Data and analytics platforms that join your systems into one tested set of numbers

A data warehouse stores cleaned, structured and modelled data ready for reporting, while a data lake holds large volumes of raw data in its original form, usually as files in object storage. Warehouses answer known business questions quickly and consistently. Lakes keep everything cheaply for later processing, machine learning or replay. Most platforms we build use both ideas: raw data lands in low-cost storage first, then trusted models are built in the warehouse on top of it.

For a single application with light reporting needs, yes, ideally from a read replica rather than the primary database. That approach stops working once you need to combine several sources, keep history the application overwrites, or run heavy queries that compete with customer traffic. At that point a separate warehouse costs less than a slower product. We often begin with a replica and introduce a warehouse when the second or third source arrives.

PostgreSQL is often enough for moderate data volumes and teams that want one familiar, low-cost database, while a managed cloud warehouse makes sense when data grows large or queries scan very large tables. Managed options such as BigQuery on Google Cloud, Amazon Redshift on AWS or the analytics services in Microsoft Azure separate storage from compute and scale without server management. We compare likely volumes, query patterns and running costs for your case, and write models in portable SQL so changing engine later does not mean starting again.

We reconcile every key metric against an independent trusted figure before launch, then test the data automatically on every pipeline run. Before go-live, revenue, order counts and similar totals are matched with finance or operational reports, and each difference is explained. After launch, checks for freshness, volume, duplicates, missing values and broken relationships run with each load. When a check fails, the owning team is alerted and the affected dashboard can show a warning rather than a wrong number.

ETL transforms data before loading it into the warehouse, while ELT loads raw data first and transforms it inside the warehouse, and we usually use ELT. Keeping the raw copy means transformations can be rerun when a definition changes, without going back to the source system. Transformations live as version-controlled SQL and Python, reviewed and tested like application code. We still transform before loading where data must be masked, filtered or reduced before it leaves the source environment.

Dashboards can refresh at whatever interval the decision needs, from daily batches to near real-time streams, and the right choice depends on cost and the source systems. Most management reporting works well with hourly or daily loads, which are simpler and cheaper to run. Operational views such as order queues, stock levels or delivery status may justify streaming through Kafka or change data capture. We agree the freshness each dashboard needs and show its last refresh time on screen.

We can connect most sources that offer an API, a database connection, scheduled exports or file drops, and that includes spreadsheets. Typical sources are CRMs, accounting and ERP systems, e-commerce platforms, payment providers, Google Analytics, advertising platforms and your own application databases. Spreadsheets are brought in through controlled templates with validation, so a renamed column fails loudly instead of corrupting a report. Where a system offers no access at all, we explore export options with the vendor during the source audit.

Yes, because the warehouse is designed to be tool-neutral, so Power BI, Looker Studio, Grafana or a custom dashboard can all read the same modelled tables. Business definitions such as revenue or active customer live in the warehouse models rather than inside one tool, which keeps figures consistent wherever they are viewed. If your team already knows a BI tool well, we connect it to the warehouse rather than replacing it. Where analytics must appear inside your own product, we build embedded dashboards with Next.js and React.

We bring in only the personal fields a report actually needs and keep direct identifiers apart from the analytical tables. Names, email addresses and phone numbers are masked or replaced with pseudonymous keys, so records can still be joined without exposing who they belong to. Row-level rules limit each user to their own region, branch or client. Retention rules cover the raw copies as well as the warehouse, and deletion requests reach both. We design with UK GDPR in mind and work within any further requirements your compliance team sets.

Build time is driven by how many sources are connected, how clean their data is and how quickly teams agree metric definitions. We deliver in increments rather than one large launch. A first version that loads two or three core sources into a warehouse and produces one tested management dashboard can typically be ready within a few weeks of discovery. Further sources, models and dashboards are then added from a prioritised backlog, with working output to review every one to two weeks.

Build costs depend on the number of sources, the complexity of business rules and the dashboards required, and we give a fixed quote after discovery. Running costs come from cloud storage, compute for pipelines and queries, and any BI tool licences, and they vary with data volume and refresh frequency. We estimate monthly running costs for your expected volumes during design and choose refresh schedules and storage tiers that keep spend proportionate to the value of the reporting.

A data platform gives AI projects the clean, historical and documented data they depend on, which is usually the slowest part to get right. Forecasting and classification models need consistent history with known definitions, and assistants that answer questions about the business need reliable tables and metrics to query. Because pipelines, tests and lineage already exist, models can be trained and refreshed from the same trusted source as your dashboards. Our senior AI/ML engineer works alongside our cloud and DevOps engineers, so the data and model work is planned together.

Not necessarily, because a tested, monitored platform needs far less day-to-day attention than a set of manual reports. Someone in your organisation must still own the metric definitions and decide when they change, and that is usually an analyst or finance lead rather than an engineer. Routine engineering work, such as repairing a connector after a vendor changes its API, adding a source or tuning slow queries, can be done by an in-house developer we train or by us under an agreed support arrangement.