Tableau Architecture and Ecosystem Components

Tableau is a popular Business Intelligence (BI) and data visualization platform. Organizations use the platform to connect with various data sources and analyze data. You can mainly build visually appealing dashboards and insightful reports with Tableau.

If you want to become a skilled data analyst, business analyst, data scientist, or BI developer, gaining Tableau skills is a big advantage. Coherent Market Insights states that the business intelligence and data analytics market will hit $95.8 billion by 2033.

This article provides a detailed discussion of Tableau's architecture and ecosystem components for both budding and experienced data professionals.

Table of Contents:

• Tableau Desktop and Tableau Cloud – An Overview
• Tableau Server – Overview
• Tableau Server Architecture
• Tableau Server Components
• Tableau Cloud vs Tableau Server Architecture - Differences
• Tableau Ecosystem Components
• Tableau AI Components
• Tableau Next
• Tableau Drivers
• Frequently Asked Questions

Tableau Desktop and Tableau Cloud – An Overview

When exploring the Tableau architecture, it is best to start with Tableau Desktop and Tableau Cloud.

Tableau Desktop is a business analytics and robust data visualization tool. You can use it to create, edit, and publish dashboards on Tableau Server. You can access several data sources and develop visualizations in Tableau Desktop.

It can run on both physical and virtual machines. It is a multi-process, multi-threaded, and multi-user system. Tableau Desktop offers the option to select both live and extracted data. You can simply switch between extracted and live data.

Furthermore, it works on both virtual machines and physical machines.

• Tableau Cloud

Tableau Cloud is a fully managed cloud-based data analytics platform. It enables robust business intelligence in the cloud. You can publish, share, and collaborate on dashboards using  Tableau.

It is a SaaS solution hosted and maintained by Salesforce, eliminating the need to install and maintain servers. Also, software updates are automatically applied in Tableau Cloud.

Architecture of Tableau Cloud:

The architecture consists of components such as users, authentication, data sources, and Tableau Bridge. Let’s discuss them in detail below.

• Users

Tableau Cloud allows users to access dashboards through web browsers or the Tableau Mobile application.

• Authentication

Tableau Cloud supports various authentication mechanisms, including SAML, OpenID Connect (OIDC), OAuth, and multi-factor authentication (MFA).

• Tableau Bridge

You can use Tableau Bridge to securely access on-premises databases. Mainly, it supports live queries and scheduled extract refreshes.

• Data Sources

Tableau Cloud directly connects to multiple cloud databases, including Snowflake, Google BigQuery, Amazon Redshift, and Salesforce.

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Tableau Server – An Overview

Tableau Server is an analytics platform with many components, including gateway, VizQL Server, backgrounder, data server, cache server, and file store.

Tableau Server is developed to connect various data tiers. It connects clients from mobile devices, desktops, and the web. Tableau Server can be deployed on Windows and Linux servers on Google Cloud Platform, Amazon EC2, Alibaba Cloud, Microsoft Azure, or on-premises infrastructure.

Furthermore, Tableau Server stores metadata in the repository and uses hyper extracts to perform in-memory analytics.

Tableau Server Architecture:

As Tableau Server integrates with many elements of our IT infrastructure, it requires a robust architecture. Tableau Architecture is an n-tier client-server architecture that supports web, desktop, and mobile clients.

This architecture securely links to different data sources. It can also connect to real-time data by linking directly to databases.

The following are the different components of the Tableau Server Architecture:

• Data Server:

Tableau Data Server manages the shared published data sources, virtual connections, data policies, and hyper extracts.

• Data Marts:

Data Marts are subsets of a data warehouse. They are more specific locations for data, commonly dedicated to a specific business group or line, such as sales.

They support advanced data analytics with rapid, more flexible data ingestion and storage. It enables data professionals to quickly analyze data in various ways.

• Files:

In Tableau, you can save the results of the data analysis in different formats for sharing and storage. The different formats are called file types, and you can identify them by their extensions.

The formats of the results depend on how you will produce them and for what purposes you will use them. You can store them in .twb, .twbx, and .hyper files.

• Data Connectors:

Data connectors offer the interface for connecting external data sources with Tableau Data Server. Tableau has a built-in ODBC/SQL connector. You can use this ODBC connector to connect to databases without using their original connectors.

Additionally, Tableau supports native connectors, JDBC, Salesforce connectors, and cloud warehouse connectors. Note that ODBC is used when no native connector is available.

