Apache Kafka vs Apache Pinot

Apache Kafka is an open-source distributed event-streaming platform for data pipelines, streaming analytics, data integration, and mission-critical applications. Applications can publish and subscribe to event streams, retain them durably, and process them either in real time or later. Kafka supports durable, fault-tolerant replication across availability zones, data centers, or geographic regions. Kafka Streams provides stateful operations, aggregations, windowing, joins, event-time processing, and exactly-once processing. Kafka Connect links hundreds of event sources and sinks, including Postgres, JMS, Elasticsearch, and AWS S3. Deployments can use bare metal, virtual machines, or containers, on premises or in the cloud, with self-managed or vendor-managed options. The project describes network-limited throughput, latency as low as 2 ms, and clusters of up to 1,000 brokers. Kafka is self-hosted, free, and open source under Apache License 2.0. A local quickstart requires Java 17 or later.

Apache Pinot is a free, open-source distributed OLAP datastore for real-time analytics and user-facing workloads. It ingests streaming data from Apache Kafka, Apache Pulsar, and AWS Kinesis, plus batch data from Hadoop, Spark, Amazon S3, Azure ADLS, and Google Cloud Storage. Streaming and batch sources can be combined into one queryable table. Pinot provides SQL through a query editor and REST API, with JDBC, Java, Python, and Golang clients for applications. Project materials advertise P90 query latency in the tens of milliseconds on petabyte-scale data and capacity for hundreds of thousands of concurrent queries per second. Logical namespaces provide multitenancy. Security features include HTTP Basic Authentication, ACLs, TLS, mutual TLS, and row-level security filters in version 1.4.0 and later. Superset, Tableau, and Power BI can connect as BI tools. Official installation guidance covers Docker and Kubernetes with Helm. The self-hosted plan is listed at free.