Apache Pinot vs Feldera
Apache Pinot
pinot.apache.orgFeldera
feldera.comSide by side
| Figure | Apache Pinot | Feldera |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Streaming Analytics Software | 8.1 (#4) | 7.8 (#12) |
| Deployment | The official quick install supports Docker and Kubernetes with Helm. | Feldera offers Docker for local development, testing, and prototyping, a free online sandbox, and an enterprise offering for production. |
| Purpose | Apache Pinot is a real-time distributed OLAP datastore for low-latency, high-throughput analytics and user-facing workloads. | Feldera is an incremental view maintenance engine that keeps SQL views in sync with changing data and works alongside a lakehouse or warehouse. |
Plans
In their own words
Apache Pinot
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.
Feldera
Feldera is an incremental view maintenance engine that keeps SQL views current as input data changes, for use alongside a lakehouse or warehouse. Instead of rebuilding a full dataset after each change, it updates views in proportion to the change. It supports SQL window functions and recursive queries. Documented inputs include Kafka, Google Pub/Sub, NATS, PostgreSQL, AWS S3, Delta Lake, and Apache Iceberg; outputs include Kafka, Delta Lake, PostgreSQL, Redis, DynamoDB, Snowflake, and Iceberg, with several output connectors marked experimental. The free open-source edition runs as a single node and container, supports all connectors and datasets larger than memory, and includes community support through Slack. Docker is available for local development, testing, and prototyping, alongside a free online sandbox. Enterprise runs on a customer-provided cloud or Kubernetes cluster and adds multi-node deployments, fault tolerance, resource isolation, and dedicated support. Its documentation says data stays on the customer's premises. Enterprise pricing is custom, and pipeline scale-out is listed as coming soon.