Improving ANSI SQL Compliance - to that end, we’ve added better NULL handling, window functions, and as stated above, the capability for native JOINs.Handling Semi-Structured/Unstructured Data - Pinot can easily index JSON and text data types at scale.

Pluggable architecture - a broad user base requires the ability to extend the database with new customizable index types, routing strategies and storage options.

This we will discuss in more detail below.

Query-time Native JOINs - it was important to get this right, so that they were performant and scalable, allowing high QPS.

Upserts - data-in-motion tends to stay in motion, and one of the cornerstone capabilities of Apache Pinot is upsert support to handle upsert mutations in real-time.

Let’s look at how much innovation has gone into Apache Pinot over the years: Apache Pinot in 2023 is continuously evolving to address emerging needs in the real-time analytics community.

Back then it was developed at a single company with a single use case in mind: to power “who viewed my profile?” Over the ensuing decade the Apache Pinot community expanded to be embraced by many other organizations, and those organizations have expanded its capabilities to address new use cases.

By: Hubert Dulay, Mayank Shrivastava, Neha Pawar What Makes a “1.0 Release?” #Īpache Pinot has continuously evolved since the project’s inception within LinkedIn in 2013.

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