Data Engineering

We run the legacy and new pipelines in parallel — typically for two to four weeks per wave — comparing row counts, aggregated totals, and key metric values at each layer. Discrepancies are tracked in a reconciliation report until they fall within an agreed tolerance. We do not cut over to the new system until the parallel-run sign-off is completed with your data owners.

Where source-system schemas are poorly documented, we capture them empirically using automated profiling before any transformation work begins, so the new models reflect the data as it actually behaves today.

Streaming is justified when the business action it enables cannot wait for the next batch window — fraud detection, live inventory, personalisation at click time. For most analytical use cases, a micro-batch pipeline delivering data every five to fifteen minutes gives the same business outcome at significantly lower operational complexity.

Our default recommendation is to start with the simplest architecture that meets the latency SLO, then introduce streaming components only where batch genuinely cannot satisfy the requirement. This avoids the operational overhead of Kafka clusters for dashboards that are refreshed once an hour.

Yes — and in most cases that is the right starting point. Platform replacement carries significant risk and disruption. We typically begin with a health assessment of the existing warehouse: query performance patterns, resource utilisation, unused tables, and schema debt. Many organisations find that better dbt modelling, clustering and partitioning optimisation, and workload governance resolves the underlying problem without a platform change.

Where a migration is genuinely warranted, we plan it as a series of incremental waves rather than a big-bang cutover, preserving access for downstream BI tools throughout.

We start with a data inventory and sensitivity classification — understanding what data exists, where it lives, and who accesses it today. From there, we define ownership (which team is responsible for each domain), build the business glossary, and implement access policies in the warehouse layer rather than relying on downstream BI tools for security.

Governance does not need to be a multi-year programme. A pragmatic first phase — covering the ten to twenty most critical datasets — can be delivered in six to eight weeks and creates a foundation that the organisation can extend incrementally as data literacy matures.