Embedding

Embedding with Tableau Online – 2020 Edition

Given all the updates to Tableau Online over the last few years, now seems like as good of time as any to review over when Tableau Online works for embedding Tableau.

What do I mean by embedding in this case? Specifically, to display Tableau visualizations in another web application, without the customer being aware of Tableau Online as a separate entity from the rest of the web application.

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Attribute-Based Row Level Security in Tableau, a Working Technique

Tableau has the most flexibility for doing Row Level Security filtering using what is termed the “Standard Method”: a JOIN between a Data Table and an Entitlements table. The basic assumption, as seen in the RLS Whitepaper, is that this Entitlements table exists in the same database at the Data Table. This is a very narrow assumption, that is only true for a small subset of real deployments.

In many environments and applications, there is no “Entitlements” table within the same logical database as the Data table. In fact, there may be no “Entitlements table” that can be reached at all — entitlements can be retrieved somehow, but they may be in any number of forms.

The most common request I see is to pass a set of Attributes (lists or arrays) of values directly into a Tableau report at run-time. The rest of the post will describe a flexible and generic method for making this possible in a secure way. It builds upon the baseline of the PostgreSQL/Python environment in the using a Web Service as a Data Source post, but is even simpler.

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Trusted Tickets Best Practice Implementation

For a variety of reasons,Trusted Authentication / Trusted Tickets is often the best choice for SSO into a Tableau Server. Trusted Authentication actually establishes a Tableau Session in the browser, so you can use it once to establish SSO for a whole browser session.

In this post, I’ll describe a best practice method for implementing the Session-Level Sign-O and provide a small JavaScript file with actual implementation of all the concepts.

You can find the auth_helpers.js file on GitHub with all the code necessary for this process (except for the back-end Trusted Tickets request, which you need to build in your own web application environment).

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Generating Good Thumbnails / Previews using Tableau Server REST API

The Tableau REST API has very convenient endpoints for retrieving a Workbook or View Preview Image. Unfortunately, there are many conditions where Tableau Server itself doesn’t generate anything other than the gray “User Filtered View” thumbnail. This post describes a process for using the full Image (not preview) REST API endpoints to generate attractive thumbnails images.

One of the conditions that results in a gray generic thumbnails is any workbook that is connected to a Published Data Source that has Row Level Security set up through Data Source Filters.

The basic workflow is:

  1. Request all of the Views from a Workbook (or the Default View if you only need thumbnails at the Workbook level) for one particular user, who you know has enough data access to result in a good looking thumbnail
  2. Call the actual Image endpoints (instead of the Thumbnail / Preview endpoints) for every View you need
  3. Reduce the images to thumbnail size
  4. Cache the thumbnails for efficient retrieval by your embedded web application

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Passing a JWT as a Tableau Parameter

You’re probably aware that Tableau Parameters are not an inherently secure way to filter down data. A lot of people are using JWTs to pass around tokens with security entitlements, so if you are good with that, did you know you can pass one right into a Tableau Parameter?

I’ve built out an example where the JWT comes through then a function fires off a web service request, as part of the work for using a Web Service / REST API as a live data source, but there’s no reason you couldn’t build a smaller function for processing the JWT and then use just that in Custom SQL in Tableau when building your data source.

Although the following example uses PostgreSQL (and PL/Python), you could theoretically implement this in any language with access to a language that can process JWT. In MS SQL Server, a CLR Stored Procedure can access C# functionality, and the Systems.IdentityModel.Tokens.Jwt namespace appears to have all the necessary functionality to implement a very similar workflow.

Below I’ll work through a workflow using PL/Python on PostgreSQL — again, the same concept could be implemented on any RDBMS with functions.

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Apply All buttons in JavaScript and Extensions API

Both the Tableau JS API and Extensions API send commands using Asynchronous JavaScript, based on the Promises JavaScript framework. If you are not well-versed Promises or asynchronous JavaScript in general, it can be difficult to go from the basic examples in the reference guide to more complicated solutions.

