Legacy ETL & pipeline modernisation
Legacy data pipelines are costing you more than you think - in money, in time, and in missed opportunity.
Data engineers in most enterprises spend around half their time maintaining existing pipelines rather than building anything new. That is not a skills problem, it is an architecture problem. Fragmented integration tools, brittle batch processes, and bespoke scripts that nobody wants to touch add up to an estate that is expensive to run, difficult to govern, and fundamentally incompatible with what real-time data and AI require.
Dot Group has been modernising data integration environments for nearly three decades. We know where the risk lies in a migration, how to sequence a programme that keeps the business running while the new architecture takes shape, and how to make a team who have built their working lives around one platform feel confident about the next one.
From batch to continuous data flow
Batch processing was designed for a world where data moved slowly and decisions could wait. That world has largely gone. Regulatory deadlines, fraud detection, personalisation, inventory management – the use cases that matter most today require data that moves continuously, not data that arrives overnight.
The shift from batch to event-driven, real-time pipelines is not just a technical upgrade, it is the foundation for everything else an organisation wants to do with its data. We assess your current pipeline landscape, identify where batch is still appropriate and where it is a bottleneck, and deliver the architecture to support both without forcing a wholesale change that disrupts everything at once.
One platform, every integration style
The average enterprise runs four to six separate data integration tools – one for batch, another for streaming, a third for replication, often several more inherited from acquisitions or technology decisions made a decade ago. Each requires its own skills, its own maintenance, its own governance. The cumulative cost of that fragmentation is rarely visible in a single budget line, but it is consistently significant.
IBM DataStage, IBM watsonx.data integration, Confluent and IBM StreamSets bring batch, micro-batch, and real-time streaming together in unified platforms with consistent pipeline creation, monitoring, and governance across every integration style. Consolidating onto modern tooling reduces licensing cost, simplifies operations, and creates an integration estate that is genuinely manageable at scale.
Built to support real-time and AI
Modern AI workloads do not run well on stale data. Models trained on yesterday’s information make yesterday’s decisions and the gap between what AI can theoretically do and what it delivers in practice is often traceable directly to the quality and freshness of the data feeding it.
IBM StreamSets and Confluent are built for this requirement: continuous data ingestion, intelligent drift detection that adapts automatically when source systems change, and seamless integration with the analytical and AI platforms where data needs to land. Getting the pipeline layer right is not glamorous, but it is the reason AI initiatives either work in production or do not.
The Dot Group Advantage
Dot Group is a leading European partner for IBM StreamSets and IBM DataStage, with a dedicated team of data integration specialists who have delivered pipeline modernisation programmes across financial services, retail, logistics, and media. We do not just implement the technology — we work through the organisational side of migration too: the teams who built the legacy systems, the processes that depend on them, and the knowledge transfer that makes modernisation stick.
Our client relationships last. Compass, Brake, Greencore – organisations that came to us with a data challenge and stayed because of the way we work, not just what we delivered.
Your pipeline estate is the foundation of everything else. Make sure it is solid.
Talk to us about where your data integration environment is holding you back and what a modernisation programme would realistically deliver.