Author: Tommy Tan
//Introduction
One of exciting features in Tableau next release 2020.2 is Metrics. In this blog, we will share with you the preview from the Tableau beta program, where we configured and tested this feature over the past month.
Tableau despite being an excellent tool to quickly visualize the data can also be used for creation and verification of Linear regression models used for predictive analytics. The ability of Tableau to integrate with external statistical languages like Python or R allows it to use the Regression models built in those languages to directly be used in Tableau.
Reading data correctly from a data source is the basis of the correct and meaningful visualization! This is true for all visualization tools and Tableau is no exception.
Issue
A peculiar Issue was faced with Tableau data reading once when it suddenly started dropping the data rows that it was reading from a csv file. The environment setup was as follows:
There were ^ delimited csv files getting generated daily from Oracle database by using PL/SQL code.
The files were fairly large (700 MB).
There were more than 100 columns in the csv file with almost 20 measure columns.
These csv files were then used to be appended to the Tableau Data extract (TDE) daily.
Optimizing your supply chain is a never ending task. Recently a client asked us to explore localized weather patterns with Tableau. This article shows how we were able to massage the data before importing it into Tableau.
We will use the NOAA (National Centers for Environmental Information) and GSOM (Global Summary of the Month) weather data into Tableau to do some analytics.
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