Data & Analytics
Data Warehouse Development Services
We build the central, analytics-optimised store that becomes your single source of truth, modelled properly, so every report on top of it is fast, consistent and trusted.
What Data Warehouse Development means in practice
Who it’s for: Organisations whose data has outgrown scattered spreadsheets and operational databases, whose reports have become slow or contradictory, and who want a properly modelled single source of truth that every analysis and dashboard can rely on.
A data warehouse is the central store your reporting and analytics sit on: an analytics-optimised database, kept separate from the systems that run the business day to day, where data from every source is brought together, cleaned into a consistent shape, and modelled so that questions are quick to answer and everyone gets the same number for the same metric. It is the foundation the whole analytics stack rests on. The pipelines that feed it are one thing, the dashboards and models that read from it are another, but the warehouse in the middle is where “what did we sell, to whom, where, and how does that compare to last quarter” becomes a query that takes seconds and returns an answer nobody argues with.
The reason this work deserves real thought is that the data model is load-bearing in a way that is easy to underestimate. A badly modelled warehouse does not announce itself; it just makes every downstream report a little painful: the join that is slightly wrong, the metric that means two different things in two dashboards, the query that scans the whole table because the grain was never decided. Those small frictions compound until nobody trusts the numbers and every analysis starts with an argument about definitions. So the money in this service is not in spinning up a Snowflake account (that is the easy part), it is in the dimensional model underneath: the facts, the dimensions, the grain, and the way history is kept, decided deliberately before a hundred reports are built on top of them.
We also right-size, honestly. Not every company needs a warehouse. A small business with data in one well-structured operational database and a handful of reports is often better served by querying a read replica than by standing up a warehouse and a modern data stack it will spend more time maintaining than using. We build warehouses (and lakehouses), where the data really is scattered, where analytics has outgrown the operational database, or where reporting has become slow and contradictory. Where it has not, we will tell you so, and point you at the cheaper thing that fits.
What you get
- A dimensional model designed for your business, fact and dimension tables, the grain of each fact decided explicitly, and star or snowflake schemas chosen on the trade-off rather than by habit
- The warehouse platform selected and built on the right engine for you (Snowflake, BigQuery, Redshift or a Databricks lakehouse), with the warehouse-versus-lake-versus-lakehouse decision argued from your workload, not from fashion
- Slowly-changing-dimension handling so history is kept the way the business actually needs it. The difference between “what is this customer’s region now” and “which region were they in when they placed that order”
- Transformation built as version-controlled, tested code (typically dbt), turning raw ingested data into the clean, modelled tables analysts and dashboards read from, with the logic reviewable rather than buried in a BI tool
- A semantic or metrics layer so a metric is defined once, centrally, and every report inherits the same definition, no more “revenue” meaning three different things in three teams’ dashboards
- Access control and PII handling appropriate to a central store, role-based access, sensitive columns identified and protected, and the data minimisation that holding everything in one place demands
- Documentation, lineage and a handover so the warehouse is something your own analysts and engineers can extend and trust, not a black box only its builder understands
What Data Warehouse Development does for you
A foundation the whole analytics stack can rely on
Everything downstream, dashboards, self-serve analytics, the metrics in a board pack, the features a data science team builds, reads from the warehouse. Model it well and all of that becomes easy; model it badly and every report inherits the friction. We front-load the dimensional modelling precisely because it is the layer that either quietly saves years of pain or quietly creates it, and because it is far cheaper to get the facts, dimensions and grain right before a hundred reports are built on top than to unpick them afterwards.
Consistent numbers, defined once
The most corrosive problem in analytics is not slow queries. It is metrics that mean different things in different places, so nobody fully trusts any of them. A semantic layer fixes this at the root: “active customer”, “revenue”, “churn” are each defined once, centrally, in reviewed code, and every dashboard, export and query inherits that definition. The effect is that a number in one report matches the same number in another, and decisions stop stalling on whose spreadsheet is correct.
