Why Apache Ozone is the Preferred Object Store for Big Data

Tayfun Yalcinkaya — Mon, 05 Jan 2026 21:42:00 +0000

The limitations of traditional HDFS architecture when facing billions of small files, combined with the search for S3-like flexibility in on-premise environments, drive us toward a modern solution: Apache Ozone.

From a technology perspective, the abundance of products and methods available for data storage requires serious expertise to navigate. If you need to store a wide variety of data, standard RDBMS technologies will eventually fall short. You need to turn to independent, cost-effective, yet efficient storage technologies that allow you to query data performantly regardless of its type.

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 an object storage architecture, implemented through an on-premise object store. For organizations with requirements to keep data in-house, on-premise solutions are a necessity.

Unlike traditional object storage systems that prioritize API compatibility, Apache Ozone is designed as a storage system optimized for analytical engines rather than object semantics alone.

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 is designed to provide strong consistency via the Raft consensus protocol. This ensures that 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:
Unlike basic S3-compatible stores that offer limited integration with tools like Hive and Impala, Ozone is built as a core part of the Hadoop ecosystem. This results in seamless, out-of-the-box support for major big data processing engines Hive, Spark, and Trino.For instance, you can check the detailed Hive Integration Documentation to see the level of optimization.
POSIX Compatibility & File System Behavior:
Through its OFS layer, Ozone offers POSIX-like behavior and a directory hierarchy. This allows for native atomic renames, which are crucial for the performance and reliability of Hadoop-based workloads.
Full Kerberos Support:
Leveraging its native Hadoop compatibility, Ozone offers full integration with Kerberos for enterprise-grade security , a feature often lacking in S3-only object stores.

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	Native & Seamless Integration	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:
It supports 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 that Iceberg’s immutable file model requires.

Feature	Apache Ozone	S3-compatible Object 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 a purpose-built on-premise object store for big data workloads. It bridges the gap between traditional file systems and modern object storage, making it the ideal choice for high-performance data lakehouse architectures.

What is your preferred storage layer for on-premise big data projects? How could Ozone’s advantages resolve bottlenecks in your current architecture?

Written by Tayfun Yalçınkaya, working on large-scale Big Data platforms and Lakehouse architectures.
Connect with me on LinkedIn

DEV Community: Tayfun Yalcinkaya

Why Apache Ozone is the Preferred Object Store for Big Data