Tableau Architecture is an n-tier client-server architecture that aids web clients, desktop-installed, and mobile clients software. Tableau offers different robust features, so Tableau architecture plays a vital role in understanding its functionality. The tableau architecture contains components like Data Warehouse, Data Marts, Cubes, Data Connectors, and Files.
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|
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.
|If you want to enrich your career and become a professional in Tableau, then enroll in "Online Tableau Certification Training" - This course will help you to achieve excellence in this domain.|
Following are the different components of the Tableau Server Architecture:
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.
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.
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.
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.
[ Check out Tableau Dashboard Best Practices ]
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.
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.
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.
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.
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.
[ Check out Tableau Subscription and Report Scheduling ]
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.
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.
In Tableau, we can edit and view dashboards and visualizations through different clients. Clients are mobile applications, Tableau Desktop, and Web browsers.
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.
If you have any queries, let us know by commenting in the below section.
Stay updated with our newsletter, packed with Tutorials, Interview Questions, How-to's, Tips & Tricks, Latest Trends & Updates, and more ➤ Straight to your inbox!
|Tableau Training||Jun 03 to Jun 18|
|Tableau Training||Jun 06 to Jun 21|
|Tableau Training||Jun 10 to Jun 25|
|Tableau Training||Jun 13 to Jun 28|
Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
Copyright © 2013 - 2023 MindMajix Technologies