Tableau_ Live Vs Extract

Live vs Extract


  • This refers to a data source which contains direct connections to the real-time data.
  • Live connections can be used at a place where the data is real-time data which when get updated, so our visualization also gets updated automatically.
  • EXAMPLE– real time transactions.
  • Live connections are slower for complex visualizations.
  • Live connections are used especially in less complex visualizations with small data sets, filters, calculations etc.
  • Live connections get refreshed when there is a change in the original data source.
  • Live connections always rely on the database for all queries.
  • A Live connection in Tableau basically means that Tableau is querying and reading directly from your vs extract


  • Extract files are the local copy of the data source that you can use to make the view.
  • Extracts can be used at a place where the view can be created by a subset of the data source.
  • EXAMPLE– Keeping the record of the monthly trends of a hospital.
  • Extracts are much faster for visualization.
  • Extracts are used especially in more complex visualizations with large datasets, filters calculations etc.
  • Extract is a snapshot of the data, so they need to be refreshed to receive updates from the original data source.
  • However extracts databases should not always be optimized.
  • The extracts in tableau are refreshed in two ways:-


  • A refresh in which only the rows and columns are added which are new since the previous time you extracted the data.
  • Most data sources support an instrumental refresh as it takes less time.
  • Although it is faster but once in a period of time the user needs to use the full refresh to ensure that the data is most up to date.


  • It refers to the refreshment of the entire extract
  • In this refresh, all of the rows are replaced with the data in the original data source.
  • This kind of refresh ensures that the user has an exact copy of the original data.
  • A full refresh can sometimes take a long time & be expensive on the database.

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