Analytics that does not fight production
Running reports against the database that also serves your application is a false economy: a heavy analytical query can lock tables, spike load and slow the product for real users at exactly the moment someone runs a big report. A warehouse separates the two concerns entirely: the operational database stays lean and fast for transactions, while analysis runs against a store built and scaled for exactly that. Each system does the one job it is good at, and neither degrades the other.
Why teams choose us for Data Warehouse Development
- You want the dimensional model designed by engineers who have felt what a bad one costs, who will decide the grain of each fact table deliberately, choose star versus snowflake on the real trade-off, and get history handled correctly before fifty reports depend on it, rather than treating the schema as a detail to sort out later.
- You want the platform and the warehouse-versus-lakehouse decision argued honestly from your workload. Snowflake, BigQuery, Redshift or Databricks chosen on how your data is shaped and queried and what your team can operate, not on which name looked best in a vendor deck.
- You want the transformation layer built as tested, version-controlled code (typically dbt), so the logic that turns raw data into trusted tables is reviewable, has lineage, and is not buried unversioned inside a BI tool where nobody can see or test it.
- You want someone who will right-size the thing, who will build a warehouse when the data genuinely warrants one and tell you plainly when a well-structured operational database and a read replica would serve you better and cheaper, rather than selling you a stack you will spend more time maintaining than using.
What Data Warehouse Development includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
Dimensional modelling, facts, dimensions and grain
The core of the work: modelling the business as fact tables (the measurable events. An order, a payment, a page view) surrounded by dimension tables (the context: customer, product, date, location). We decide the grain of each fact explicitly, because it governs everything: what can be aggregated correctly, how metrics compose, and whether a report is a clean query or a minefield of caveats. Star schemas for the query simplicity and speed most analytics wants; snowflake where normalising a large dimension genuinely earns it. The output is a model a new analyst can read and a business can trust.
Choosing the platform. Snowflake, BigQuery, Redshift, Databricks
The cloud data warehouses have different strengths, and the right one depends on your workload, your existing cloud, your team and your cost profile. Snowflake for its separation of storage and compute and operational simplicity; BigQuery where a serverless, GCP-native model fits; Redshift where the AWS estate and workload favour it; a Databricks lakehouse where you want warehouse-style analytics and data-science and ML workloads on one platform. We make the call on the trade-offs that actually apply to you, and we are candid that for many teams several of these would work fine and the deciding factor is operational fit, not raw capability.
Warehouse, data lake or lakehouse
These are not the same thing and the distinction matters. A warehouse stores structured, modelled data optimised for fast SQL analytics. A data lake stores raw data of any shape cheaply, which is flexible but undisciplined: a lake with no modelling becomes a swamp nobody can query. A lakehouse aims to combine the two: lake-style cheap, open storage with warehouse-style structure, transactions and performance on top. We help you choose based on whether you have unstructured or semi-structured data and ML workloads that a lakehouse serves well, or a primarily structured, SQL-analytics need that a classic warehouse handles more simply.
Slowly-changing dimensions and history
Business facts change (a customer moves region, a product changes category, a salesperson changes team), and how the warehouse remembers those changes decides whether historical analysis is correct. Overwrite the old value and you can no longer answer “which region were they in when they bought this”; keep versioned history and you can. We implement slowly-changing dimensions to the type each dimension actually needs, overwrite where only the current value matters, full history where point-in-time accuracy is required, so reports about the past reflect the past as it was, not as it is now.
Transformation with dbt and the modern data stack
Once data is ingested, it has to be transformed from raw source shapes into the clean, modelled tables analysts read. We build this as dbt: transformations written as version-controlled SQL, with tests that catch bad data before it reaches a dashboard, documentation and lineage generated from the code, and a clear staging-to-marts structure. The point is that the logic turning raw data into trusted numbers lives in reviewable, testable code, not scattered, unversioned, across a BI tool where it cannot be inspected, tested or reused.
