Last updated on January 10, 2018
Tableau is a powerful collaboration platform. One can easily create, share and distribute the workbooks and dashboards set up in Tableau. Since people across the organization depend on Tableau, it’s essential to keep it performant and scalable.
We need to understand one important thing about the performance of Tableau that it is only as fast as your data source. In case if the data source is responding slower to queries, then Tableau, in turn, would have delayed response. Here are few points to consider on Tableau performance optimization:
Extracts are much faster to work with than a live data source. The extract is columnar store database which compresses the data internally. There are ways to improve the extract performance by ensuring
Context filters can be faster than extract. In this case, we still leverage the speed, power, and optimization of the database instead of relying on the client machine. There is also no need to re-extract to get the current data. The thumb rule for a context filter to work effectively is to use it in cases where we are filtering more than 90% of the data.
Lastly, Tableau Performance Recorder is an excellent utility which records key events as we interact with a workbook. This would help us view the performance metrics that Tableau creates to analyze and troubleshoot different events such as:
Tableau is architected in such a way that it maps very well to relational algebra and can splendidly turn a graphical interface into a query language. While translating the graphical interface into query language, It does not need the user to extract/import the data from the database into an analytical silo. Tableau permits the users to explore data quickly.
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Gaurav Mittal is Principal Consultant Tableau, Business Intelligence @YASH Technologies
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