We design and build scalable Lakehouse architectures on Databricks — ETL/ELT pipelines, legacy migrations off Oracle, DB2, Cosmos and SQL Server, and real-time pipelines that feed your BI, your data science and your AI agents from one governed source. In-house data and cloud architects who solve the platform and the cloud together. Building since 2013.
Teams spend months on the clever part and lose on the boring part underneath it. Every one of these is a data-layer failure, and every one of them is avoidable.
A spreadsheet dump gets you a prototype. No refresh, no lineage, no scale — and the day the data changes, the whole thing quietly stops being true.
No provenance, no access control, no idea what's actually in it. You can't prove what your model learned from, and in a regulated industry that's the whole ballgame.
Hand-rolled ETL that fails at 2am with no retry, no idempotency, no alert. The dashboard goes stale, the model goes stale, and a customer notices before you do.
The data governed in a warehouse, the AI running somewhere else on a copy nobody's tracking. That gap is where trust, governance and every hard audit question fall through.
Scalable Lakehouse on Databricks — Delta Lake, medallion architecture, structured and unstructured data in one governed place. The warehouse's reliability with the lake's flexibility, without running both.
Batch and incremental pipelines built declaratively on Lakeflow and Spark — clean, conformed, tested data with quality checks and lineage baked in, not bolted on after.
Off Oracle, DB2, Cosmos, SQL Server and on-prem warehouses onto the Lakehouse — incremental and validated, so you cut over with evidence instead of crossed fingers.
Streaming ingestion and change data capture, so the data driving your dashboards and your agents is fresh enough to act on — not yesterday's snapshot.
We build on the medallion pattern — three layers, each one a step up in quality. Data lands raw, gets cleaned and conformed, then curated into business-ready tables. Every layer is Delta Lake underneath: ACID transactions, schema enforcement, time travel. Nothing is overwritten; everything is auditable back to source.
This is the argument that changes the conversation. Most companies build a data platform for BI, then start an entirely separate AI project on a separate copy of the data. That's two budgets, two governance stories and one gap between them. Build the Gold layer once, govern it once, and BI, data science and your AI agents all read from the same trusted source.
Most enterprises don't start clean — they start on Oracle, DB2, SQL Server, Cosmos or an on-prem warehouse that's expensive, slow and in the way. We move you off it incrementally: land the raw data first, rebuild the logic in Lakeflow, run old and new side by side, and cut over only once the numbers reconcile. No rip-and-replace, no six-month blackout, no leap of faith.
Databricks at the core, with the ingestion, orchestration and BI tools around it — and the alternatives we'll use when they fit what you already run. A sample across the layers:
We built governed ETL pipelines feeding live business intelligence for a large automotive supplier — data fresh enough for the business to act on, governed end to end. On the insurance side of the same relationship, we added intelligent document processing over ACORD forms, policy Q&A and inspection reports. One team, the platform and the pipelines, in production — not a pilot.
In-house data architects and cloud architects who solve the platform and the cloud together — natively on AWS, also Azure and GCP. No hand-off, no two vendors pointing at each other.
A Databricks Partner with a dedicated team. The same Lakehouse we build feeds the AI agents we run on AgentBricks — so the data foundation and the AI on top come from one place.
Since 2013, moving legacy estates off Oracle, DB2, SQL Server and on-prem — incremental, parallel-run, validated. You cut over on evidence, not optimism.
Built for Unity Catalog governance and an ISO 27001:2022 practice, so the data your BI and your AI depend on is data you can actually prove.
Our data's scattered across systems and our AI project is stalled on it.
We're on Oracle and SQL Server, we know we need to modernise, and a big-bang migration terrifies us.
Our dashboards are always a day behind the business.
We want to build AI agents but our data isn't ready for them.
We have a data lake and a warehouse and they don't agree with each other.
We need our BI, our data science and our AI to run off the same trusted numbers.
Usually a CTO, VP of Engineering, Head of Data or Data Engineering, a Chief Data Officer, or a data-platform lead — in the US, Europe and APAC.
The governance layer over everything you've just built — Unity Catalog access, lineage and audit, plus Databricks SQL. The control plane on top of the pipelines.
The agents this data is actually for — production, in-platform, reading the governed Gold layer instead of a scraped copy.
The method that governs the whole AI lifecycle — nine phases, EU AI Act, evidence packs. The platform enforces it; this is the method behind it.
Because a Lakehouse gives you the reliability and governance of a warehouse and the flexibility and scale of a lake in one platform — instead of running both and reconciling them. And it's the one place where your data, your BI and your AI agents can live under a single governance model, which is the whole point of this cluster.
No. We land your raw data first, rebuild the logic incrementally, and run old and new in parallel until the numbers reconcile. You cut over piece by piece, on evidence — never a big-bang blackout.
Both are options. We migrate off legacy platforms where that's the goal, but we can also integrate — Delta Sharing and federation let the Lakehouse sit alongside what you have and take over gradually.
Streaming ingestion, change data capture and Auto Loader keep the data fresh enough to drive live dashboards and agents. The same medallion architecture runs in batch or streaming — it's a speed setting, not a rebuild.
Yes — Unity Catalog gives you access control, lineage and audit across every layer, and we work to an ISO 27001:2022 practice. The governance layer is deep enough that it's a page of its own: the Governed Data Platform.
No. The same Lakehouse powers your BI, your data science and your analytics — AI is one consumer of it, not a precondition. Plenty of clients build the data foundation first and add agents later, or never. It stands on its own.
We build and migrate natively on AWS, and also on Azure and GCP. Our data and cloud architects work together, so the platform and the cloud underneath it are designed as one thing.
Start with a data assessment — we'll map your current estate, the migration path off it, and the Lakehouse architecture that feeds your BI, your data science and your agents from one governed source.