Visual Analytics is the union of Data Analytics and Visualisations. This problem-solving approach is concerned with effectively facilitating high-level, complex activities such as reasoning and data-driven decision-making by integrating interactive visual representations with underlying analytical processes. Tableau is a Visual Analytics Engine that simplifies the creation of interactive visual analytics in the form of dashboards. These dashboards facilitate the conversion of data into intelligible, interactive visualizations for non-technical analysts and end-users.
In this article, you will gain information about Tableau Metadata API. You will also gain a holistic understanding of Tableau, its key features, Tableau Metadata, Tableau Metadata API, and GraphQL in Tableau Metadata API. Read along to find out in-depth information about Tableau Metadata API.
Table of Contents
What is Tableau?
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Tableau is a modern Data Analytics and Business Intelligence platform. It is an easy-to-use tool, hence, it offers a smooth experience to its users. Some of Its amazing features include real-time analytics, quick responsiveness, and interactive dashboards.
It also offers simple yet appealing graphics/visualizations that you can use to present your data pictorially. It comes with all the features needed for data extraction, data processing, and generating reports and dashboards.
It also offers a drag-and-drop functionality that makes it faster than other BI tools. It is also a very scalable tool, which gives it the capability to adapt to both individual and enterprise needs. You can also connect it to multiple data sources without the need to purchase a license. It is mobile compatible and it comes with an online version.
It can be used by all kinds of users and no specific skill or knowledge is needed to work with the tool. Users from any department in your company can use it for Data Analysis and Data Visualization.
Key Features of Tableau
Tableau is a powerful tool and is widely used by a lot of industries. To understand Tableau better let’s look at some of its key features:
1) Supports Multiple Data Sources
Since every task is performed on data in Tableau, it allows you to integrate your data from a large variety of data sources like:
- Microsoft Excel
- CSV files
- MS SQL Server
- Oracle
- IBM DB2
- Google BigQuery
- Windows Azure
- ODBC/JDBC, etc
You can make use of these integrations to stream your data to Tableau and analyze it seamlessly.
2) Houses a Wide Range of Visualizations
Tableau also provides a large number of simple tools for its users (both for technical and non-technical people) and empowers them to create different types of visualizations using their data. You can use these tools and create simple or complex visualizations using Tableau. Its key visualizations include:
- Scatter plot
- Line plot
- Pie Chart
- Bar Chart
- Bullet Chart
- Highlight Tables
- Gantt Chart
- Boxplot, etc.
Tableau’s Map features allow you to visualize your data on a geographical map. It is very useful if your data needs to be categorized region-wise or across various countries to help you analyze the performance of each region.
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3) Allows Data Filtering
With the help of Tableau, you can filter data from a single source or multiple sources. But the only condition that needs to be satisfied for filtering data from multiple Data Sources is that the data must have the same dimensions. Once this is satisfied, Tableau automatically updates the required changes to all your worksheets using the same Data Sources and the same filters that you set previously.
4) Dynamic and Real-time Dashboards
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You can build dynamic and interactive Dashboards using Tableau. Building Reports and Dashboards is made very simple by Tableau. You can make them more informative by adding colorful charts and diagrams. Using these real-time Dashboards, you can monitor everything in absolute depth for your organization. Tableau also houses a feature that allows you to share your Dashboards and Reports with other employees in the organization.
5) Powerful Collaboration
Every person can work a lot more efficiently if they understand their data and make informed decisions which are critical to success in any organization. Tableau was initially built to enable collaboration among employees. Using Tableau, all the members in a team can share their work, make follow-up queries with fellow peers, and share visualizations with any employee in the organization, allowing them to gain valuable insights easily.
Tableau gives its users the ability to work and understand the data they need from web editing and authoring to Data Source recommendations. You can easily publish your Dashboard to Tableau Server or Tableau Online within seconds and as a result, everyone in your organization can see your insights, ask various questions, and make the right decisions.
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The Tableau Metadata API discovers, tracks, and stores information about Tableau content before surfacing it.
The content can be classified according to its type (e.g., table or workbook). The content can be unique to Tableau (for example, embedded data sources and calculated fields) or it can have external assets that are not unique to Tableau (e.g., database tables and columns). Information can be attached to both content and external assets (e.g., tags and ratings). Both content and external assets can be related to other content and external assets.
Tableau Metadata is made up of the relationships between content and external assets, as well as information about each.
