Ageing systems brought forward without a rewrite freeze.
Legacy modernisation spans more than code — it's often an end-of-life data store nobody wants to touch (a Cassandra-to-PostgreSQL migration, for instance) sitting underneath a monolith nobody wants to touch either. We handle both: application-level strangler-fig decomposition and the underlying data store migration, sequenced so the business doesn't stop.
What this looks like in practice
An inventory of both layers that usually need modernising together — the application (an ageing Java monolith) and the data store underneath it (often an end-of-life or unsupported database).
Strangler-fig decomposition at the application layer, so the monolith keeps serving traffic while capabilities peel off, route by route.
Data store migration — including cross-technology moves like Cassandra to PostgreSQL — engineered as idempotent, resumable Apache Spark pipelines with row-level reconciliation.
Sequencing that respects your actual risk tolerance: which routes and datasets move first is a business decision as much as a technical one, and we plan it with you.
A rollback plan at every phase, so modernising never means betting the business on one big irreversible cutover.
Zero data-loss, at scale
Large-scale data migration with Apache Spark — 80% faster migration, zero data-loss incidents, on a repeatable, resumable pipeline. Read the case study →
Legacy modernisation — frequently asked questions
How is this different from your Legacy Java Modernisation service page?
What does a Cassandra-to-PostgreSQL migration involve?
Do we have to freeze feature work during modernisation?
Talk to an architect about your legacy system.
Tell us what's ageing and what's actually at risk — we'll map the sequencing.