S3 Storage in Your Own Data Center: Scalable Data Architecture with CEPH

For those building modern data engineering pipelines, S3 (Simple Storage Service) is indispensable. It is the industry standard for accessing unstructured data, model checkpoints, and data lakes. But what if data must remain on-premise for compliance reasons or if the hyperscalers’ egress costs are breaking the budget?

The answer for cloud-native architectures is CEPH. As a highly scalable, software-defined storage system, CEPH enables companies to operate an S3-compatible storage infrastructure on standard hardware within their own data center.

Why “traditional” NAS isn’t enough for Data Engineering

Conventional storage solutions (like classic NFS shares) quickly reach their limits in modern AI and big data scenarios:

1. Scalability: When storage is full, expensive proprietary hardware often needs to be purchased.
2. Protocol Conflicts: Modern tools like Apache Airflow, Spark, or TensorFlow are optimized for object storage (S3), not file system mounts.
3. Single Point of Failure: If the central storage controller fails, the entire pipeline comes to a halt.

CEPH: The Resilient Backbone for Kubernetes

In our projects, we use CEPH as the primary storage backend because it integrates seamlessly with Kubernetes (often via Rook, the cloud-native orchestrator for CEPH).

1. Unified Storage: One for All

CEPH is a “jack of all trades.” It offers:

• Object Storage (RGW): The S3 interface for data lakes and training data.
• Block Storage (RBD): Fast storage for databases like PostgreSQL or ClickHouse.
• Shared File System (CephFS): For scenarios where multiple pods need simultaneous access to the same files (e.g., shared Jupyter workspaces).

2. Horizontal Scalability Without Downtime

Does the data platform need more space? Simply add new servers with standard drives (NVMe, SSD, or HDD) to the cluster. CEPH recognizes the new capacity and automatically redistributes the data in the background (self-healing and self-managing). There’s no more “big forklift upgrade.”

3. Performance Through Layer Separation

In a data platform, we have different requirements. CEPH allows us to define storage tiers:

• Hot Tier: Ultra-fast NVMe pools for active training jobs and analytical databases.
• Cold Tier: Cost-effective HDD pools for long-term archives and backups.

The Strategic Importance: Cloud Flexibility On-Premise

The greatest advantage of CEPH is its API compatibility. Since your applications communicate with CEPH via the S3 interface, your entire pipeline remains portable.

A data engineer writes their code against an S3 URL. Whether this URL points to an on-premise CEPH cluster at your facility or to a cloud storage is irrelevant to the code. This prevents the dreaded vendor lock-in and enables true hybrid cloud scenarios: develop in the cloud, conduct productive training on sensitive data in your own CEPH cluster.

Conclusion: No Scaling Without Solid Storage

Data is the fuel for AI, but storage is the tank. CEPH provides the necessary elasticity and resilience to manage even petabyte ranges without losing control over data sovereignty.

Are your data still in inflexible silos? ayedo supports you in designing and building a modern CEPH infrastructure on Kubernetes – for maximum performance and full sovereignty.

FAQ

What is Rook and what role does it play with CEPH? Rook is an open-source cloud-native storage orchestrator for Kubernetes. It automates the deployment, management, and scaling of CEPH within the cluster and makes storage operations standard Kubernetes objects.

How secure is CEPH against data loss? CEPH uses methods like replication (multiple copies of data) or erasure coding (similar to RAID, but across nodes) to ensure data remains available even if multiple drives or entire server nodes fail.

Can CEPH match the performance of cloud-native storage? Yes. In combination with NVMe drives and fast 25/100-GbE networks, CEPH often achieves higher throughput rates and lower latencies in its own data center than public cloud storage offerings, as the physical distance is shorter.

Is CEPH suitable for small setups? CEPH shows its full strength in medium to large clusters (starting from about 3-5 nodes). For very small setups, the management overhead can be higher than with simple solutions, which is why professional orchestration via Rook/Kubernetes is highly recommended.