What is ETL?
Think of it as housecleaning for your data. ETL stands for extract, transform, and load. ETL is software that enables businesses to consolidate their disparate data while moving it from place to place, and it doesn’t really matter that that data is in different forms or formats. The data can come from any source. ETL is powerful enough to handle such data disparities. For example, a financial institution might have information on a customer in several departments and each department might have that customer’s information listed in a different way. The membership department might list the customer by name, whereas the accounting department might list the customer by number. ETL can bundle all this data and consolidate it into a uniform presentation, such as for storing in a database or data warehouse. Another way that companies use ETL is to move information to another application permanently. For instance, word-processing data might be translated into numbers and letters, which are eas
Extraction, Transformation and Loading processes comprise multiple steps, aimed at transferring data from production applications to the Business Intelligence systems: • Extraction of the data from production applications and databases (ERP, CRM, RDBMS, files, etc.) • Transformation of this data to reconcile it across source systems, perform calculations or string parsing, enrich it with external lookup information, and also match the format required by the target system (Third Normal Form, Star Schema, Slowly Changing Dimensions, etc.) • Loading of the resulting data into the various BI applications: Data Warehouse or Enterprise Data Warehouse, Data Marts, Online Analytical Processing (OLAP) applications or “cubes”, etc. Latency of ETL processes vary from batch (sometimes monthly or weekly, but most often daily), to near-real-time with more frequent refreshes (every hour, every few minutes, etc.).
Extract-Transform-Load (ETL), is a process that is used to take information from one or more sources, normalize it in some way to some convenient schema, and then insert it into some other repository. A common use is for data warehousing, where regular updates from one or more systems are merged and refined so that analysis can be done using more specialized tools. Typically the same process is run over and over, as new data appears in the source application(s).
