Unify transactional data, analytical pipelines, and real-time dashboards with a modern lakehouse architecture. Query latency, concurrency, and cost targets are established against your actual data volume and access patterns during discovery — not assumed from a generic “big data” reference number.
Who this is for
Teams juggling separate, poorly-integrated systems for transactional storage, analytics, and real-time dashboards, or who’ve outgrown a single warehouse but aren’t sure whether a lakehouse, a traditional warehouse, or a low-latency serving layer (or some combination) is the right next step.
Expected outcomes
A clear decision about which workloads belong in a lakehouse versus a warehouse versus a low-latency serving system — not every named platform in our stack applied as if they’re interchangeable — plus the ingestion, governance, and query layers to support it and a migration path validated against your real data before cutover.
Scope & deliverables
Depending on where your workload actually sits, this may mean Spark/Delta Lake pipelines, Snowflake optimization, Trino federated queries across existing sources, or ClickHouse for low-latency OLAP — selected and combined based on your access patterns, not applied as a uniform stack. We also cover data quality checks, access/governance, catalog and lineage, and ingestion/orchestration where those are in scope.
Our approach
We start by profiling your actual data volume, query patterns, freshness requirements, and current pain points, propose an architecture (which may be narrower than “everything”), and validate it against a representative subset of your real data and queries before a full migration.
Client responsibilities & exclusions
You’ll need to provide representative data and query samples for us to validate against — performance and cost figures we propose are tied to that validation, not a generic benchmark. Ongoing operation of the resulting platform can be retained by your team (with documentation and handover) or scoped separately as continued support.
Related: Real-Time Data Streaming