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.
Lavastorm have been busy moving their agile data management platform into a web based user interface, adding new functionality and improving usability. The video below highlights just some of the features available in the latest iteration:
Many a times there is a need in our analyses to aggregate the measures at a different level than what is shown in the view. For example, if we drag a column called Region and then Sales then the measure value will be returned as per each Region:
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.
It is widely known that 80 percent of the world’s data is unstructured or semi-structured data, NoSQL concept has beceome more and more popular. This artical help you understand basic difference between SQL and NoSQL data and which database should you choose to use.
To Understand how various login method that tableau server supports. We need to understand two basic concept:
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.
When I first worked at Lavastorm, many years ago now, it was most commonly used for Revenue Assurance and Fraud Analytics in Telecommunications companies. Today, the same visual mapping of data processes allows analysts from all industries rapidly build data models and apply analytics to data from many disparate sources.
Revenue Assurance commonly requires that you acquire data as close to the source as possible, the objective being that you would identify any discrepancy in how business rules had been applied to the data as early as possible in the data value chain. The consequence being that downstream processes, including billing, would use this information and if errors existed this could result in a financial loss or gain, rework and/or impact customer experience.
That’s where Dataverse’s ability to read complex data types, and build your own complex data readers, can come in handy. It allows you to quickly take a vendor or standard specifications (such as the GSMA’s TAP specification for sharing data about roaming customers) and read the data, converting it into records.
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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