It includes extracting data from its original source, transforming it and sending it to the target destination, such as a database or data warehouse. The process of data transformation, as noted, involves identifying data sources and types determining the structure of transformations that need to occur and defining how fields will be changed or aggregated. What are the key steps in data transformation? Data transformation is one element of harnessing this data, because - when done properly - it ensures data is easy to access, consistent, secure and ultimately trusted by the intended business users. As the volume of data has proliferated, organizations must have an efficient way to harness data to effectively put it to business use. It is also a critical component for any organization seeking to leverage its data to generate timely business insights. The transformation may involve converting data types, removing duplicate data and enriching the source data.ĭata transformation is crucial to processes that include data integration, data management, data migration, data warehousing and data wrangling. It's then transformed into a target format that can be fed into operational systems or into a data warehouse, a date lake or another repository for use in business intelligence and analytics applications. Next, the raw data is cleansed, if needed. The extraction phase involves identifying and pulling data from the various source systems that create data and then moving the data to a single repository. The process of data transformation can also be referred to as extract/transform/load ( ETL).
Transformations typically involve converting a raw data source into a cleansed, validated and ready-to-use format. Data transformation is crucial to data management processes that include data integration, data migration, data warehousing and data preparation. Data transformation is the process of converting data from one format, such as a database file, XML document or Excel spreadsheet, into another.
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