Apache Druid vs Apache Spark
Apache Druid
druid.apache.org
Apache Spark
spark.apache.orgSide by side
| Figure | Apache Druid | Apache Spark |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Big Data Analytics Software | 9.0 (#2) | 8.1 (#13) |
| Spec score · Stream Analytics Software | 9.0 (#2) | 8.2 (#7) |
| Spec score · Streaming Analytics Software | 9.0 (#2) | 8.1 (#5) |
| Deployment | Druid can run on commodity hardware in *NIX environments and is designed to run in AWS, GCP, Azure, and other cloud environments. | Spark documents standalone, Hadoop YARN, and Kubernetes deployment options. |
| Integrations | Core extensions support systems and formats including S3, HDFS, Google Cloud Storage, Azure, Kafka, Kinesis, Avro, ORC, Parquet, MySQL, and PostgreSQL. | The listed ecosystem includes pandas, TensorFlow, PyTorch, scikit-learn, Apache Kafka, Kubernetes, Delta Lake, and Apache Iceberg. |
| License | Apache Druid and its documentation are licensed under the Apache License, Version 2.0. | The project website states Apache Spark is licensed under the Apache License, Version 2.0. |
| Operating systems | The quickstart supports Linux, Mac OS X, and other Unix-like operating systems; Windows is not supported. | Spark runs on Windows and UNIX-like systems, including Linux and macOS, where a supported Java version is available. |
| Purpose | Apache Druid is a high-performance real-time analytics database for sub-second queries on streaming and batch data at scale. | Apache Spark is a unified engine for large-scale data analytics. |
| Security | Security features include TLS, authentication, and authorization, but they are disabled by default and the documentation says to configure them for production. | Security features such as authentication are not enabled by default, and the documentation says deployments are not secure by default. |
| Support | The project directs users to its community network for help using Druid. | The project directs users to mailing lists and community resources for help. |
Plans
In their own words
Apache Druid
Apache Druid is a free, open-source analytics database for querying streaming and batch data. It is designed to return OLAP queries in milliseconds, including on high-cardinality datasets with billions to trillions of rows. The project describes applications ranging from hundreds to 100,000 queries per second. Native Apache Kafka and Amazon Kinesis integrations support low-latency ingestion and querying as data arrives. Druid organizes ingested data into columnar, time-indexed, encoded and compressed storage. Queries can use Druid SQL or JSON-over-HTTP, and joins are available during ingestion or at query time. Its web console can load data, manage datasources and tasks, show server status and run queries. Extensions connect to storage, databases and formats including S3, HDFS, Azure, PostgreSQL, Avro, ORC and Parquet. Druid can be self-hosted on Linux, macOS and other Unix-like systems; Windows is not supported. The local quickstart needs at least 6 GiB of RAM and Java 17. Production security controls require configuration.
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.