# Tableau Architecture For Integrations Tableau and Treasure Data together empower data-driven companies to rapidly explore data and get insights. By combining these two solutions, you can focus on insights, not infrastructure, with an industry-leading visual analytics tool. While Treasure Data is a BI tool agnostic service, customers use Tableau for the BI and Visual Analytics. In this article, we showcase how to combine Treasure Data, Tableau Desktop, and Tableau Server/Cloud. ## About Tableau Tableau is a business intelligence software that helps people see and understand their data. There are two major products provided by Tableau Software: Tableau Desktop and Tableau Server. Tableau Desktop is a Desktop Application used to visualize and analyze data. It helps create workbooks, visualizations, dashboards, and stories. ![](/assets/tableau_features.b1c89679628dd52a8cb1d3d1040ae75878f0ffa070157abafc9b96b0dec8b551.d76444dc.jpg) Users can publish visualized data to Tableau Server for sharing within an organization. Tableau Desktop is a BI designer tool, and Tableau Server is a publishing environment to share the visualizations. Tableau Online is a hosted version of Tableau Server, which doesn’t require you to manage the BI server. - [Tableau Desktop Product Tour (Video)](https://www.youtube.com/watch?v=37Mx3uZRwBE) - [Tableau Server Product Tour (Video)](https://www.youtube.com/watch?v=uGgkiBhkRHk) - [Treasure Data Introduction (Video)](https://www.youtube.com/watch?v=pk7oAN_nH4w) - [Treasure Data Technical Overview & Concepts (Video)](https://www.youtube.com/watch?v=lFxJgTD5eqw) ## Tableau and Treasure Data’s Reference Architecture You combine Treasure Data and Tableau because Treasure Data provides a scalable backend to handle new customer data sources (application logs, web logs, mobile data, sensor data, etc), while Tableau provides flexible visual analytics for existing data sources (EDW, CRM, ERP, etc). By combining Treasure Data and Tableau, you can quickly get insights on any type of data sources of any size, as illustrated in following architecture diagram. ![](/assets/tableau-architecture-1.23b0683171b26ad08430e5d39569430bfc2a7dee56befe612d733570bf593b00.d76444dc.png) ## Approach 1 - Use Activation to Tableau To provide a better experience for the Data consumers, it’s a good idea to summarize this raw data into smaller sets for performance reasons. By using Treasure Data's query engines, you can crunch big data into an aggregated format. Treasure Data supports ‘Result output/Activate to Tableau’ so you can directly push the aggregated results into Tableau Server/Cloud. You don’t need any additional infrastructure to do this. You can even automate this process by using [scheduled jobs](https://docs.treasuredata.com/smart/project-product-documentation/scheduling-jobs-using-td-console) / Activation from Audience Studio to periodically aggregate the data. Treasure Data can push the query results as a [Tableau Data Hyper format file](https://tableau.github.io/hyper-db/docs/#:~:text=Hyper%20is%20Tableau's%20blazingly%20fast,and%20Tableau%20Prep's%20ETL%20transformations.). Hyper is Tableau’s new in-memory data engine technology, designed for fast data ingest and analytical query processing on large or complex data set. The Hyper file will be directly saved into Tableau Server. - [Tableau Server Export Integration](/int/tableau-server-export-integration) - [Tableau Cloud Export Integration](/int/tableau-cloud-export-integration) ## Approach 2 - Use Datatank 2.0 as a datamart Using [Datatank 2.0](https://docs.treasuredata.com/smart/project-product-documentation/about-data-tank-2-0), that provides PostgreSQL interface compatibility, Tableau’s various functionalities and features can assist in efficiently visualizing this data and creating Dashboards. While you can export data to Datatank 2.0 via Activation to PostgreSQL, you will need to integrate Datatank 2.0 with Tableau as well. You must first install PostgreSQL database drivers into your Tableau environment. - [Tableau PostgreSQL Connector](https://help.tableau.com/current/pro/desktop/en-us/examples_postgresql.htm) We recommend Approach 1 or Apporach 2 if you are not familar with Tableau technically because these architectures are a simple and a good performance for visualizing data on Tableau. ## Approach 3 - Use Tableau Server/Cloud to Share the Connections Tableau can publishing data sources to Tableau Cloud or Tableau Server to be integral to maintaining a single source for your data. Publishing also enables sharing data among colleagues; including those who don’t use Tableau Desktop, but have permission to edit workbooks in the web editing environment. Analysts can quickly iterate on the data and reports by having access to all the data sources, so they’re self-reliant. You must make a new connection with Treasure Data Trino/Presto, and save the connection and publish it to your Tableau Server/Cloud. - [Best Practices for Published Data Sources](https://help.tableau.com/current/pro/desktop/en-us/publish_datasources_about.htm) - [Tableau Desktop for Windows Import Integration](/int/tableau-desktop-for-windows-import-integration) This allows you to configure the scheduled refresh extract from Treasure Data directly, so that you don't worry about Treasure Data resource by increasing a number of Tableau users. - [Quick Start: Refresh Extracts on a Schedule](https://help.tableau.com/current/server/en-us/qs_refresh_extracts.htm) ## Links to Child Pages - [Tableau Desktop for Mac](/int/tableau-desktop-for-mac) - [Tableau Desktop for Windows Import Integration](/int/tableau-desktop-for-windows-import-integration) - [Tableau Cloud Export Integration](/int/tableau-cloud-export-integration) - [Tableau Server Export Integration](/int/tableau-server-export-integration)