The semantic and metrics layer
Between the modelled tables and the reports sits the layer that defines metrics once. Rather than each dashboard re-deriving “monthly recurring revenue” or “active user” in its own way (and getting subtly different answers), we define each metric centrally, so every consumer inherits the same logic. This is what turns a warehouse from a place data is stored into a genuine single source of truth: consistency is enforced by construction, and a change to a definition propagates everywhere instead of being fixed in one dashboard and forgotten in five others.
Where it fits
Consolidating scattered data into one source of truth
A business whose data lives in a CRM, a finance system, a product database and a heap of spreadsheets, where every cross-system question means exporting and stitching by hand. We bring those sources into a single modelled warehouse, resolve the mismatches between them (the customer who is one record here and another there), and build the dimensional model that lets someone answer a question spanning all of them in one query. The value is that “what is our actual revenue per customer segment” stops being a two-day exercise and becomes a report.
Ending the “whose number is right” argument
An organisation where reporting exists but nobody trusts it, because the same metric comes out differently depending on who built the dashboard and how they defined it. We model the data cleanly and put a semantic layer over it so each metric is defined once, centrally. After that, the number in the sales dashboard matches the number in the board pack because both derive from the same definition, and reviews stop opening with an argument about whose figure to believe and get on with the decision.
Taking analytics load off the production database
A product whose reporting has been bolted onto the operational database, where a big analytical query now periodically slows the application for real users. We stand up a proper warehouse, move the analytical workload onto it, and leave the production database to do the transactional job it is good at. Analysts get a store built for the heavy, wide-ranging queries they run; customers get an application that stays fast because it is no longer competing with the finance team’s month-end report.
Building the foundation for scaling analytics and ML
A company that has outgrown ad-hoc analysis and wants to add self-serve analytics, more sources, and eventually data-science and ML work. We build the warehouse or lakehouse as a deliberate foundation: modelled, tested, documented, with the transformation layer in code and a semantic layer on top, so that adding a source or a dashboard is routine and a data team has clean, well-understood tables to build models on. The warehouse becomes the platform the rest of the data capability grows from, rather than a bottleneck it grows around.
How we approach Data Warehouse Development
We start from the questions the business needs answered, not the tables you happen to have. Dimensional modelling is done well when it is driven by the analyses people actually run (what they measure, how they slice it, what they compare against), because those questions decide the facts, the dimensions and, most importantly, the grain of each fact table. Getting the grain right is the single most consequential decision in the whole model: it is what determines whether a metric can be aggregated correctly, whether history holds together, and whether a report is a simple query or a tangle of caveats. So we spend real effort there, and we write the model down, because it is far cheaper to change on a whiteboard than after fifty reports depend on it.
From that model we build the modern data stack around it: data ingested into the warehouse, transformed with tested, version-controlled code into the clean modelled tables, and exposed through a semantic layer that defines each metric once. We choose the platform on your workload and the warehouse-versus-lakehouse question on the honest trade-offs, and we right-size the whole thing, no standing up a Snowflake estate and a dbt project for a business that a well-indexed read replica would serve. The aim is a foundation that makes downstream reporting easy, because the hard thinking was done in the model rather than pushed into every dashboard.
How the engagement runs
We open with the questions, not the tools. The first work is understanding what the business needs to measure, how it slices those measures, and what it compares against, because those analyses decide the facts, the dimensions and the grain of the model. We map the sources feeding in, the mismatches between them, and where the current reporting hurts. Out of that comes a dimensional model written down and reviewed, and an honest recommendation on platform and on warehouse-versus-lakehouse. This is also where we will tell you if you do not actually need a warehouse yet, better to learn that now than three months into building one.
From there we build the stack in reviewable steps: ingestion into the warehouse, transformation as tested dbt code turning raw data into the modelled tables, slowly-changing-dimension handling where history matters, and a semantic layer defining the metrics. You see progress in working, queryable tables and in the first trusted reports coming off them, not in a status deck. Data-quality tests, access control and lineage go in as part of the build rather than being retrofitted, and we hand over documentation and the model itself so your own analysts can extend it. The measure of success is a report that used to take days returning in seconds, and returning the same answer every time.
