Why Apache Ozone is the Preferred Object Store for Big Data
Source: Dev.to
The Shift to On‑Premise Object Storage
If your data landscape includes structured, semi‑structured, and unstructured data, and you aim for cost efficiency by avoiding separate silos, all paths lead to object storage. For organizations with requirements to keep data in‑house, on‑premise solutions are a necessity.
While the market offers several options like MinIO or Ceph, if you are utilizing big‑data engines such as Hive, Spark, Trino, or Impala, there is a particularly optimized solution: Apache Ozone.
You can explore the technical architecture of Apache Ozone here.
Key Technical Advantages of Apache Ozone
Source: Cloudera Ozone Overview Documentation
Strong Consistency
Ozone provides strong consistency via the Raft consensus protocol. Data is immediately visible once written, with guaranteed atomic write support. In contrast, S3‑compatible interfaces in other systems may exhibit eventual consistency, leading to potential delays or conflicts during overwrite or list operations.
Native Ecosystem Integration
Built as a core part of the Hadoop ecosystem, Ozone offers seamless, out‑of‑the‑box support for major big‑data processing engines such as Hive, Spark, and Trino. See the detailed Hive Integration Documentation for optimization details.
POSIX Compatibility & File System Behavior
Through its OFS layer, Ozone offers POSIX‑like behavior and a directory hierarchy, enabling native atomic renames that are crucial for the performance and reliability of Hadoop‑based workloads.
Full Kerberos Support
Leveraging native Hadoop compatibility, Ozone integrates fully with Kerberos for enterprise‑grade security—a feature often lacking in S3‑only object stores.
Feature Comparison
| Feature | Apache Ozone | S3 (MinIO, Ceph, etc.) |
|---|---|---|
| Performance | Optimized for large‑scale data lakes | High throughput, limited metadata handling |
| Consistency Model | Strong Consistency (Raft‑based) | Eventual Consistency (possible delays) |
| Hadoop/Spark/Trino Integration | Native & seamless | Limited (especially for Hive/Impala) |
| POSIX / File System | POSIX‑like (native atomic rename) | None (object‑based only) |
| Kerberos Support | Fully compatible (native) | None |
The Perfect Match for Modern Data Lakehouse (Apache Iceberg)
If you are moving toward a Data Lakehouse architecture using Apache Iceberg, Ozone stands out as the superior storage layer.
Atomic Commits
Iceberg relies on atomic metadata updates to prevent data corruption during concurrent writes. Ozone supports this natively through its atomic rename functionality.
Native Locking
Ozone provides the locking mechanisms necessary to prevent metadata inconsistencies, whereas S3‑compatible stores often require external services like Zookeeper to manage locks.
Snapshot Isolation
Ozone’s architecture ensures that data is not considered committed until acknowledged by all replicas, preserving the consistent view required by Iceberg’s immutable file model.
Feature Comparison
| Feature | Apache Ozone | S3‑compatible Stores |
|---|---|---|
| Atomic Commits | Fully supported (via OFS) | No native support (workarounds required) |
| Locking Mechanism | Native support | Requires external tools (Zookeeper, etc.) |
| Snapshot Isolation | Guaranteed (strong consistency) | Very limited / eventual consistency |
| Directory Structure | Native support | Simulated (prefix‑based) |
Conclusion
For organizations aiming to process unstructured and structured data effectively using Spark, Hive, or Trino, Apache Ozone is not just an alternative—it is the most reliable on‑premise object store. It bridges the gap between traditional file systems and modern object storage, making it the ideal choice for high‑performance data lakehouse architectures.