Posts in Category: Tableau

Tableau Server - Bad behavior

What constitutes bad behavior on the part of Tableau Server users?  Here's my view, what's yours?

  1. Old reports that aren’t used yet still run extracts.
  2. Failed extracts, especially repeat offenders on PROD.
  3. Huge number of subscribers to reports which take minutes to render.
  4. DEV versions on PROD server that aren’t actively being UAT.
  5. Repetitive extracts embedded in workbooks rather than as a datasource.
  6. Huge extracts when someone has access to a more cost effective db solution.
  7. Custom SQL when a Tableau build with referential integrity could have been done.
  8. Thirty or so connections in a single workbook.
  9. Using Projects instead of Sites.
  10. Governing AD groups by an organization distant from the managers of the AD group.
  11. Leaving departed users licensed on a server.
  12. Poor Tableau performance from attempting to design according to another product’s ‘best practices’.

Tableau Export Crosstab - getting large numbers to 'look right'

Here’ a trick to use when Tableau’s Export Crosstab has String fields filled with large numbers. As you likely have found out, when the csv is opened by Excel, the Microsoft Jet Engine interprets to the default ‘General’ cell formatting. A Tableau string field with numbers is converted to Excel number values, with the result that leading zeros are dropped and, if it is a large number, being displayed in Scientific notation. This is typically not what users want to see for fields like account numbers. What they want is for the string number to come across into a formatted text cell, but that won’t happen.

So, an alternative is to create a text version dimension of the field by appending it with a non-printing character. The character I’ve used is the ‘En Quad’, which can be found in MS Word by inserting a Symbol, then typing 2000 in the character code field in the dialog box as shown below. Once the character is selected, then click Insert to place it into a MS Word document. For ease of finding this character, I typically insert it between two visible characters, like the !@ shown below.

Highlight the character between the two visible, copy it and then paste that into your Tableau calculated field equation, which would be something like this:

// Appends non-visible character to string “ ” + [field]

Use the new calculated field in your worksheet and when exported, the MS Jet Engine will interpret the column as text, showing the entire large number, even with leading zeros.

MS Symbol Dialog:
Smiley face

Sample insert between two visible characters:

        !!͏ @


Foraging through Teradata

I have been asked to find data in a Teradata data warehouse without being provided any contextual 'sherpa guide'.  Fortunately, the requested person is familiar with the data that they seek and already has sourced a vetted version of the data through other means.  When that person then wants the data to come from a Teradata connection, it is typically because the vetted source doesn't have a convienent means to enable Tableau to connect to the data directly, but the requesting party knows that the data resides in the data warehouse and has obtained proper permissions to read the data directly.

After securing permissions and having knowledge of what the 'right' data should look like, I then connect to Teradata only to find something like 1 million unique Database/Table/Column combinations.  To assist my finding the data, I need to create my own sherpa guide.  Fortunately, DB's that run Teradata DW typically have instilled common naming conventions to their databases/tables/columns and rarely have obfuscated those items.  Connecting Tableau to the DBC database and the columsv view provides a comprehensive list of databases, tables and columns that then can be filtered using wildcard matching to find potential data sources for the requested data.

After finding potentials, creating a connection to each found db/table enables the data discovery (and lets you know if you have permissions to see that particular db/table/column).  The last step is to verify the quality of the data to the known vetted source.  With the last step complete, going further to actually create data visualization stories with Tableau can begin.

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