How we architect it
The heart of the architecture is the dimensional model and the modern data stack around it: ingest, warehouse, transform, serve. Data is ingested from the source systems into the warehouse in as raw a form as is practical; transformation (built as tested, version-controlled dbt code), turns those raw shapes into staging tables and then into the modelled fact and dimension tables that analytics reads. On top sits the semantic layer that defines metrics once, and above that the BI tools and analyses that consume them. Keeping transformation in code between the raw and modelled layers is deliberate: it makes the logic reviewable, testable and traceable, so when a number looks wrong there is a clear lineage back to where it came from rather than a shrug.
We are explicit about what the warehouse is and is not. It is the analytics-optimised store and the model: it is not the pipelines that feed it, which is data engineering’s ETL work, and it is not the dashboards on top, which is business intelligence. Confusing these is how projects end up with a beautiful BI tool sitting on an unmodelled swamp, or a heroic pipeline delivering data into a schema nobody can query sensibly. On the platform itself we lean on the strengths of modern cloud warehouses, separated storage and compute so analysis can scale without over-provisioning, and columnar storage that makes wide analytical scans fast, and we choose warehouse or lakehouse on whether the workload is primarily structured SQL analytics or genuinely spans raw, semi-structured data and ML.
Security
A warehouse concentrates data from across the business into one place, which is exactly what makes it useful and exactly what makes it a liability if access is loose. So access control is designed in from the start: role-based access so each person and each tool can read only the data their role justifies, analytical consumers given read-only credentials, and administrative access separated from day-to-day querying. The principle is that pulling everything into one store must not mean everyone can see everything in it: the blast radius of a central store is, by definition, the whole business’s data.
Personally identifiable information gets particular attention, because a central store is where PII from many systems ends up together and the obligations around it are real. We identify sensitive columns explicitly, restrict access to them, and apply masking, tokenisation or column-level controls where a role needs the surrounding data but not the personal detail itself. We minimise: holding personal data only where an analysis genuinely needs it and only as long as it is needed, and keep an eye on the retention and residency obligations that come with holding it. Data is encrypted in transit and at rest, secrets are kept out of transformation code and logs, and access is auditable, so who read what is a question with an answer. Security here is a property of how the warehouse is designed, not a review bolted on before an audit.
Signs it’s time
- Your data is scattered across systems that were never meant to be analysed together (the CRM, the finance tool, the product database, a pile of spreadsheets), and every cross-system question is a manual export-and-stitch exercise
- Reports take too long and the numbers conflict: the same metric comes out differently depending on who built the dashboard, and every review starts by arguing about whose figure is right
- Analytical queries are running against your production database and slowing down the application your customers use, because there is no separate place built for analysis
- Analytics is outgrowing what an operational database can serve. You are adding sources, analysts and dashboards, and the ad-hoc approach that worked at small scale is now the bottleneck
Our working method
The organising belief is that the warehouse is the foundation the whole analytics stack sits on, so the data model deserves real, up-front thought. We model dimensionally from the questions the business asks, decide the grain of each fact table before anything is built on it, and handle history (slowly-changing dimensions), the way each dimension actually needs rather than uniformly. We build transformation as tested code so the logic that produces trusted numbers is reviewable and has lineage, and we put a semantic layer over the model so metrics are defined once and stay consistent. A badly modelled warehouse makes every downstream report painful, so we would rather spend the effort on the model while it is cheap to change than let the pain accumulate across fifty dashboards.
