KNIME Tableau Integration User Guide

Navigate to File → Install KNIME Extensions and type "Tableau" in the search box that appears. When you do, you will see something like the screen below.

Install the extension KNIME Tableau Integration.

Once you have installed the extension(s), restart KNIME Analytics Platform and you should see the Tableau nodes in the node repository under KNIME Labs. You can now drag and drop the Tableau nodes into your workflow and use them to export data.

For the purpose of this guide, we will assume that you want to publish data to Tableau Online. However, most of what is discussed here also applies if you host your own Tableau server. If you do not already have a Tableau Online account, you can create a trial account. For the steps that follow, you will need a few pieces of information:

You will also need to have a Tableau Project already in place on the server. By default, Tableau Online creates a default project for you, but it is a good idea to create one and give it an appropriate name. You can do this in Tableau Online by navigating to the projects section and clicking on the New Projects button. The project we will create for this guide is called "Adult Income".

To get started, download the Tableau Integration Example workflow from the EXAMPLES server. The workflow and associated dataset is available at EXAMPLES/05_Reporting/02_Tableau/01_Tableau_Integration_Example. Go ahead and execute the five nodes listed in the "Usual Modeling Steps" section. This is a workflow that reads in a file containing demographic and income information for a sample of adults working in the United States, and uses the decision tree node to predict and score income classification for the sample. Of more interest to us, the workflow also exports both the raw data and results of the decision tree model using Tableau nodes in a few different ways, which we will discuss in more detail below.

Now that our Tableau Online account is in place and KNIME Analytics Platform has been configured, use the Tableau Writer node to export a .hyper file. Double click the node assigned to write out model scoring statistics to open its settings. The only thing we need to do here is to give a path and file name for the file:

Now, we can execute the node, and a .hyper file is created. Files of this type can be imported into Tableau Desktop for further analysis, and from there, published to Tableau Server. .hyper files can also be published directly to a Tableau server by using Tableau’s command line utility tabcmd. We will discuss each of these methods below.

.hyper files exported from KNIME Analytics Platform can be used directly with a installation of Tableau Desktop. Double clicking on the .hyper file will open the Tableau interface where you can begin construction of visualizations on the data right away. You can also publish the .hyper file for use in the Tableau Online environment via the desktop GUI. To do this, navigate to Server → Publish Data Source, assign a Project in the resulting dialog, and click Publish. This will make the data from the .hyper file available in the Tableau Online Environment.

Publishing .hyper files can also be done without a GUI, using Tableau’s command line utility tabcmd, which requires a separate installation, as detailed on the linked page). tabcmd allows for scripting of data management tasks in the server environment, and has a straightforward set of options and parameters. Logging into a Tableau server, for example, can be accomplished with the following command:

Publishing a .hyper file to the server can then be done as follows. Use our generated .hyper file as an example, making sure to include the full path and project name on the server:

The figure below presents the results of using tabcmd to publish a .hyper file using these commands. The tabcmd documentation, linked above, presents a more comprehensive list of available commands and options.

Perhaps you would like to skip the creation of .hyper files altogether, and instead send your data directly to Tableau Online from a KNIME workflow. This can be done using the Send to Tableau Server node. For this example, we send the raw Adult dataset to our Tableau Online server for additional visualization.

Double click the node to bring up the configuration dialog, and enter the required information under the Tableau Server Settings tab. Use the Browse button to have the node query the Tableau server for active projects. Here, we will be sending our file to the Adult Income project, and giving it a name of AdultRaw. Carefully note the format of the URL!

If we navigate to our project space in Tableau Online, we can now see three available data sources:

Once your data has been published to Tableau, you are free to create your visualizations in the usual way. Here are a couple of examples generated from the Adult dataset.The first presents a map of average ages for adults in the data set, by country of origin. The second is a stacked bar chart that shows, in descending order, the total hours adults worked, by gender, in each country.

Navigate to File → Install KNIME Extensions and type "Tableau" in the search box that appears and deselect the checkbox "Group items by category". When you do, you will see something like the screen below.

If you have Tableau 10.5 or later, you can use the .hyper format, which is created using the Tableau Data Extract API 2.0. Versions 10.4 and earlier use the older TDE format, which is created using the Tableau SDK. Select the extensions to install according to what you need. If you are not sure, you can install both, and tell KNIME which format you want to use later. The two extensions labeled KNIME Tableau Integration include the KNIME nodes, while the other extensions labeled Platform Specific Packages include the back-end support files - specifically, the Extract API or SDK files.

Once you have installed the extension(s), restart KNIME Analytics Platform and you should see the legacy Tableau nodes in the node repository under KNIME Labs. You can identify the legacy nodes by the suffix "(Hyper)" or "(TDE)". You can now drag and drop the Tableau nodes into your workflow, but you will need to perform some additional one-time setup on your system PATH before they will export data properly.

The error "Hyper Server did not call back on the callback port: process = -1" can appear randomly using a correctly configured setup with the "Tableau Extract API 2.0 (Hyper)".

This issue is related to the use of the "Tableau Extract API 2.0" and can be solved by using the new KNIME extension which makes use of the "Tableau Hyper API". Install the KNIME extension "KNIME Tableau Integration" as described in the Installation section and use the nodes "Send to Tableau Server" and "Tableau Writer".

This extension will be the default Tableau integration for the upcoming KNIME release.