Data Lakehouse and Warehouse Build
One governed place where your structured tables and your unstructured content both live, modelled well enough that two teams asking the same question get the same answer.
A data lake becomes a swamp for one reason: files were landed with no model, no ownership and no contract, on the assumption that meaning could be added later. Meaning is never added later. The lakehouse pattern exists to keep the flexibility of cheap object storage while restoring the table semantics that make data usable.
A data lakehouse combines the low-cost, open-format storage of a data lake with the transactions, schema enforcement and performance of a warehouse, usually through an open table format over object storage. A data warehouse is a managed, structured analytical database. The lakehouse pattern is generally preferred where unstructured content and machine learning workloads sit alongside classic reporting.
Choosing the shape honestly
| Your situation | Likely right answer | Why |
|---|---|---|
| Structured sources, BI and reporting only | Warehouse | Simplest to operate; the lakehouse flexibility earns nothing |
| Documents, images and audio alongside tables | Lakehouse | One governed store for both; no second copy to reconcile |
| Heavy machine learning and feature work | Lakehouse | Open formats read directly by training code without export |
| Very large volumes, cost under pressure | Lakehouse | Object storage is materially cheaper per terabyte |
| Small team, limited platform skills | Warehouse | Lower operational burden matters more than architectural purity |
| Existing warehouse working well | Extend, do not migrate | Migration cost frequently exceeds the benefit |
That last row is a real recommendation we make and occasionally lose work over. A warehouse that meets the requirement is not a problem to be solved.
What we build, layer by layer
Raw layer, immutable
Source data landed unchanged, partitioned and retained, so any downstream table can be rebuilt from first principles. This is what makes a business-logic correction a rerun instead of an incident.
Modelled core, the layer that matters
Conformed, tested tables representing the entities your business actually talks about: customer, order, product, contract, claim. This is where a lakehouse either succeeds or degenerates into a file dump, and it is where we spend the most time with your domain experts.
Consumption layer, shaped per audience
Aggregates and marts shaped for BI tools, application access and model training. Different consumers get different shapes, all derived from the same core so that the numbers reconcile by construction rather than by coincidence.
Unstructured content, governed alongside
Documents, images and transcripts stored with metadata, lineage and the same access controls as the tables, so that semantic search and retrieval-augmented generation inherit governance instead of quietly bypassing it.
Access control that reflects the organisation
Role-based access, row and column level security where it is needed, and classification of sensitive fields. Configured at the platform rather than in each consuming tool, which is the only version that survives a new BI tool arriving.
Cost controls before the first invoice
Partitioning, file sizing, compaction, storage tiering and query patterns are designed rather than discovered. Lakehouse cost problems are almost always small-file problems and full-scan problems, and both are far cheaper to prevent than to unwind.
The core model is the deliverable people underestimate
Platforms can be migrated in weeks. A conformed core model that your finance, operations and data science teams all accept takes months of conversation and is what actually stops two teams reporting different revenue figures. We treat it as the centre of the engagement rather than as a schema exercise.
Making it ready for AI, not just reporting
Analytics and AI make different demands, and a platform designed only for dashboards frustrates model work in specific, predictable ways:
- Point-in-time correctness. Training data must reflect what was known at the time, not what is known now. Without it, models look excellent in evaluation and fail in production.
- Direct read access. Training and feature code reads open formats directly rather than through an export step that becomes stale the day it is written.
- Unstructured content in the same governance. So a retrieval system cannot serve a document a user is not entitled to see.
- Lineage good enough to explain a model. Which sources, which transformations, which version, for the audit that eventually arrives.
Where a feature store or a vector database is warranted, the lakehouse is the substrate both sit on rather than a separate island.
How the engagement runs
The core model is agreed with your domain experts before the platform is built out.
Requirements and platform choice
Workloads, volumes, governance needs and skills assessed; warehouse or lakehouse decided and priced.
Core model design
Entities, grain, conformance and ownership agreed with domain experts and written down.
Build
Raw, core and consumption layers built with tests; access control and unstructured storage configured.
Load and reconcile
Historic load, reconciliation against source systems, performance and cost tuning.
Handover
Documentation, runbook and working sessions with your data team.
What you receive
A governed platform with a core model your organisation agrees on, and the cost behaviour understood.
Deployed platform
Raw, core and consumption layers with transactions, schema enforcement and retention configured.
Core data model
Conformed entities with grain, ownership and definitions your domain experts have signed off.
Access control configuration
Roles, sensitive-field classification and row or column security as required.
Reconciliation report
Loaded data reconciled against source systems, with any discrepancies explained.
Cost model and controls
Storage and compute projections, partitioning strategy and the levers that change spend.
Documentation and runbook
Model documentation, lineage, operational procedures and onboarding for new engineers.
Is this the right engagement?
Worth being direct. Data Lakehouse and Warehouse Build is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- Reporting is assembled from several systems and the numbers disagree.
- Unstructured content needs to sit under the same governance as your tables.
- Machine learning work is blocked on access to consistent historic data.
- Storage or query costs are rising without a clear cause.
- A core set of business entities needs a single agreed definition.
Choose something else if
- The target architecture is not yet decided. Start with data strategy and architecture.
- One source system and a BI tool genuinely meet the need.
- The existing warehouse works and the case for migration is architectural preference.
- Nobody can commit domain experts to agree the core model, which is the part that cannot be outsourced.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What is the difference between a data lakehouse and a data warehouse?
A warehouse is a managed structured analytical database with strong performance and simple operations. A lakehouse puts warehouse-like transactions, schema enforcement and performance on top of cheap open-format object storage, so unstructured content and machine learning workloads live in the same governed place as your tables. Lakehouse costs less per terabyte and demands more platform skill.
Do we need a lakehouse if we already have a warehouse?
Often not. If your workloads are structured reporting and the warehouse performs and costs acceptably, extending it is usually better value than migrating. The honest triggers for a lakehouse are unstructured content, serious machine learning, or storage volumes where the per-terabyte difference is material.
How long does a lakehouse build take?
Typically eight to sixteen weeks for a first production platform with a modelled core covering the initial domains, then incremental as further domains are added. The variable is rarely the technology; it is how quickly your domain experts can agree what a customer or an order actually is.
How do you keep cloud costs under control?
By designing partitioning, file sizing and compaction up front, tiering cold storage, and monitoring cost per query and per pipeline from the first week. Runaway lakehouse bills are nearly always small files and full scans, both of which are cheap to prevent and expensive to retrofit across a mature estate.
Can this support retrieval-augmented generation?
Yes, and it is one of the better reasons to choose the lakehouse pattern. Documents are stored with metadata and lineage under the same access control as your tables, so a RAG system built on top inherits permissions rather than quietly bypassing them, which is the most common governance failure in production RAG.
Often paired with this
Most clients combine two or three engagements from the Data Engineering and AI Readiness pillar. These are the ones that most often run immediately before or after.
Data Strategy and Architecture
A target data architecture and a sequenced roadmap, derived from the decisions you need to make rather than from a vendor shortlist.
Read more →Data Pipeline Development
ETL and ELT pipelines that survive schema drift, late data and reruns, with tests and monitoring from day one.
Read more →Vector Database Design and Implementation
Vector stores designed around chunking, filtering and reindexing, tuned against measured retrieval quality.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.