The other half of the method is right-sizing, honestly. Not every business needs a warehouse, and we will say so: a company with data in one well-structured operational database and modest reporting needs is often better off querying a read replica than standing up and maintaining a whole stack. We recommend a warehouse (or a lakehouse), when the data really is scattered, when reporting has become slow or contradictory, or when analytics has outgrown the operational database. And we are careful to keep the boundaries clear: the warehouse is distinct from the ETL pipelines that feed it and the BI that sits on top, and treating it as its own well-designed layer is what stops the whole analytics effort collapsing into a swamp.
Technologies we build it with
Chosen per problem, not per fashion. This is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Want a straight answer on Data Warehouse Development?
A short call with a senior engineer, before you write a brief. If Data Warehouse Development is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
One source of truth
A central, properly modelled warehouse where a metric is defined once and every report inherits the same definition, so teams stop arguing about whose number is right and start deciding on it.
Reporting that is fast
An analytics-optimised store, separate from production, where questions that used to take days of manual stitching return in seconds, and where heavy analysis never slows the app your customers use.
A model that scales
A dimensional model built around the questions the business actually asks, so adding a source, a dashboard or a new metric is straightforward rather than a fight with the schema.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- Fixed-scope build for a well-defined warehouse. A defined set of sources, a dimensional model, the transformation layer and the first reporting-ready marts, quoted once the sources, the questions and the grain are understood, so you are not paying for scope nobody has pinned down.
- Focused assessment or modelling engagement. A review of an existing warehouse that has become slow or inconsistent, or a dimensional-modelling and platform-selection piece delivered as a model and a recommendation before any build, priced by the scope of the problem.
- Monthly senior engagement for an evolving warehouse, where sources, models and metrics grow with the business and you want continuity on modelling, transformation, the semantic layer and operational health rather than a one-off handover.
- Advisory-only where the honest answer is that you do not yet need a warehouse. A short engagement to right-size the problem and point you at the cheaper approach that fits, rather than a build you would regret.
Typical timeline
- 01
Discovery and dimensional modelling
One to two weeks understanding the questions the business needs answered, mapping the sources and their mismatches, and designing the dimensional model (the facts, dimensions and the grain of each), plus an honest platform and warehouse-versus-lakehouse recommendation.
- 02
Platform, ingestion and transformation
Standing up the chosen warehouse, getting the sources ingested, and building the transformation layer as tested dbt code that turns raw data into the modelled staging and marts tables, with data-quality tests catching bad data before it reaches a report.
- 03
History, semantic layer and first reports
Implementing slowly-changing dimensions where history matters, adding the semantic layer so each metric is defined once, and delivering the first reporting-ready tables, proving the model against real analyses rather than assuming it holds.
- 04
Access control, lineage and handover
Role-based access and PII protection in place, lineage and documentation generated, monitoring on freshness and quality, and a handover so your own analysts and engineers can extend the warehouse and trust what comes out of it.
What working with us actually means
Senior engineers only
Dimensional modelling is easy to do adequately and hard to do well. The wrong grain, the fact that cannot be aggregated correctly, the dimension whose history was overwritten and can never be recovered. The people designing your model have built warehouses that stayed trustworthy as sources and reports piled up. No juniors learning what a fact table is on the foundation your whole analytics capability will sit on.
We operate what we build
We run the warehouses we design, so the things that matter over time (data-quality tests, freshness monitoring, lineage, sane cost management on the platform), go in from the start rather than after the first month where a dashboard silently served stale or wrong numbers. We optimise for a warehouse that stays correct and fast as sources and analysts multiply, not one that demos well on a handful of clean tables.
We right-size, and we will talk you out of it
Not every business needs a warehouse, and we would rather tell you that now than sell you a stack you will spend more time maintaining than using. Where a well-structured operational database and a read replica would serve you, we will say so. Where the data genuinely warrants a warehouse or a lakehouse, we build it, and we choose the platform on your workload, not on what is fashionable this quarter.
We keep the layers honest
The warehouse is distinct from the pipelines that feed it and the BI that sits on top, and treating it as its own well-designed layer (a properly modelled single source of truth), is what stops the whole analytics effort collapsing into a swamp with a nice dashboard on it. We build the model with the care it deserves, because everything downstream inherits it.
