In fact, it is most often used to create backups for disaster recovery. Data replication is often listed as a data integration method.
Data replication copies changes in data sources in real time or in batches to a central database. CDC can be used to reduce the resources required during the ETL “extract” step it can also be used independently to move data that has been transformed into a data lake or other repository in real time. Change Data Capture (CDC) identifies and captures only the source data that has changed and moves that data to the target system. Typically, ETL takes place during off-hours when traffic on the source systems and the data warehouse is at its lowest.ĮTL and ELT are just two data integration methods, and there are other approaches that are also used to facilitate data integration workflows. For most organizations that use ETL, the process is automated, well-defined, continuous and batch-driven. Typically, this involves an initial loading of all data, followed by periodic loading of incremental data changes and, less often, full refreshes to erase and replace data in the warehouse. In this last step, the transformed data is moved from the staging area into a target data warehouse. 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.
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