Apache Druid vs Apache Hadoop HDFS
Apache Druid
druid.apache.org
Apache Hadoop HDFS
hadoop.apache.orgSide by side
| Figure | Apache Druid | Apache Hadoop HDFS |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Big Data Analytics Software | 9.0 (#2) | 8.8 (#6) |
| Architecture | Loosely coupled ingestion, query, and orchestration components with deep storage support scale-up and scale-out. | A cluster uses a NameNode for namespace and access coordination and DataNodes for block storage and serving reads and writes. |
| Integrations | Core extensions support systems and formats including S3, HDFS, Google Cloud Storage, Azure, Kafka, Kinesis, Avro, ORC, Parquet, MySQL, and PostgreSQL. | Hadoop 3.5.0 includes a FileSystem implementation for Google Cloud Storage, and its documentation also lists connectors for Amazon S3, Azure Blob Storage, and Azure Data Lake Storage. |
| License | Apache Druid and its documentation are licensed under the Apache License, Version 2.0. | Apache packages are generally distributed under the OSI-approved Apache License 2.0 unless a package states otherwise. |
| Purpose | Apache Druid is a high-performance real-time analytics database for sub-second queries on streaming and batch data at scale. | HDFS is a distributed file system designed for high-throughput access to large application data sets on commodity hardware. |
| Security | Security features include TLS, authentication, and authorization, but they are disabled by default and the documentation says to configure them for production. | Production clusters are expected to use Kerberos for caller authentication and access control. |
| Support | The project directs users to its community network for help using Druid. | The project asks users not to request private support from its non-paid volunteers and directs help requests to module mailing lists. |
Plans
Apache Hadoop HDFS
- Apache Hadoop HDFSFree
- Apache Hadoop / HDFS open-source distributionFree
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 Hadoop HDFS
Apache Hadoop HDFS is a free, open-source distributed file system for high-throughput access to large application data sets on commodity hardware. It is designed for clusters of hundreds of nodes and can support tens of millions of files in one instance. HDFS copies file blocks across machines and detects failed DataNodes so it can start creating replacement copies. Its design prioritizes batch processing and streaming throughput rather than low-latency interactive workloads. Files follow a write-once, read-many model: appends and truncates are supported, but arbitrary in-place changes are not. Applications can connect through Java, a C wrapper, REST, a browser interface or an NFS gateway. The documentation lists connectors for Google Cloud Storage, Amazon S3, Azure Blob Storage and Azure Data Lake Storage. HDFS supports transparent encryption. Apache warns that without Kerberos caller authentication, anyone with network access to cluster servers can access data and run code, and recommends securing clusters before production. Apache also recommends checking release downloads with GPG signatures or SHA-512 checksums. The listed stable release is Hadoop 3.5.0, dated April 2, 2026.