Apache Pinot vs Databricks Lakehouse Federation

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.

Databricks Lakehouse Federation has no write-up on Specifiction yet; its figures are above.