![]() ![]() This frees up valuable time for business analysts to focus on turning data into valuable insights. ETL pipelines handle data access, transformation into a common format, and preparation for downstream analysis. This is where ETL for business intelligence comes into play. Most business intelligence teams use tools from across the Modern Data Stack to ensure scalability, streamline workflows, and automate workflows in a reliable manner. ![]() Enterprise data typically lives in databases, applications, or file stores and must be accessed and consolidated before analysis can take place. To power analytics, the first step is always to extract data from disparate source systems. ETL: Key Use CasesĮTL pipelines create business value in 3 ways.Īnalytics - also known as business intelligence - is the most common use case for ETL.īusiness intelligence involves turning raw data into reports, visualizations, and data insights to improve strategic decision making. Now that we understand the benefits of an ETL process, let's dig into the 3 ways to generate value from data. If the company can't depend on its data pipelines, why should anyone trust the insights the team generates? Strong enterprise data integration capabilities allows businesses to: ![]() With reliable ETL pipelines in place, data-driven organizations are able to seamlessly access, analyze, and leverage their data effectively. The purpose of an ETL process is to allow enterprise data teams to pull information from disparate source systems (databases, applications, file stores) and to centralize the data for analytics, automation, or product development.Īs you would expect, everything revolves around creating business value. Using an ETL pipeline to get data from source systems into a centralized processing environment is one of the most important steps in architecting a scalable data pipeline. Load - The loading step syncs data to the target system (typically a data warehouse or data lake) where it can be used to create business value. Transform - The transform step involves cleaning, formatting, and sorting the data. There are three steps in the ETL process:Įxtract - The extraction step involves identifying and retrieving data from various sources. ETL (extract, transform, and load) is the process of extracting data from one system, transforming the information (cleaning, formatting, sorting), and loading the data into a target system. ![]()
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