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A data warehouse is a central reporting database that brings together data from separate systems such as the ERP, CRM, web shop and production floor. ETL pipelines extract it automatically, transform it to shared definitions and keep its full history. Reports run from that single source without slowing live systems, so sales and finance see the same figures.
As a business grows, its data spreads out: finance and stock in the ERP, quotes in the CRM, orders across web shop and marketplace panels, production data in the MES, staff records in the HR system. An ERP alone cannot pull all of that together. Each one produces data in its own format and on its own schedule. When a report is due, someone exports and stitches it together in Excel; sales and finance end up quoting different revenue figures, and heavy reporting queries slow the ERP down during working hours. For international companies with a Turkish subsidiary, the gap between local systems and group reporting adds another manual step.
Digital Bridge consolidates these sources into a single data warehouse with automated ETL, matches records to shared definitions and keeps the full history. Reports, BI dashboards and AI models then draw from that one source without putting load on your live systems, and everyone works from the same figures. The hours your teams spend exporting and stitching spreadsheets together at month end go into interpreting the numbers instead. As a first step, let us list your source systems and the reports that cause the most debate: request a data warehouse review.
Data is extracted from each source on a set schedule, transformed according to your business rules and loaded into the warehouse. Pipelines are scheduled and replayable; a failed load is logged, retried automatically and reported to the person responsible.
PostgreSQL, MSSQL or a cloud platform (BigQuery, Redshift), chosen to suit your data volumes and existing estate. A star-schema model of fact and dimension tables keeps reports fast, and historical figures stay intact when prices, customer records or the organisation structure change.
A separate storage layer for high-volume or unstructured data such as sensor streams, log files and images. Raw, curated and ready-to-use layers are kept apart, so you can always trace which raw data a report came from.
Empty fields, duplicates, format errors and broken references are caught as data loads. Faulty records are held back before they reach a report, and if quality falls below the agreed level the right person is alerted.
Each dataset moves at the pace the business needs: critical data such as stock and orders within minutes, finance data in a daily batch. Change Data Capture (CDC) transfers only what has changed, so the ERP and other live systems are not slowed down.
Separate, ready-to-use datasets for sales, production, finance and stock, each carrying its own KPI definitions. They are served to BI tools, to Excel or to your own applications via API.
Picture a distributor that invoices in Logo, keeps quotes in its CRM and takes some orders through marketplaces. The sales manager pulls revenue from the CRM, the finance manager from the ERP; one has deducted returns, the other has not, and the meeting is spent arguing over which figure is right. In the data warehouse, "net sales" is defined once (excluding VAT or not, after returns or not, by which date), ETL pipelines combine the three sources overnight, and everyone opens the same figure on the dashboard in the morning. The meeting can then move on to what to do in which region and product group.
The warehouse calculates each figure correctly in one place; the BI dashboard puts it in front of the people who decide. When one team builds both layers, KPI definitions are written once, and meetings move on from arguing about the numbers to acting on them. If your source data is full of duplicates or inconsistencies, we recommend starting with data governance and quality; for connecting systems to each other, see API integration.
Guides worth reading before you decide: what a data warehouse is and how ETL works, the difference between ETL and ELT and what drives data warehouse cost. Browse every article in our data and analytics guides.
We have developed software and hardware within the same team since 2013. That means we can connect sensor, meter and machine data from the production floor to the warehouse in the same project as your office systems, so you can see energy monitoring or MES data alongside sales and cost figures. The process is always the same: we review your sources and reports, then present a written proposal covering scope, phases and cost.
A data warehouse is a central reporting database that brings together data from separate systems such as the ERP, CRM, web shop and production floor. Operational systems are built for day-to-day transactions; heavy reports slow them down and cannot combine data across systems. With a data warehouse, reports run from one source and sales and finance work from the same figures.
ETL stands for extract, transform and load: data is pulled from source systems, cleaned and transformed according to your business rules, then loaded into the warehouse. The pipelines we build are scheduled and replayable; a failed load is logged, retried automatically and reported to the person responsible. Quality checks also run during loading, so faulty records are held back before they reach a report.
A data warehouse holds structured, report-ready data modelled as fact and dimension tables. A data lake stores high-volume or unstructured raw data such as sensor streams, log files and images. We often design both together, keeping raw, curated and ready-to-use layers apart so you can always trace which raw data a particular report came from.
No, it does the opposite. Reports run against the warehouse instead of the ERP, so live systems are not loaded by heavy queries. With Change Data Capture (CDC), only changed records are transferred, which keeps the ERP responsive during working hours. ERPs such as SAP, Logo and Mikro, along with CRM and e-commerce systems, can be connected as sources.
Each dataset moves at the pace the business needs. Critical data such as stock and orders can arrive within minutes, while finance data is usually loaded in a daily batch. Refresh frequency is agreed based on the source system's capacity and how the report is used, so real-time transfer is only built where it adds value and load on live systems stays low.
We use PostgreSQL, MSSQL or cloud platforms such as BigQuery and Redshift, chosen to suit your data volumes and existing systems. The model is built as a star schema of fact and dimension tables, which keeps reports fast and preserves historical figures when prices or the organisation structure change. Data is then served to BI tools, to Excel or to applications via API.
Company size matters less than how scattered your data is. If reports draw on two or three separate systems such as an ERP, CRM, web shop or production floor, someone merges them in Excel every month, and departments quote different revenue figures, a data warehouse pays off. We start with the sources behind your most-used report and extend the warehouse to new systems as needs grow.
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