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 like retailer MUJI use Tableau for the BI and Visual Analytics. In this article, we showcase how to combine Treasure Data, Tableau Desktop, and Tableau Server.
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.
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 and Treasure Data’s Reference Architecture
You combine Treasure Data and Tableau because Treasure Data provides a scalable backend to handle new big 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.
Use Treasure Data to Collect Big Data
You start by collecting data into Treasure Data. Treasure Data provides various ways to collect data into the cloud in near-real-time. The data sources depicted here are ‘time-series’ data, which means there is historical data, produced in real time, and growing rapidly as your business scales. The main data collection capabilities provided by Treasure Data are:
Mobile and Gaming SDK (Android, iOS, Unity, Unreal Engine) for mobile and gaming application tracking
TD Agent for streaming data collection
Bulk Loader for parallel bulk loading
Data Connector for pre-built integrations
Treasure Data imports almost 1 Million records per second and Treasure Data customers benefit from such scale. Setting up the data collection typically takes minutes or a couple of hours.
Use Treasure Data to Aggregate Big Data
Now we have raw data in the cloud. To provide a better experience for the BI consumers, it’s a good idea to summarize this raw data into smaller sets for performance reasons. By using one of Treasure Data’s embedded query engines, you can crunch big data into an aggregated format.
Treasure Data supports ‘Tableau Result Output’ so you can directly push the aggregated results into Tableau Server. You don’t need any additional infrastructure to do this. You can even automate this process by using scheduled jobs to periodically aggregate the data.
Treasure Data can push the query results as a ‘Tableau Data Extract‘ (TDE) file. TDE is Tableau’s proprietary columnar file format, optimized for efficient slicing and dicing data (see Why Use Tableau Data Extracts). The TDE file will be directly saved into Tableau Server.
Use Tableau Desktop to Design Workbooks
Now we have raw data access and aggregated data too. It’s time to explore the data using Tableau Desktop. Tableau offers a lot of built-in connectors for existing data sources (EDW, CRM, ERP, Excel, etc), that you can interact with directly.
Treasure Data provides an ODBC driver for Tableau Desktop so that data analysts can have raw data access.
Analysts can choose any of the above methods depending on their needs. You can also join across these data sources. For example, you can create a join between Salesforce.com data and a TDE file, or even join multiple TDE files. When the workbook is created, Tableau Desktop can publish it to Tableau Server.
Use Tableau Server to Share the Workbooks
Analysts can publish workbooks to the server and the consumers can view these from their browsers. Analysts can quickly iterate on the data and reports by having access to all the data sources, so they’re self-reliant.