include data analytics features and support for technologies like machine learning.

Some top ETL providers include:

  • IBM
  • SAP
  • Fivetran
  • Qlik
  • Informatica
  • Talend 

Although ETL is better suited to smaller batches of data, there are still ETL tools that process big data. Enterprises looking to extract, transform, and load their volumes of big data will be best able to do so with a tool specifically suited for larger volumes, especially unstructured data. 

Considering an ETL tool? Read the Best ETL Tools & Software.

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