Data warehouse services: one place your reports can rely on

When every report pulls from a different system, every meeting starts with an argument about whose number is right. We design and build data warehouses and lakehouses on Azure, AWS, Microsoft Fabric and Google Cloud, load them on a schedule from the systems you already run, and alert someone when a load fails.

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What we cover

When you need oneSigns your reporting has outgrown the source systems
DesignModeled around the questions you ask
Data integrationPipelines that load on time, and say when they do not
Cloud analytics platformsAzure, AWS, Fabric, Google Cloud or the database you have
Quality and accessNumbers people trust, and access they should have
After go-liveReports, analytics agents and someone to run it

When you need one

Signs your reporting has outgrown the source systems

Most companies start by reporting straight from the ERP, the CRM and a few spreadsheets. That works until the questions cross systems: margin by customer, which needs sales and finance together, or a trend that needs years of history the application no longer keeps. Reports slow down the systems people work in, analysts copy data by hand, and the same measure comes out differently depending on who built the report.

A data warehouse gives reporting its own home. Data is copied out of the source systems, cleaned, joined and kept with its history, in a structure built for questions rather than transactions. The applications stay fast, and everyone reads the same numbers.

Design

Modeled around the questions you ask

We start with the decisions the warehouse has to support, not with the tables in the source systems. With the people who use the reports, we list the measures, the dimensions they are cut by (customer, product, region, period) and the level of detail each one needs. From that we design the model: fact and dimension tables for reporting, a cleaned layer below it, and a raw layer that keeps what each source sent, so a load can be replayed.

Where a lakehouse fits better, for example when files, logs and semi-structured data sit beside the tables, we design one, and the reporting model on top stays the same.

What we do

Requirements workshops with report owners
Dimensional model and data dictionary
Raw, cleaned and reporting layers
History kept for every change that matters

Data integration

Pipelines that load on time, and say when they do not

Getting data out of the source systems is most of the work. We build the pipelines that extract from your ERP, CRM, finance and line-of-business databases, files and APIs, load on a schedule or as changes happen, and check what arrived: row counts, required fields, values that should never appear. When a load fails or a check does not pass, the pipeline alerts a named person instead of leaving a gap in the next report.

Every data flow is documented, so your team can see where each figure comes from and what happens to it on the way.

What we do

Batch and incremental loads
Validation checks on every load
Failure alerts to a named owner
Documentation of every data flow

Cloud analytics platforms

Azure, AWS, Fabric, Google Cloud or the database you have

We build on the platform that fits your estate and your team. On Microsoft that is usually Azure or Microsoft Fabric, which brings data engineering, warehousing and Power BI reporting into one service. On AWS and Google Cloud we use their managed warehouse and storage services, and we also work with Snowflake and with warehouses built on Oracle and SQL Server. Moving an existing warehouse to the cloud is a migration in its own right: we plan it in waves, run the old and the new side by side, and compare the results before anyone switches.

What we do

Cloud data warehouses on Azure, AWS and Google Cloud
Lakehouse and warehouse design in Microsoft Fabric
Warehouse migrations, tested side by side
Cost and performance tuning

Quality and access

Numbers people trust, and access they should have

A warehouse is only useful if people believe it. We agree the definition of each measure with its owner and write it down, add quality checks where the data enters, and route problems to the person who can fix them at the source. Access follows your roles, so finance data stays with finance and personal data is masked where it should be. For deeper data quality work, such as profiling, matching and master data, our master data management service uses Ataccama ONE.

What we do

Agreed definitions for every measure
Quality checks at the point of entry
Role-based access and masking
An audit trail of what changed and when

After go-live

Reports, analytics agents and someone to run it

Once the warehouse is live, we connect Power BI or the reporting tool you use, and we can add analytics agents that let managers ask a question in plain English and get an answer computed from the data. If you would rather not run the pipelines yourself, our managed data service watches the loads, fixes failures and keeps the model current as your systems change.

Questions

Common questions

What is the difference between a data warehouse and a data lake?

A warehouse holds cleaned, structured data modeled for reporting, so the same question always gets the same answer. A lake stores raw data of any kind, including files, logs and documents, in its original form, for exploration, data science and AI. Many companies need both, and a lakehouse keeps the two in one platform, with a reporting model on top of the raw files.

Do we have to move to the cloud to have a data warehouse?

No. We build warehouses in the cloud and on your own servers. The cloud is usually quicker to start and easier to scale, but if your data must stay on premises, or you already run a capable database, we design for that.

Which source systems can you load from?

Anything with a database, an API or a file export: ERP, CRM, finance, e-commerce, spreadsheets and line-of-business applications. We look at each source first, because how a system exposes its data decides how often and how safely it can be loaded.

How long does a data warehouse project take?

It depends on the number of sources and how clean they are. We do not model everything at once: we start with one subject area that matters, such as sales or finance, get its reports live, and then add the next. You get a phased plan after the assessment.

Can you take over a warehouse someone else built?

Yes. We review the model, the pipelines and the documentation, fix what is fragile, and then either hand it back to your team or run it for you.

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