Apache Pinot vs Apache Spark
Apache Pinot
pinot.apache.org
Apache Spark
spark.apache.orgSide by side
| Figure | Apache Pinot | Apache Spark |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Streaming Analytics Software | 8.1 (#4) | 8.1 (#5) |
| Spec score · Stream Analytics Software | 8.2 (#6) | 8.2 (#7) |
| Spec score · Big Data Analytics Software | 8.1 (#12) | 8.1 (#13) |
| Deployment | The official quick install supports Docker and Kubernetes with Helm. | Spark documents standalone, Hadoop YARN, and Kubernetes deployment options. |
| Founded | 2013 | 2009 |
| Purpose | Apache Pinot is a real-time distributed OLAP datastore for low-latency, high-throughput analytics and user-facing workloads. | Apache Spark is a unified engine for large-scale data analytics. |
| Support | The project provides Slack, mailing lists, GitHub Issues, and community meetups for questions and discussion. | The project directs users to mailing lists and community resources for help. |
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.
Apache Spark
Apache Spark is a free, open-source engine for large-scale data analytics. It processes batches and real-time streams using Python, SQL, Scala, Java, or R. Spark SQL runs distributed ANSI SQL queries on structured tables and unstructured data such as JSON or images; MLlib provides machine-learning algorithms for local use or cluster scale. The project also describes exploratory analysis on petabyte-scale data without downsampling. Listed ecosystem components include pandas, TensorFlow, PyTorch, scikit-learn, Apache Kafka, Kubernetes, Delta Lake, and Apache Iceberg. Deployment options include standalone, Hadoop YARN, and Kubernetes. Spark runs on Windows and UNIX-like systems, including Linux and macOS, when a supported Java version is available. The 4.2.0 documentation lists Java 17, 21, or 25, Scala 2.13, Python 3.10+, and R 4.0+ (deprecated). Spark Connect separates client applications from clusters for remote access. Security is not enabled by default; Spark supports TLS for RPC connections and encryption of temporary local-disk data. The project is licensed under Apache License, Version 2.0.