ETTL—Extracting, Transferring, Transforming, and Loading
Data besides the difference in designing the database, building a data warehouse involves a
critical task that does not arise in building an OLTP system: to extract, transfer,transform, and load (ETTL) data from diverse data sources into the data warehouse
( or )
Data besides the difference in designing the database, building a data warehouse involves a
critical task that does not arise in building an OLTP system: to extract, transfer,transform, and load (ETTL) data from diverse data sources into the data warehouse
ETL stands for extraction, transformation and loading. Etl is a process that involves the following tasks:
extracting data from source operational or archive systems which are the primary source of data for the data warehouse transforming the data - which may involve cleaning, filtering, validating and applying business rules loading the data into a data warehouse or any other database or application that houses data
The ETL process is also very often referred to as Data Integration process and ETL tool as a Data Integration platform.
The terms closely related to and managed by ETL processes are: data migration, data management, data cleansing, data synchronization and data consolidation.
The main goal of maintaining an ETL process in an organization is to migrate and transform data from the source OLTP systems to feed a data warehouse and form data marts.
The terms closely related to and managed by ETL processes are: data migration, data management, data cleansing, data synchronization and data consolidation.
The main goal of maintaining an ETL process in an organization is to migrate and transform data from the source OLTP systems to feed a data warehouse and form data marts.
Typical Data Warehousing Environment
