Tableau Architecture & Server Components

Tableau Architecture

Tableau Server is developed to connect various data tiers. It connects clients from the web, Desktop, and Mobile. Tableau Desktop is a strong data visualization tool. It is highly available and secure. It works on both virtual machines and physical machines. It is a multi-user, multi-threaded, and multi-process system. In this Tableau server architecture(Tableau Architecture) blog, you will learn about the different layers of the Tableau server. 

Tableau Architecture - Table of Contents

What is Tableau Server? Tableau Server Architecture Data Server Data Connectors Components of Tableau Server Gateway Client

What is Tableau Server?

Tableau server is developed in such a way for connecting various data tiers. It connects clients from mobiles, desktops, and the web. The tableau desktop is a robust data visualization tool. It is highly secure and available. It can run on both physical and virtual machines. It is a multi-process, multi-threaded, and multi-user system. We can install the Tableau Server on Google Cloud Platform, Amazon EC2, Alibaba Cloud, and MS Azure. 

Multiple server processes operate together for providing the services in different tiers. As Tableau Server Integrates with a number of elements in our IT Infrastructure, it needs a strong architecture. 

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Tableau Server Architecture

Following are the different components of the Tableau Server Architecture:

1. Data Server

We can create the database connection in two ways; a live data connection that transmits immediate queries to the data source and retrieves results immediately. Another method is extracting the data from the data source and including the local copy of it as the temporary database. We can fetch the data through the live extraction or connection into both Tableau Server and Tableau Desktop.

2. Data Warehouse

Data Warehouse helps us to solve big data challenges from disparate and disorganized data sources with lengthy analysis time. In spite of the name, it is not just one huge database or dataset. As the system used for data analysis and reporting, the warehouse combines several enterprise data sources and is an essential component of business intelligence.

3. Enterprises Data Warehouses

They are suitable for comprehensive business intelligence. They maintain data organized and centralized for supporting advanced data governance and analytics requirements because they deploy with the available data architecture. They turn the essential information hub throughout the processes and teams, for unstructured and structured data. Snowflake is the industry leader in data warehouse solutions.

4. Data Marts

Data Lakes or Data Marts are the subsets of the data warehouse - not a warehouse substitute. They are more particular locations for data, commonly dedicated to one specific business group or business line, such as sales. They support advanced big data analytical needs using rapid and more flexible data ingestion and data storage for anyone to fastly analyze primary data in various ways.

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5. Files

In Tableau, we can save the results of the data analysis in different formats, to be distributed and saved. The different formats are called different file types and we can identify them using different extensions. The formats of the results rely on how we produce them and for what intentions we use them. We can store them in XML files, which we can open and edit.

6. Cubes

A Cube data source is a data source in which the cube’s designer creates aggregation and hierarchies. Cubes are very strong and can retrieve information very rapidly, frequently much faster than the relational database. But, the reason for the speed of the cube is that all its hierarchies and aggregations are pre-designed. Cube data sources(also called OLAP data sources or multidimensional) have particular characteristics that distinguish them from relational data sources when we use them in Tableau.

7. Data Connectors

Data connectors offer the interface for connecting external data sources with Tableau Data Server. Tableau has a built-in ODBC/SQL connector. We can connect this ODBC connector with all the databases without using their original connectors. Tableau Desktop has a choice for selecting both live and extracts data. According to the users, we can simply switch between extracted and live data. 

• Real-time Live Connection or Data: We can connect Tableau with real-time data by connecting to the explicit database directly. It utilizes the infrastructure available database by sending dynamic SQL statements and multidimensional expressions(MDX). We can use this feature as a connection between Tableau and live data instead of importing the data. It makes a rapid and optimized database system. Mainly in other companies, the database size is large and they will update it regularly. 
• Extracted or Active Memory Data: Tableau is the choice of extracting the data from explicit data sources. We build a local copy in the form of a Tableau extract file, we can delete millions of records in the Tableau data engine through a single click. The data engine of Tableau utilizes storage like Cache, ROM, and RAM for storing and processing the data. Through filters, Tableau extracts a few records from the large dataset. This enhances performance, particularly when we work on huge datasets.

Components of Tableau Server

1. Application Server

We use the application server for providing the authentications and authorizations. It manages the administration and permissions for web and mobile interfaces. It provides an assurance of security by recording every session-id in the Tableau server. The administrator is configuring the default timeout of the session in the server.

2. VizQL Server

We use the VizQL server for converting the queries from a data source into visualizations. After the client request is forwarded to the VizQL process, it passes the query directly to the data source for retrieving the information in the format of images. This image or visualization is displayed to the users. Tableau server generates the cache of visualization for reducing the load time.

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3. Data Server

We use Data Server for storing and managing the data from explicit data sources. It is the central data management system. It offers metadata management, data security, driver requirements, data storage, and data connection. It saves the associated details of the data set like metadata, calculated fields, parameters, sets, and groups. Data source extracts the data and makes live connections with explicit data sources.

4. Gateway

It directs the requests from the users to the tableau components. When the client passes the request, it is passed to the explicit load balancer to process. Gateway operates as the distributor of the processes to distinct components. In the absence of an external load balancer, the gateway also operates as the load balancer. For the single-server configuration, one primary server or gateway handles every process. For the multiple server configurations, one physical system works as the primary server, and others are utilized as worker servers.

5. Clients

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

• Web Browsers: Web Browsers like Safari, Firefox, and Google chrome support Tableau Server. The dashboard contents and visualization can be edited through these web browsers.
• Tableau Desktop: It is a business analytics tool. We use it for creating, publishing, and viewing the dashboards in the Tableau server. We can access several data sources and develop visualizations in the Tableau Desktop.
• Mobile Applications: The dashboards from the server can be collaboratively visualized through mobile applications and browsers. We can use mobile applications for viewing and editing the workbook contents.

Conclusion

The tableau server architecture links different data sources securely. It can also connect live and real-time data by linking the database directly. It also retrieves the local copy of the data using its in-built data store for rapid processing. I hope this Tableau server architecture article provides you with the required information about Tableau server architecture.

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