A Data Team’s Guide to Real-Time Analytics for Apache Kafka

In 2025, Apache Kafka has cemented its place as the leading platform for streaming data, with over 50,000 companies and 80% of the Fortune 500 relying on it to process real-time event streams. Streaming data has become the backbone of modern analytics, powering everything from fraud detection and personalization to real-time monitoring and AI-driven automation.

However, while Kafka has revolutionized data movement, the challenge has always been how to analyze that data efficiently.

Initially, stateless stream processing tools like ksqlDB and Apache Flink emerged to perform real-time transformations on Kafka data. These tools are great at filtering, aggregating, and enriching data in motion—but they lack access to historical context. Without persistent storage, they can’t support complex analytical queries, historical comparisons, or deep trend analysis at scale.

To fill this gap, organizations turned to stateful analytical databases to combine real-time and historical data for deeper insights. However, most traditional solutions—such as Snowflake, Redshift, and BigQuery—were built for batch or micro-batch ingestion. While powerful, they introduce latency, making them unsuitable for truly real-time analytics on Kafka streams.

The next evolution is real-time, stateful analytics that is purpose-built for streaming data—offering instant queryability, high concurrency, and true real-time performance to complement Kafka’s unmatched streaming capabilities.

Enter Apache Pinot: A Stateful, Real-Time Analytics Database

Apache Pinot represents the next generation of real-time, stateful analytics, purpose-built to ingest and query streaming data with sub-second latency. Unlike batch-oriented databases, Pinot:

• Ingests data as a continuous stream (from Kafka, Pulsar, Red Panda, Kinesis, and others) and makes it instantly queryable—eliminating batch delays.
• Combines real-time and historical data to power complex analytical queries that require both fresh and contextualized insights.
• Scales to millions of queries per second, enabling many use cases on a single instance, thereby delivering a full platform.

Most analytical databases ingest data in micro-batches, creating unavoidable delays between data arrival and availability for query. Apache Pinot takes a fundamentally different approach—streaming data directly into the database without batch windows, ensuring that fresh data is instantly available for analysis.

Unlike traditional architectures that index data after ingestion, Pinot indexes data during ingest, eliminating any period where new records are unsearchable. This means that Kafka events flowing into Pinot are queryable in milliseconds, even at massive scale.

Pinot also supports hybrid storage, merging real-time streams with deep historical context. This allows organizations to analyze the latest Kafka events alongside years of historical data—essential for detecting trends, running anomaly detection, or powering AI-driven analytics.

Perhaps most critically, Pinot is designed for extreme concurrency. While traditional databases struggle under high query loads, Pinot delivers sub-second SQL queries at 30,000+ QPS, ensuring instant insights for millions of users. Whether powering real-time dashboards, AI-driven automation, or customer-facing analytics, Pinot transforms Kafka streams into actionable intelligence.

Real-Time Analytics as Part of a Streaming Platform

With the emergence of Pinot, real-time analytics is no longer just about processing streams but also analyzing them at scale. In a modern streaming analytics platform, the components work together:

• Apache Kafka captures high-velocity event data.
• Apache Pinot makes both real-time and historical data available instantly for analytics.
• Dashboards, AI models, and applications leverage Pinot’s sub-second queries to drive real-time decisions.

This architecture allows businesses to move beyond simple streaming transformations and power advanced real-time intelligence at scale.

Why Pinot Matters

Organizations that rely on batch-oriented data warehouses for real-time analytics are stuck with unnecessary delays and high costs. Pinot changes the game by bringing together the speed of stream processing and the power of stateful analytics—allowing businesses to:

• Deliver personalized user experiences based on the freshest data.
• Detects anomalies and fraud in real time.
• Power operational intelligence dashboards and observability stacks with instant updates.
• Enable AI-driven automation that reacts to events as they happen.

The Future of Real-Time Data

The evolution of streaming analytics has led to a new paradigm—one where stateless transformations alone are not enough, and traditional stateful databases are too slow. Apache Pinot bridges the gap, providing a real-time, scalable, and cost-efficient solution for analyzing event-driven data.

As more organizations move toward event-driven architectures, the ability to not just process but analyze data in real time will define the next generation of data platforms. Apache Pinot is leading this transformation—powering the world’s most demanding real-time analytics workloads.