• Real-time Data Connection:

You can use this feature to connect Tableau to live data rather than import it. You can connect Tableau to real-time data by connecting directly to the underlying database. It uses the available database infrastructure by sending dynamic SQL statements and multidimensional expressions (MDX).

Now that you have gained some exposure to server architecture. It is time to explore the components of Tableau Server next.

Components of Tableau Server:

Let’s discuss the components of the Tableau Server in this section.

• Application Server: 

You can use the application server to provide the authentication and authorization. It manages permissions for web and mobile interfaces.

It provides security assurance by recording every session ID in Tableau Server. Administrators can configure the default session timeout on the server.

• VizQL Server:

You can use the VizQL server to convert queries from a data source into visualizations. After the client request is forwarded to the VizQL process, it generates visualization instructions that browsers offer interactively.

Further, the image or visualization is displayed to the users. Tableau Server caches visualizations to reduce load time.

• Data Server:

You can use Data Server to store and manage data from published data sources. It enables data security, storage, and connectivity. 

It stores the associated details of the data set, such as metadata, calculated fields, parameters, sets, and groups. It extracts data and establishes live connections to explicit data sources.

• Gateway:

Gateway routes user requests to the Tableau components. When the client submits a request, the gateway routes it to the appropriate Tableau Server processes.

Gateway operates as the request distributor of the processes to distinct components. When there is no external load balancer, the gateway also operates as the load balancer. 

Further, in the single-server configuration, a single primary server or gateway handles all processes. In multi-server environments, one physical system serves as the primary server, and the others serve as secondary servers.

• Backgrounder

You can use Tableau Backgrounder to perform scheduled refreshes, subscriptions, flow execution, and extract refreshes.

• Repository Stores

Developers use repository stores to store metadata, user information, permissions, and workbook metadata.

• Coordination Service

This service replaces ZooKeeper and coordinates communication between different Tableau Server processes. It enhances server reliability, availability, fault tolerance, and multi-cloud deployments.

• Clients

In Tableau, you can edit and view dashboards and visualizations through different clients. Clients are mobile applications, Tableau Desktop, and Web browsers.

• Web Browsers

Tableau Server supports web browsers such as Safari, Firefox, and Google Chrome. The dashboard content and visualizations can be edited in these web browsers.

• Mobile Applications

Server dashboards can be collaboratively visualized via mobile applications and web browsers.

[ Check out Tableau Dashboard Best Practices ]

Tableau Cloud vs Tableau Server Architecture - Differences

Both Tableau Cloud and Tableau Server are Salesforce products, but they differ in many ways. Let’s compare the architectures of Tableau Cloud and Tableau Server against multiple factors in the table below.

Features	Tableau Cloud	Tableau Server
Deployment	It is hosted as SaaS by Salesforce.	It can be deployed on both servers and the cloud.
Architecture Type	Multi-tenant architecture	Single-tenant and self-managed architecture
Hardware Requirement	Hardware is not required.	It requires dedicated servers and Virtual Machines (VMs).
Software Updates	Updates are made automatically.	Updates are made manually.
Maintenance	You don’t need to worry about maintenance.	Frequent maintenance is required.
Disaster Recovery	Salesforce manages disaster recovery.	You need to manage disaster recovery.

Tableau Ecosystem Components:

We’ll go through the Tableau ecosystem components in this section. Tableau Desktop, Tableau Cloud, and Tableau Server are key components of the Tableau ecosystem, in addition to the following:

• Tableau Prep: It is a data preparation tool. You can use this tool to connect, clean, combine, reshape, and prepare data before analysis. It easily connects to multiple data sources, including databases, spreadsheets, and more.
• Tableau Public: It is a cloud-based platform that helps you create, publish, and share interactive dashboards. It simplifies sharing dashboards using a URL.
• Tableau Mobile: This tool allows you to access Tableau Desktop visualizations on mobile devices. It easily connects to Tableau Cloud and Tableau Server, enabling you to monitor business metrics and make data-driven decisions.

[ Check out Tableau Subscription and Report Scheduling ]

Tableau AI Components:

Let’s discuss Tableau AI components that play crucial roles in its architecture.

