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.
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