![]() ![]() ![]() There are clearly, some similarities between the two, primarily in how the order of operations changes to meet different end goals: Is ETL the Same as Extract Load Transform (ELT)?Īnother similar term (Extract Load Transform, or ELT) is often confused with ETL. Often, but not exclusively, this is a data warehouse or testing database for cloud applications.Ĭombined, Extract Transform Load form an ETL pipeline where data can be predictably moved from collection through standing to its usable form in the cloud databases. Once data is transformed, it is written from the staging area to a target storage location. As such, you’ll often find extensive error-checking, auditing, and reporting to help admins and engineers understand what’s happening and, if necessary, make corrections. Transformation, however, isn’t a simple process, as many different and overlapping transformations must occur to ensure the data is ready for use.īecause many operations are happening in this stage, there is a genuine possibility of error, corruption, or data loss. TransformĪs the name suggests, the transformation stage is when the raw data collected in the extraction stage is processed for operational use. However, if the system cannot determine changes in data in the staging area, then full extraction methods are suitable for this stage. If data is continually pulled from remote sources such that records are being changed, then comparisons between old and new data objects can provide opportunities for optimization through partial extraction. Data mustn’t be directly extracted into a data warehouse infrastructure to avoid undermining that warehouse’s data structures and the reliability of analytics conducted on them. ExtractĮxtraction is the process of taking data from various heterogeneous sources and moving them to a staging area (such as a data lake) in preparation for cleaning and processing. ![]() This process is called Extract Transform Load, or ETL. Regardless of where data is traveling to, an organization must have a process to ensure that it gets there exactly as it should. In contrast, data lakes can serve as either a landing space for raw data or a space to power dynamic data structuring and analytics. Outside of architectural differences, data warehouses will often serve as resources to quickly draw reports and analytics.
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