• Tableau Pulse - It is Salesforce’s AI-powered analytics solution that organizations use to gain insights into their business metrics. It provides proactive notifications when key metrics change significantly.
• Tableau Agent - It is an AI-powered assistant you can use to analyze data in natural language. Not only does it create charts and graphs, but it also explains trends and suggests calculations in natural language.
• Einstein Discovery - It is an AI-powered analytics feature of Salesforce. It uses ML models to analyze data, identify patterns, and make predictions. Besides, it enables root cause analysis to identify the factors that impact business performance.
• Einstein Trust Layer: A Salesforce AI framework that helps you protect sensitive data, ensure compliance, and enforce policies. It acts as a bridge between Salesforce applications and LLMs.
• Agentforce: Salesforce’s AI-powered platform for building and deploying autonomous AI agents. Agentforce can perform multi-step tasks, unlike traditional chatbots. Above all, you can interact with Agentforce in plain English.

Tableau Next:

Tableau Next is Salesforce’s next-generation AI analytics platform. It integrates Tableau Analytics with Agentforce, generative AI, and a semantic data layer. The combined power of these tools helps explore data in-depth, automate data analysis, and make data-driven decisions.

Tableau Next is Salesforce’s next-generation AI analytics platform. It integrates Tableau Analytics with Agentforce, generative AI, and a semantic data layer. The combined power of these tools helps explore data in-depth, automate data analysis, and make data-driven decisions.

This platform uses the Einstein Trust Layer to protect sensitive data, apply user permissions, and implement governance controls.

Tableau Drivers:

Tableau drivers are basically software components. It helps connect Tableau Desktop, Tableau Prep, and Tableau Server to external data sources.

Let’s now take a look at the various Tableau drivers below:

• Native connectors

You can use these drivers to connect to specific databases such as Oracle and IBM Db2.

• ODBC Driver

You can use the Open Database Connectivity (ODBC) driver to connect Tableau to many relational databases such as MySQL and PostgreSQL.

• JDBC Driver

You can use this Java Database Connectivity driver to connect to the Tableau Server and Tableau Prep with databases.

• Tableau Essbase Driver

This driver allows Tableau to connect directly to an Oracle Essbase multidimensional (OLAP) database. You can analyze cube data without exporting it to flat files.

It supports features such as hierarchies, shared members, default members, and the accounts dimension.

Generally, Tableau drivers help connect Tableau products to modern platforms and databases, including Snowflake, BigQuery, Amazon Redshift, Azure SQL, and Salesforce Data 360.

Tableau distinguishes drivers into different categories as shown below:

• Fully Functional

A driver that supports multiple functions and capabilities, including those used by Tableau, is known as a fully functional ODBC driver.

• Major Limitations

Drivers that don’t support the major sets of capabilities critical to Tableau are categorized here. These drivers might support extracting the details into Tableau on the first run, but might not be able to apply any further filters to the extracted data, etc.

• Fatal Limitations

These drivers do not support even the minimal set of capabilities required for Tableau to connect. So Tableau can't proceed with the connection in such cases.

After a successful connection is made, Tableau will notify us of any limitations identified for the data source and its associated ODBC driver.

Frequently Asked Questions:

1) What is Tableau architecture?

Tableau architecture is a framework that describes how data flows from source systems to Tableau dashboards. It consists of several layers and components that you can use for analytics and reporting.

2) What is the latest version of Tableau?

Tableau 2026.2 is the latest version of Tableau.

3) Is Tableau available for free?

Yes, you can use the free version of Tableau via this link.

4) What components does the Tableau ecosystem include?

Tableau components include Tableau Desktop, Tableau Server, Tableau Cloud, Tableau Prep Builder, Tableau Public, Tableau Mobile, and Tableau Pulse.

5) What is Tableau Agent?

Tableau Agent is an AI-powered analytics assistant. You can use the agent to interact with data in natural language. It summarizes key insights in natural language and provides a brief on trends and anomalies in the data.

6) What is Einstein’s Trust Layer?

Salesforce’s AI framework helps organizations protect their sensitive data. It ensures that interactions between Salesforce applications and LLMs are secure and compliant.

7) What is the architectural difference between Tableau Cloud and Tableau Server?

Tableau Cloud is a fully managed SaaS solution, whereas customers manage Tableau Server in their IT infrastructure. In Tableau Cloud, Salesforce handles updates and patches, whereas the customer handles updates, patches, and backups in Tableau Server.

Conclusion

I hope this article has provided you with the information you need about Tableau Server architecture. You have also learned in detail about Tableau ecosystem components and Tableau drivers.

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