Tableau Data Management - Tableau Tutorial

- [Instructor] As organizations build out their data culture, their needs also evolve. This changes the way they use the products we've covered so far in very subtle ways and business need often emerges to either handle these subtle elements or do things better. An interesting dynamic in the last few years is that Tableau has generally sought to meet some of these evolving needs in new ways, beyond the usual licensing mechanics we've seen in the past. Instead, they've taken some of the needs they would consider niche and have developed additional products to sit alongside the main platform. When they launched this suite of products, they were called add-ons, and although Tableau, now Salesforce, generally shifted away from this terminology, I think that's the clearest way to understand the way they work. You add additional products to the existing platform to enable or switch on an enhanced set of features. The first one we're going to look at is called Tableau Data Management. Now, earlier we talked about Tableau Cloud and Tableau Server, and the fact that they act as the connective tissue that brings the platform together. An important part of that platform is of course, data. In any business scaling its data culture, several challenges arise. As the number of data source grows, you want to enable users to continue to connect freely to data they have access to. That said, you'll still want to maintain a high level view of how data is being used so you can propagate change centrally where necessary. And in some cases, make it even easier to connect to data whilst giving your data security teams the ability to build a security model at connection. You'll also want to allow automation to become an important element of how data is kept fresh so that once an analyst builds a workflow, the output from that stays up to date and potentially serves many more analysts. To serve all these needs, Tableau Data Management has a set of features to address these needs. The first one is Tableau Prep Conductor. This allows you to schedule and automate your flows so that any prep flow you've built can serve up fresh data that drives dashboards experiences, such as an Ask Data view and many other workflows. It also supports capability to link flows so you can orchestrate related or unrelated flows to help reduce the load on your database or ensure that data sources are created in the right sequence for more complex pipelines. In some cases, flows might fail and you'll want to notify downstream workbooks and assets of an issue. To do that, Tableau Data Management allows you to add data quality warnings on your data sets to notify end users or authors of issues related to data sets or changes that might come into effect soon. These can also be automatically applied in the instance that a flow or a link flow fails to update a data set. This sort of information is typically called metadata and that's the information that gives you more context about your data, such as when it's created or in this instance, it's quality. On the topic of metadata, Tableau Data Management also allows you access to the Tableau catalog, a repository of all your assets and sources stored as metadata that powers data lineage capabilities which are presented visually. This allows you to look at any asset on the server and look upstream to see what data sources, database, and even servers are being used or downstream to assets that interact with it, all the way down to calculations and even comments in calculations. This visibility allows you to make better decisions about your data strategy, monitor usage patterns and identify areas where training and development might be necessary to help steer best practice. One feature we will mention here is the metadata API. Think of this as an API that allows you to query this metadata to either analyze it in your tool of choice or ingest it into a central metadata store for a centralized view of your infrastructure. The last capability we'll cover is a fairly recent one, virtual connections and link to that row-level security for that feature set. In a nutshell, virtual connections allow you to centrally build and define data sets that can be used through a single connection. This connection can include single or multiple tables from a data source. The benefit here is that if anything changes, making a change to the virtual connection will propagate that change to all downstream assets, which is a much simpler and cleaner workflow and avoids duplication of data sets that serve the same purpose. On top of this, you can also bake in row-level security with policies that define what access specific users or groups have. It has a well considered policy and entitlement relationship configuration that means that wherever data is used, the security policies applied to it all have consistent behavior, especially where sensitive data is involved. As the landscape evolves, no doubt Tableau will add more capability to this product so be sure to visit the Tableau website to stay up to date with the capabilities being added. And also take a look at the Tableau Release Navigator, a resource designed to show you changes in the platform by product and release.