The different types of Tableau Content are as follows:
1) General Tableau Content
Tableau Content is the content that is unique to the Tableau platform. It includes the following:
- Data sources: Both published & embedded sources Workbooks
- Sheets: Inclusive of dashboards & stories
- Fields: calculated, column — as they relate to the data source, group, bin, set, hierarchy, combined, and combined set
- Filters: Data source
- Parameters
- Flows
- Metrics
2) Tableau Online and Tableau Server specific Content
The following Tableau content can only be managed through Tableau Online or Tableau Server:
- Sites
- Projects
- Users
- Certifications & certifiers
- Data quality warnings & messages
3) External assets associated with Tableau Content
The Metadata API considers any information that comes from outside of the Tableau environment to be external assets. External assets include the following:
- Databases include local files, remote server connections, and web data connectors (WDC)
- Tables include queries (custom SQL)
Note: Cubes are not supported
What is Tableau Metadata API?
The Tableau Metadata API seeks, discovers, and indexes all content on your Tableau Online or Tableau Server site, including workbooks, data sources, flows, and metrics. Indexing is used to collect information about Tableau content, or metadata, such as schema and lineage. The Tableau Metadata API then identifies all of the databases, files, and tables used by the content on your Tableau Online site or Tableau Server based on the metadata.
Benefits of Tableau Metadata API
The benefits of Tableau Metadata API are as follows:
- Discover data that is related to the content you’ve published on your Tableau Online or Tableau Server. Find external assets such as tables, databases, and data sources.
- Keep track of the lineage or relationships that exist between content and external assets such as data sources and workbooks. Determine which workbooks, for example, use a specific published data source.
- Conduct an impact analysis. You can assess the impact of content changes by using upstream and downstream lineage information. Find all worksheets that rely on a database table column, for example, or identify the authors who should be notified when a data source changes.
What is GraphQL in Tableau Metadata API?
The Tableau Metadata API is both fast and adaptable. When you need specific information about the relationships between content and assets or their structures, you can use the Tableau Metadata API.
The Tableau Metadata API makes use of GraphQL, an API query language that describes how to request and receive only the data that you’re interested in.
GraphQL APIs, like REST APIs, are served over HTTP. Instead of sending requests to multiple endpoints to retrieve the data of interest, you can make a single query to a single endpoint and have it filtered to return only what you ask for. A GraphQL server is linked to a single endpoint.
GraphQL is based on the concept of a graph (queries) and the relationship between the GraphQL schema’s types (objects).
I) Objects & their Roles in GraphQL in Tableau Metadata API
The GraphQL schema used by the Metadata API organizes content and assets on Tableau Online and Tableau Server by grouping them by object and role. These objects and their roles are the foundation of the GraphQL queries in Tableau Metadata API.
1) Parent Object & Role
The Metadata API’s GraphQL schema organizes content and assets on Tableau Online and Tableau Server by object and role. The GraphQL queries in Tableau Metadata API are built on these objects and their roles.
The Parent Objects are as follows:
- Databases
- Tables
- Published data sources
- Workbooks
- Metrics
- Flows
- Sites
- Projects
- Users
2) Child Object & Role
Child Objects, which are a combination of content and assets, cannot be managed independently of their parent object. As a result, child objects are dependent on their parent object.
Some child objects have the ability to own other child objects, refer to other child objects, and in some cases, refer to specific parent objects.
The Child Objects are as follows:
- Columns
- Fields
- Embedded data sources
- Sheets
- Parameters
- Data source filters
3) Parent/Child object relationship
The parent/child object relationship in GraphQL is defined by what the objects can own or contain. In other words, a parent object is one that serves as a container for other (child) objects.
| Parent Object | Child Object that Parent Object can Contain |
| Database | DatabaseTable , Column |
| PublishedDataSource | Field, DataSourceFilter, Parameter, DataQualotyWarning, DataQualityCertification |
| Workbook | View, EmbeddedDatasource, Field, Parameter |
II) Object Types & Inherited Attributes
The GraphQL schema organizes objects by object type and the attributes that it can inherit from the object, similar to the parent/child object role and relationship.
Objects are implemented in the GraphQL schema using “interfaces” to accomplish this. Object types inherit attributes and properties from each interface. This means that object types with the same interface are logically related to one another and share common attributes and properties. Object types can have unique attributes and properties in addition to shared attributes and properties.
For further information on Object Types & Inherited Attributes with detailed examples in GraphQL queries used in Tableau Metadata API, you can visit here.
