Data blending

Data blending is similar to extract, transform, load (ETL). Both ETL and data blending take data from various sources and combine them. However, ETL is used to merge and structure data into a target database, often a data warehouse. Data blending differs slightly as it's about joining data for a specific use case at a specific time. With some software, data isn't written into a database, which is very different to ETL. For example, with Google Data Studio. ==Software products==

Representing the increased demand for analysts to combine data sources, multiple software companies have seen large growth and raised millions of dollars, with some early entrants into the market now public companies. Examples include AWS, Alteryx, Microsoft Power Query, and Incorta, which enable combining data from many different data sources, for example, text files, databases, XML, JSON, and many other forms of structured and semi-structured data. Tableau In tableau software, data blending is a technique to combine data from multiple data sources in the data visualization. A key differentiator is the granularity of the data join. When blending data into a single data set, this would use a SQL database join, which would usually join at the most granular level, using an ID field where possible. A data blend in tableau should happen at the least granular level. Looker Studio In Google's Looker Studio, data sources are combined by joining the records of one data source with the records of up to 4 other data sources. Similar to Tableau, the data blend only happens on the reporting layer. The blended data is never stored as a separate combined data source. == Challenges with data blending ==

The most common custom metadata question is: "How can this dataset blend with (join or union to) my other datasets?" == See also ==