Broadberry WEKA Storage

How WEKA Works

WEKA distributes file data and metadata across storage backends, allowing many clients to access shared files without relying on a single intermediary server.

WEKA Architecture Services

Before recommending a WEKA architecture, Broadberry evaluates your active dataset, concurrency, client resources, performance targets and retention requirements.

Why Choose WEKA?

Match WEKA Storage to the Workload

WEKA is best suited to applications where file I/O is a measured performance constraint. Broadberry sizes the platform around the actual mixed workload rather than a single synthetic bandwidth number.

Active Performance, Retained Capacity

Engineer the Complete Data Path

WEKA Storage Use Cases

WEKA is well suited to performance-sensitive shared workloads that combine metadata activity, concurrency and large data transfers.

Is WEKA Right for Your Environment?

Why Broadberry for WEKA?

Broadberry combines WEKA software expertise with workload-led hardware design to deliver a qualified storage platform built around the way your applications actually use data.

Workload-Led Design

Architecture based on active datasets, concurrency and performance requirements.

Qualified Hardware

NVMe, CPU, memory and networking selected around WEKA support requirements.

Complete System Engineering

Storage, client resources, networking and lifecycle policies designed together.

Deployment and Lifecycle Support

Qualification, validation, expansion and ongoing operational support.

Frequently Asked Questions About WEKA Storage

Which workloads benefit most from WEKA?

WEKA is best suited to shared applications with demanding latency, metadata or throughput requirements, including AI, engineering, media, scientific processing and analytics.

Is an external object store required?

No. A WEKA configuration can keep all data on flash. Object storage is optional and can be used for additional capacity or recovery.

Does tiering change the application's file path?

No. Applications continue to use the same logical file path while WEKA manages data placement. Reads from data held only in the object tier may have additional latency.

Is WEKA suitable for AI and GPU clusters?

Yes. WEKA can provide shared NVMe storage for concurrent training reads, metadata-heavy pipelines and checkpoint workloads. The full application pipeline should be validated before scaling.

Does GPUDirect Storage make every AI application faster?

No. GPUDirect Storage can reduce CPU-memory copies on supported paths, but the application must use an enabled configuration. Other bottlenecks can limit performance, so the complete pipeline should be tested.

Can WEKA run alongside application compute?

Yes. WEKA supports converged and dedicated deployment models. Resource budgets for CPU, memory, devices and networking must be reserved appropriately when roles are combined.

Does distributed protection replace backup?

No. Local snapshots and fault-tolerant storage protect against different risks than independent backup and disaster recovery.

Will tiered data always respond at NVMe speed?

No. Data resident on flash uses the SSD path, while reads of object-only data require retrieval from the capacity tier.

Plan the Right WEKA Storage Platform

Tell us about your applications, active dataset, concurrency, performance targets and retention requirements. Broadberry will design a WEKA hardware platform around the flash capacity, client resources, networking and lifecycle model your environment needs.