How to engage us
Three ways to work with us on this, chosen to fit the problem, not our margin.
- Dedicated team A standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project. Ongoing product development
- Staff augmentation Named senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity. Filling a capability gap
- Software outsourcing A defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled. Outcome-owned delivery
Related services
Part of Data Engineering. Other work we do alongside this.
Common questions
What is the difference between a data warehouse, a data lake and a lakehouse?
A data warehouse stores structured, modelled data optimised for fast SQL analytics. It is where your cleaned, trusted, reporting-ready tables live. A data lake stores raw data of any shape very cheaply, which is flexible for data-science and machine-learning work but undisciplined: a lake with no modelling on top tends to become a swamp nobody can query with confidence. A lakehouse tries to combine the two: the cheap, open storage of a lake with the structure, transactions and performance of a warehouse on top, on platforms like Databricks. For a business whose need is primarily structured SQL analytics and reporting, a classic warehouse is usually the simpler, better fit; where you also have significant unstructured or semi-structured data and ML workloads, a lakehouse can serve both from one platform. We choose based on your actual workload rather than the label.
Do we actually need a data warehouse, or is our current database enough?
Not every business needs one, and we will tell you honestly if you do not. If your data lives in a single, well-structured operational database and your reporting needs are modest, you are often better off querying a read replica (so heavy reports do not slow the application), than standing up a warehouse and a whole modern data stack you will spend more time maintaining than using. A warehouse earns its place when your data is genuinely scattered across systems that were never meant to be analysed together, when reports have become slow or contradictory, or when analytics has outgrown what an operational database can serve. We right-size the recommendation to where you actually are, not to the biggest thing we could build.
How is this different from ETL and from business intelligence?
They are three distinct layers, and keeping them distinct is what makes the whole stack work. ETL (the pipelines that extract data from source systems and load it in), is what feeds the warehouse; that is data-engineering work. The warehouse itself is the analytics-optimised store and, crucially, the data model: the facts, dimensions and grain that make the data trustworthy and fast to query. Business intelligence is what sits on top: the dashboards, reports and self-serve analytics that read from the warehouse. Confuse these and you end up with a beautiful BI tool sitting on an unmodelled swamp, or a heroic pipeline delivering data into a schema nobody can query sensibly. This service is the warehouse in the middle. The model everything else depends on.
Why does the data model matter so much, and what is a slowly-changing dimension?
The model matters because the warehouse is the foundation the whole analytics stack sits on: a badly modelled warehouse does not fail loudly, it just makes every downstream report a little painful. The join that is subtly wrong, the metric that means two things, the query that scans everything because the grain was never decided, until nobody trusts the numbers. The grain of a fact table (exactly what one row represents) is the most consequential decision, because it governs whether metrics aggregate correctly. Slowly-changing dimensions are how the warehouse remembers changes to context: a customer moving region, a product changing category. Handle them right and you can still answer “which region were they in when they placed that order”; overwrite the old value and that history is gone for good. We decide both deliberately, before reports depend on them.
Which platform should we use. Snowflake, BigQuery, Redshift or Databricks?
It depends on your workload, your existing cloud, your team and your cost profile, and for many businesses several of these would work perfectly well, so the deciding factor is usually operational fit rather than raw capability. Snowflake is a strong default for its separation of storage and compute and its operational simplicity. BigQuery fits well where you are on Google Cloud and want a serverless model. Redshift makes sense where the AWS estate and workload favour it. A Databricks lakehouse is the choice where you want warehouse-style analytics alongside data-science and ML work on one platform. We make the recommendation on the trade-offs that actually apply to you and are candid about where the choice genuinely does not matter much: we would rather you pick the one your team can operate comfortably than the one with the loudest marketing.
Thinking about Data Warehouse Development?
Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Data Warehouse Development is the right answer here, or what would be.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.