Tableau is a popular analytics platform that enables you to gain greater intelligence about your business. It is an ideal tool for visualizing data stored in TimescaleDB.

This tutorial covers:

  • Setting up Tableau to work with TimescaleDB
  • Running queries on TimescaleDB from within Tableau
  • Visualize data in Tableau

To complete this tutorial, you need a cursory knowledge of the Structured Query Language (SQL). The tutorial walks you through each SQL command, but it is helpful if you've seen SQL before.

To start, install TimescaleDB. When your installation is complete, you can proceed to ingesting or creating sample data and finishing the tutorial.

Also, get a copy or license of Tableau.

You also want to complete the Cryptocurrency tutorial, as it sets up and configures the data you need to complete the remainder of this tutorial. You can visualize many of the queries found at the end of the Cryptocurrency tutorial.

Locate the host, port, and password of your TimescaleDB instance.

Connecting your TimescaleDB instance to Tableau takes just a few clicks, thanks to Tableau's built-in Postgres connector. To connect to your database add a new connection and under the 'to a server' section, select PostgreSQL as the connection type. Then enter your database credentials.

Let's use the built-in SQL editor in Tableau. To run a query, add custom SQL to your data source by dragging and dropping the "New Custom SQL" button (in the bottom left of the Tableau desktop user interface) to the place that says 'Drag tables here'.

Type a query in the dialog box. In this case, use the first query from the Cryptocurrency Tutorial:

SELECT time_bucket('7 days', time) AS period,
last(closing_price, time) AS last_closing_price
FROM btc_prices
WHERE currency_code = 'USD'
GROUP BY period
ORDER BY period

You should see the same results in Tableau that you see when you run the query in the psql command line.

Let's also name our data source 'btc_7_days', which you can see below.

Using Tableau to view time-series data

Results in a table are only so useful, graphs are much better! So in our final step, let's take our output from the previous step and turn it into an interactive graph in Tableau.

To do this, create a new worksheet (or dashboard) and then select your desired data source (in our case 'btc_7_days'), as shown below.

New worksheet in Tableau to examine time-series data

In the far left pane, you'll see a section Tableau calls 'Dimensions' and 'Measures'. Whenever you use Tableau, it classifies your fields as either dimensions or measures. A measure is a field that is a dependent variable, meaning its value is a function of one or more dimensions. For example, the price of an item on a given day is a measure based on which day is in question. A dimension, therefore, is an independent variable. In our example, the given day does not change based on any other value in our database.

To put it in more direct terms, July 4, 1776 is still July 4, 1776, even if the price of tea skyrockets. However, the price of tea may change, depending on which day you are looking into.

So, in this case, move the dimension period into the Columns section of your worksheet, and examine the last_closing_price measure depending on a given period. In Tableau, you can drag and drop these elements into the proper place, like this:

New dimensions and measures in Tableau to examine time-series data

Now this graph doesn't quite have the level of fidelity you want because the data points are being grouped by year. To fix this, click on the drop down arrow on period and select 'exact date'.

Analyze granular data in Tableau to examine time-series data

Tableau is a powerful business intelligence tool and an ideal companion to data stored in TimescaleDB. This tutorial only scratched the surface of the kinds of data you can visualize using Tableau.


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