And any information on any other Objects related to Tableau Content & Assets can be found here.
III) GraphQL Query Syntax
Once you have a better understanding of what GraphQL is, you need to understand what you need to include in your query.
As you can see from the sample code below, you need to name the query, specify the object you want to access (such as “tableauUsers”), and specify the associated attributes for that object (such as “username”). Some objects can also be attributes of other objects (for example, user-owned workbooks). In this case, you need to nest the “ownWorkbooks” object attribute in a new pair of curly braces.
query "query-name"{
<object> (<arguments>){
<attribute>
<attribute>{
<attribute>
}
}
}
In the output, the query returns only the data provided in the same format as provided in the query format.
Example of a GraphQL Query
In order to get information about users in Tableau Server and its workbooks you can use the following query:
query usersAndContent {
tableauUsers {
id
username
ownedWorkbooks {
name
projectName
createdAt
}
}
}
This query returns information about all users on the server, user IDs, usernames, and the workbooks that they own, such as workbooks name, workbook ID, name and project they own, and date of creation.
How to Access the Metadata API using GraphiQL?
If you are new to GraphQL, we recommend starting with GraphiQL, a graphical user interface (GUI) that you can use with Tableau Server or Tableau Online. It’s easy to use, has embedded Metadata API Documentation, and has autocomplete capabilities. You can access GraphiQL from https://<your-tableau-server>/metadata/graphiql. However, you need to authenticate access to the content available/present in your Tableau environment.
The image below shows the key components of the user interface:
What is Tableau Metadata Model?
The Tableau Metadata Model for a specific set of objects is defined by all of the objects, the roles they play, the relationships they have with one another, and their attributes and properties. The metadata model is a snapshot (or graph in GraphQL terms) of how Tableau interprets and relates a set of objects on your Tableau Online or Tableau Server. The metadata model is used by Tableau Metadata API to understand the dependencies and relationships in your data.
To understand how the metadata model used by the Tableau Metadata API works, you can go through the following example scenarios.
Assume you’ve published three workbooks (parent objects) and two data sources (parent objects) to Tableau Online or Tableau Server.
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The two published data source objects in this scenario refer to the three workbook objects. Four sheet objects are owned by the workbook objects. These sheet objects refer to two field objects owned by the published data source objects.
The Tableau Metadata Model used by the Tableau Metadata API in this example is as follows:
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- An ownership relationship is indicated by a solid line with an arrow.
- A reference relationship is indicated by a dotted line.
1) Example scenario 1: Impact analysis
In an impact analysis scenario, the metadata model can assist you in determining how data may be impacted if a component of the metadata model changes.
In this case, you might be curious about what might happen if the published data source, John County – 1, is removed.
Sheet objects are owned by workbook objects in the metadata model, and sheet objects can refer to field objects. The published data source objects own the field objects in this scenario. Field objects are owned by published data source objects. As a result, if John County – 1 is deleted, the child objects, F1, Hills Library, and Garden Library, are directly affected and their existence is jeopardized due to their reliance on that data source object. The existence of the other child objects, F2, Garden Senior Center, and Cliff Senior Center, is not jeopardized by the deletion of the data source object.
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Because John County – 1 does not own the workbook objects that connect to it, the workbook objects themselves, Sakura District and Maple District can continue to exist in the absence of the published data source object, according to this analysis.
2) Example scenario 2: Lineage analysis
In a Lineage flow analysis scenario, you can examine a specific part of the metadata model to determine where the data is coming from and how the data reacts to or is affected by various parts of the metadata model.
In this case, you might be curious about the origin of a data point from a sheet object, Garden Senior Center, in a workbook object, Maple District.
According to the Tableau Metadata model, attributes are inherited from the parent object. As a result, if you begin with the Garden Senior Center sheet object, you can move to the referring field object, F2, to see if it is owned by a published data source object. In this case, John County – 2 is the data point’s source.
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You can freely include or exclude parts of the Tableau Metadata model during this analysis to better understand the lineage flow for Garden Senior Center. You can, for example, choose to exclude the Garden Library sheet object, despite the fact that it is also owned by the same workbook object, Maple District.
For further information on Tableau Metadata API, you can visit here.
Conclusion
In this article, you have learned about Tableau Metadata API. This article also provided information on Tableau, its key features, Tableau Metadata, Tableau Metadata API, GraphQL in Tableau Metadata API.
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Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has written more than 100 articles on diverse topics related to data industry.