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Today, data comes into organizations from many places, and is stored in many more different systems and storage solutions depending on use cases. This creates a need for connecting and moving data between systems that generate and collect data, systems that store data, systems that analyze data, and more.
Data pipelines are the traditional method of moving that data from a source usually to a storage system or analytical system, such as a database or data warehouse. ETL and ELT are simply types of data pipelines that specialize in moving data from systems that generate data, like SaaS Apps, into data warehouses. ETL refers to a case where data is transformed before being loaded, and ELT refers to when data is transformed after landing in destination.
A limitation of data pipelines, including ETL/ELT, is their rigid structure, which restricts the variety and quantity of data sources and destinations that can be integrated. In addition, building and maintaining many data pipelines between sources and destinations is highly difficult and expensive, both in compute costs and time. As more data comes into the picture from more data systems, the number of data pipelines grow exponentially with each new source and destination. Think of them as creating a spiderweb of complexity that is unsustainable at scale.
For this reason, companies have been turning to data flows in recent years as a scalable and cost-effective solution for all their data movement needs. While the terms might sound interchangeable, data flows are a technical term for an evolved data pipeline that is more flexible and responsive for needs, unrestricted by type of data system at the source or destination. For this reason ETL, ELT, and other types of data pipelines aren’t relevant to data flows.
Data flows are a solution to moving data that moves beyond the type of data and data system particulars at source and destination to provide one solution to any particular combination that has to be created between source and target system. Adopting a data flow approach to moving data between systems allows companies to manage costs with growth while future proofing for new data sources and targets.
With the limitations in traditional data pipelines and recent advancement in technology to build and manage data flows instead, it’s clear why companies are adopting a data flow management approach rather than combining multiple data pipeline styles as a band-aid solution. In fact, companies are beginning to move even beyond the data flow by instead building and managing data products, an abstraction of the data itself that can be easily plugged into any needed data flow. These advancements are the answer to build scalable, cost-effective solutions that address the inevitable growth in the data and data systems that any initiative will eventually require.
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