Data blending

Data blending is a process whereby big data from multiple sources are merged into a single data warehouse or data set. It concerns not merely the merging of different s or disparate sources of data but also different varieties of data. Data blending allows business analysts to cope with the expansion of data that they need to make critical business decisions based on good quality business intelligence. Data blending has been described as different from data integration due to the requirements of data analysts to merge sources very quickly, too quickly for any practical intervention by data scientists. 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. Data blending is similar to ETL in many ways. 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 and Tableau, the data blend occurs on the reporting layer; it's not written anywhere, only displayed. In Tableau software, data blending is a technique to combine data from multiple data sources in the data visualization. The data sources are stored separately and only displayed together in a dashboard, on the reporting layer. This is one of the key concepts differentiating a Tableau data blend from other definitions of data blending. The other key differentiator is the granularity of the data join.