---
# source: src/content/services/en/data-management.md
# route:  /en/services/data-management/
title: Data Management
group: infrastructure
audience: [enterprise, provider]
order: 2
tags: [data-management, storage, architecture]
vendors: [hpe, ibm]
summary: Software-defined storage against data growth. The real gain is that the next data migration does not happen.
photoNeed: "Infrastructure work in progress at a customer site or in our lab: hands on hardware, a rack door open, a screen mid-configuration"
stub: false
draft: false
---

## The problem

Data grows, and classic block storage often lacks the functionality for that kind of
growth. At some point the problem stops being capacity and becomes the next migration onto
the next system.

## The approach

Software-defined storage. We work with Qumulo, Scality RING, Cohesity and IBM Spectrum
Scale, because they have different strengths and the choice should follow the requirements
rather than the other way round.

What these products have in common is why they solve the problem: they run on standard
x86 servers, scale out as clusters rather than up, replicate synchronously or
asynchronously across sites, and speak the protocols that are actually needed, meaning S3,
NFS, CIFS and HDFS. Add to that built-in automation and a hardware lifecycle that does not
need a migration.

Before that choice there is a requirements engineering workshop. Not as a formality:
without clarity on the access patterns, availability requirements and retention periods
that actually apply, every product decision is guesswork.

## What it actually buys

**No more migrations.** Once the data is on an SDS platform, the platform owns the
lifecycle. Changing the hardware underneath becomes an operation rather than a project.

**Availability to match.** Synchronous or asynchronous mirroring into a co-location or into
the public cloud, depending on what is needed.

**Growth is designed in.** Extending onto further clusters is part of the model rather than
the exception.