![]() ![]() Separate concernsīecause the data is transformed when it arrives at its destination, ELT allows the recipient of the data to control data manipulation. In scenarios such as this, ELT is the solution of choice because the transformation occurs after the data reaches its destination. An example of this is the stock market, which generates large amounts of data that is consumed in real-time. Often, decisions need to be made in relation to this data, and delays are unacceptable. This prevents any slowdown that can often occur if the transformation occurs before the Load function, such as in ETL. When large amounts of streaming data are generated, ELT allows that data to be loaded immediately, and transforms the data after it reaches its destination. Let’s take a look at some of the notable benefits: Move data to the destination more quickly for faster availability But, in most cases, the choice between ETL and ELT will depend on the choice between on available business resources and needs.ĮLT provides several advantages for users who integrate the process into their workflows. However, data scientists might prefer ELT, which lets them play in a “sandbox” of raw data and do their own data transformation tailored to specific applications. For example, because it transforms data before moving it to the central repository, ETL can make data privacy compliance simpler, or more systematic, than ELT (e.g., If analysts don’t transform sensitive data before they need to use it, it could sit unmasked in the data lake). There are other differences between ETL and ELT, too. They can support business intelligence, but more often, they’re created to support artificial intelligence, machine learning, predictive analytics and applications driven by real-time data and event streams. In ELT, the target data store can be a data warehouse, but more often it is a data lake, which is a large central store designed to hold both structured and unstructured data at massive scale.ĭata lakes are managed using a big data platform (such as Apache Hadoop) or a distributed NoSQL data management system. However, the order of steps is not the only difference. ELT does not transform any data in transit. ELT copies or exports the data from the source locations, but instead of moving it to a staging area for transformation, it loads the raw data directly to the target data store, where it can be transformed as needed. The obvious difference is the ELT process performs the Load function before the Transform function – a reversal of the second and third steps of the ETL process. Traditional ETL tools were designed to create data warehousing in support of Business Intelligence (BI) and Artificial Intelligence (AI) applications. It is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system. However, there are several distinct differences between ELT and ETL, which stands for extract, transform and load. It’s possible to confuse ELT with its sister process known by a nearly identical acronym. Formatting the data into tables or joined tables based on the schema deployed in the warehouse.Removing, encrypting, hiding, or otherwise protecting data governed by government or industry regulations.This may include everything from changing row and column headers for consistency to converting currencies or units of measurement as well as editing text strings and adding or averaging values-whatever is needed to suit the organization’s specific BI or analytical purposes. Performing calculations, translations, data analysis or summaries based on the raw data.Filtering, cleansing, de-duplicating, validating and authenticating the data.In this stage, a schema-on-write approach is employed, which applies the schema for the data using SQL, or transforms the data, prior to analysis. Typically, ELT takes place during business hours when traffic on the source systems and the data warehouse is at its peak and consumers are waiting to use the data for analysis or otherwise. In this step, the transformed data is moved from the staging area into a data storage area, such as a data warehouse or data lake.įor most organizations, the data loading process is automated, well-defined, continuous and batch-driven. That said, it is more typically used with unstructured data. The data set can consist of many data types and come from virtually any structured or unstructured source, including but not limited to: Extractĭuring data extraction, data is copied or exported from source locations to a staging area. ELT consists of three primary stages Extract, Load, and Transform. ![]()
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