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.
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).
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:
- 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
- Call the actual Image endpoints (instead of the Thumbnail / Preview endpoints) for every View you need
- Reduce the images to thumbnail size
- Cache the thumbnails for efficient retrieval by your embedded web application
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.
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?
- 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).
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”:
Tableau’s behavior for saving content when using Web Edit follows these rules:
- If you are the Content Owner, you can Save or Save As
- 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.
- A Project Per Team / Group
- A Project Per User
- A REST API script that “fixes” Permissions
- Publishing a New Copy rather than Save As