Apache Hadoop HDFS vs Oracle Big Data Service
Apache Hadoop HDFS
hadoop.apache.orgOracle Big Data Service
oracle.comSide by side
| Figure | Apache Hadoop HDFS | Oracle Big Data Service |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | Yes |
| Open source | Yes | — |
| Web | — | Yes |
| Windows | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 2 | 1 |
| Spec score · Data Lake Software | 8.9 (#1) | 7.5 (#10) |
| High availability | NameNode high availability can use shared NFS storage or a distributed Journal edit log, with the Journal approach recommended. | Secure, high-availability clusters are available as an option. |
| Integrations | 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. | Oracle documents integrations with OCI Object Storage and Data Catalog; Cloud SQL also analyzes data across Hadoop, Kafka, NoSQL, and object stores. |
| Security | Production clusters are expected to use Kerberos for caller authentication and access control. | Block Volume data used by Big Data Service is encrypted at rest by default, and OCI IAM controls authentication and authorization. |
Plans
Apache Hadoop HDFS
- Apache Hadoop HDFSFree
- Apache Hadoop / HDFS open-source distributionFree
In their own words
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.
Oracle Big Data Service
Oracle Big Data Service is a managed cloud service for creating Hadoop-based data lakes that process large amounts of data. Its distribution includes Apache Hadoop, Ambari, HBase, Hive, Spark, and other services. Customers can build clusters with OCI compute shapes, from small development environments to large production clusters on dedicated hardware, and choose NVMe or block storage. Clusters can be resized; secure, high-availability clusters are an option. Oracle Cloud SQL queries data across Hadoop, Kafka, NoSQL, and object stores using Oracle SQL. The service also describes SparkR for working with HDFS, Spark DataFrames, and JDBC sources and building models in R. Access is available through the OCI Console, CLI, REST APIs, and SDKs. Block Volume data is encrypted at rest by default, and OCI IAM controls authentication and authorization. Oracle describes both short-lived clusters for specific tasks and long-running data lakes. The listed service fee is 0.02 USD per OCPU per hour; compute and storage charges apply separately. Oracle lists separate hourly rates for compute shapes and Cloud SQL. Clusters need release updates to remain current and supported.