Formatting the data into tables or joined tables to match the schema of the target data warehouse.Removing, encrypting, or protecting data governed by industry or governmental regulators.Conducting audits to ensure data quality and compliance.This can include changing row and column headers for consistency, converting currencies or other units of measurement, editing text strings, and more. Performing calculations, translations, or summarizations based on the raw data.Filtering, cleansing, de-duplicating, validating, and authenticating the data.This phase can involve the following tasks: Here, the data is transformed and consolidated for its intended analytical use case. In the staging area, the raw data undergoes data processing. Those sources include but are not limited to: Data management teams can extract data from a variety of data sources, which can be structured or unstructured. Extractĭuring data extraction, raw data is copied or exported from source locations to a staging area. The easiest way to understand how ETL works is to understand what happens in each step of the process. While ELT has become increasingly more popular with the adoption of cloud databases, it has its own disadvantages for being the newer process, meaning that best practices are still being established. This work can usually have dependencies on the data requirements for a given type of data analysis, which will determine the level of summarization that the data needs to have. Even after that work is completed, the business rules for data transformations need to be constructed. Specific data points need to be identified for extraction along with any potential “keys” to integrate across disparate source systems. The ETL process, on the other hand, requires more definition at the onset.
ELT can be more ideal for big data management since it doesn’t need much upfront planning for data extraction and storage. ELT is particularly useful for high-volume, unstructured datasets as loading can occur directly from the source. While both processes leverage a variety of data repositories, such as databases, data warehouses, and data lakes, each process has its advantages and disadvantages.
ELT copies or exports the data from the source locations, but instead of loading it to a staging area for transformation, it loads the raw data directly to the target data store to be transformed as needed. The most obvious difference between ETL and ELT is the difference in order of operations.
SYNONYM FOR TRANSFORM SERIES
Through a series of business rules, ETL cleanses and organizes data in a way which addresses specific business intelligence needs, like monthly reporting, but it can also tackle more advanced analytics, which can improve back-end processes or end user experiences. What is ETL?ĮTL, which stands for extract, transform and load, 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.Īs the databases grew in popularity in the 1970s, ETL was introduced as a process for integrating and loading data for computation and analysis, eventually becoming the primary method to process data for data warehousing projects.ĮTL provides the foundation for data analytics and machine learning workstreams. ETL is a process that extracts, transforms, and loads data from multiple sources to a data warehouse or other unified data repository.
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