Over the years, customers have frequently asked about doing an “Apply All” button using the JavaScript or Extensions API, where filters on multiple different fields can all be applied from a single button click. While there is no way to do this directly in either API at the current time, you can create a process that very efficiently applies all of the filter changes as quickly as possible. This article will show one solution to the “Apply All button” request.

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Building a Flexible Extract Generator using the Extract API

One of the least mentioned, but incredibly useful APIs in Tableau is the Extract API, which allows you to programmatically create an Extract file (Hyper files starting in 10.5, previously TDE files). The main use case is for data sources that require programmatic access (as opposed to using the one of the native connectors in Tableau). Some situations where this would be useful:

  • Data coming from a Web Service/ RESTful API with an object response
  • ODBC / JDBC drivers that Tableau cannot use
  • Additional programmatic modeling / statistical analysis against a whole data set

This post is focused mostly on first use case, where you are trying to make data available from some type of Web Service / RESTful API. In particular, if you need to provide only a subset from a very flexible set of possible fields for “ad hoc” analysis, this technique is the most functional solution to the problem.

When should I build a Flexible Extract Generator?

If you:

  • Know the structure of your web service responses
  • The amount of total fields is reasonably sized
  • The web service responses will not change frequently
  • Workbooks are fully built out and will not allow web editing
  • Data Source structure can be reused across multiple reports (and possibly customers)

then the better solution for Web Service/REST API based data sources is “Live” Web Services Connections in Tableau.

If instead you want to provide a selection screen to generate an Extract that will power a Web Edit session, then it makes sense to build a Flexible Extract Generator process. This is particularly useful when the set of fields could change drastically from extract to extract, or if other processing (such as machine learning) needs to be applied based on differing parameters prior to its use by the end user (that said, if the actual output columns are consistent, the “Live” Web Services solution could still work).

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“Live” Web Services Connections in Tableau

Many organizations have begun standardizing on a “Web Services” layer for access to reporting data, often with a restriction on directly connecting to the underlying data stores that power the Web Service responses. In the majority of cases, the result is a set of RESTful endpoints returning JSON object data, but for the purposes of this article, any variation that involves HTTP requests and responses in a “web-friendly” response format (JSON / XML) will be referred to as “Web Services”.

There are many reasons for adopting this architecture, and I’m here neither to recommend or pass judgement. There is one major implication to this architectural decision though — BI systems that expect a relational model and SQL-compliant querying capabilities do not have a native, natural way to handle these data responses. Tableau falls in this category (I don’t care about any others, but it’s not an issue exclusive to Tableau).

Tableau provides a Web Data Connector technology which helps individual analysts retrieve data from Web Service Data Sources, but current design does not account for data sets to vary depending on the user looking at the workbook, something essential for scalable and secure Tableau Server reports.

However, Tableau’s ability to connect live to a wide range of relational data sources allows us to construct an alternate architecture for accessing Web Services responses “live”:

Full Embedded Web Services Architecture

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Keeping Web Edit Content Private

Tableau’s behavior for saving content when using Web Edit follows these rules:

  1. If you are the Content Owner, you can Save or Save As
  2. If you are not the Content Owner, you can Save As

Save As is only allowed to Projects where you (or the groups you belong to) have a Save permission set to “Allow”.

Since a newly Saved Workbook will take the Default Permissions of the Project it saves into, if other people also have permissions for that same Project, they will also be able to access that content. This leads to several different strategies for controlling the privacy of content created through Save As.

Possible solutions:

  • A Project Per Team / Group
  • A Project Per User
  • A REST API script that “fixes” Permissions
  • Publishing a New Copy rather than Save As

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Row Level Security using Microsoft Analysis Services Cubes in an External- Facing Environment

Later versions of Microsoft Analysis Services (MSAS) allow you to configure user and role based data security within the cube itself. However, this functionality only works when that particular user is logged in directly to the cube. In Tableau, this can be accomplished via Kerberos.

What about when you are using MSAS cubes in an external facing solution, with users who are not in the local domain? Cube connections in Tableau don’t have the equivalent of a Data Source Filter the way relational database connections do, and there is no way to pass the USERNAME() function into a Calculated Member the way you can in a relational calculated field.

In this case, the manual “User Filter” functionality can achieve a reasonable solution.

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