Apache Druid
7.2#2 in streaming analytics softwareFree planFree planWebAPILinuxmacOS
VisitApache Druid vs Apache Spark
Apache Spark
6.4#11 in streaming analytics softwareFree planFree planAPILinuxmacOSSelf-hosted
VisitWhat both publish
| Fact | Apache Druid | Apache Spark |
|---|---|---|
| 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. |
Apache Druid plans
- Apache DruidFree
Apache Spark plans
- Apache SparkFree
Pick Apache Druid if…
Druid suits teams building user-facing analytics or working with streaming data that need low-latency, concurrent queries and quick visibility into incoming data. It can also serve ad hoc analysis of semi-structured data such as JSON.
Pick Apache Spark if…
Spark suits teams working with large-scale batch or streaming analytics, SQL, or machine-learning workloads. It can fit environments using the listed languages, platforms, and deployment options.