Tableau and Apache Pinot™

Tableau defines business intelligence as the combination of “business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions.” Business intelligence isn’t just a tool, a technology, or a system. It’s also a methodology, which Tableau goes on to describe as everything “that covers the processes and methods of collecting, storing, and analyzing data from business operations or activities to optimize performance.”

Real-time analytics is a computer science discipline wherein massive amounts of data generated in a relatively short time need to be ingested, stored, and indexed, followed by additional processes that can search, filter, aggregate, and process that stored data against specific queries to produce results.

Tableau allows you to connect to various back-end data sources using a rich set of connectors to pull information into its platform for analysis and visualization, which can then be shared within and across enterprises. It is designed primarily for business analysts and data analysts.

There are any number of sources that can be brought into Tableau such as SQL and NoSQL Online Transactional (OLTP) databases, real-time Online Analytical (OLAP) databases (such as Apache Pinot), data warehouses, data lakes, and even files stored in object stores (such as AWS S3).

Traditionally Tableau worked from batch data analytics sources, which can be delayed by hours or days from the current state of the business. The minimization of delay is known as data “freshness.” Increasingly there is a need to join Tableau’s data visualization capabilities with real-time analytics systems that can produce fast and fresh insights such as Apache Pinot.

Apache Pinot runs on a distributed cluster of services that can efficiently ingest, store, index, and query data structured as tables. Data can be ingested from real-time event streaming services, such as Apache Kafka, and batch data sources such as Change Data Capture (CDC) updates from Online Transaction Processing (OLTP) databases or static files stored in cloud object stores.

Once data is within Apache Pinot, it can be indexed in many ways, including with the highly efficient star-tree index. Data within Apache Pinot is stored in columns, sometimes referred to as a column store database. Column stores are more efficient for analytics since you do not need to read data from columns that are not relevant to a query, and repetitive data found in row-after-row can be efficiently compressed.

You can establish a connection between Apache Pinot and Tableau using a free, open-source connector. This connector utilizes a JDBC Driver to link Tableau with Apache Pinot and a Tableau connector file (TACO file) to translate Tableau queries into Pinot queries. Setting up both components is straightforward. After this setup, launch Tableau Desktop and configure the connection details. Once connected, you can query your data in the Pinot table.

For more information, you can check out our detailed documentation.

StarTree Cloud, powered by Apache Pinot, is a fully managed Database-as-a-Service (DBaaS) for real-time analytics. You can use the Tableau connector to connect directly with your data stored in StarTree Cloud. Sign up for a free trial to get started in minutes.