Data Platform and Analytics

Data assets are only worth something if they're trusted, traceable, and available in a form that supports business decisions. A well-built data platform provides the foundation for the entire organisation's data-driven operations — well beyond report generation.

The InForge Labs team works on live projects across three technology families: cloud-native lakehouse (Databricks, Delta Lake), open source modern data stack (Airbyte, dbt, Dagster), and classic enterprise DWH (Oracle, MS SQL Server). The right approach depends on the client's existing environment, maturity and strategic direction.

What we deliver

Cloud-native

Modern Data Platform

Databricks-based lakehouse architecture with Delta Lake and Unity Catalog governance. Medallion (Bronze/Silver/Gold) layering, ADLS Gen2, Azure Data Factory orchestration. PySpark, Photon, DLT.

Open source

Modern Data Stack

Airbyte ingestion for classic and modern sources, dbt transformation layer, Dagster or Airflow orchestration, Kubernetes-based deployment. PostgreSQL or other open source database backend.

Enterprise

Classic DWH

Oracle and MS SQL Server-based data warehouse design and build. Kimball dimensional modelling, SSIS-based ETL, full historisation (SCD), data lineage. Banking- and telecom-grade audit requirements.

Migration

Migration and modernisation

SQL Server → Databricks, traditional enterprise data warehousing → modern data stack transitions. Hybrid environments where legacy and modern coexist. A reengineering-first mindset: not a pure lift-and-shift, but value-adding modernisation.

Governance

Data governance and quality

Unity Catalog or classic governance framework, automatic data lineage, DQ rules (Bronze→Silver), validation framework. Audit trail, compliance reporting (GDPR, NIS2, MNB).

Analytics

Business Intelligence

Power BI, Qlik Sense and SSRS-based reporting solutions. Self-service BI, semantic layer design, DirectQuery, row-level security. Executive dashboards and audit-ready reports on a trusted data foundation.

How we work

Our core team includes data engineers and architects with 20-25 years of enterprise experience. People who have built clearing systems from scratch, migrated petabyte-scale data assets, and gone live with modern Databricks-based platforms — in telecommunications, banking and public sector environments.

Our approach is technology-agnostic: the right stack fits the specific business problem and environment. At the start of a project, our first question is what the actual business problem is, what source systems exist, what the compliance context looks like, and at what maturity level the client's IT organisation operates. Based on this, we choose the technology — and if needed, work with hybrid stacks.

Incremental delivery is the default: we don't build a massive predefined end state. We start with a smaller, working data scope, demonstrate the business value, then expand gradually. This way the data platform grows alongside the client's needs — without having to redesign the foundational architecture.

When it makes sense to reach out

You're planning a new data platform — and looking for a partner who understands the business context before making a technology decision
You want to modernise an existing SQL Server or traditional enterprise data warehousing system — gradually, without disrupting working reports
You're planning to introduce a cloud-native lakehouse (Databricks) and need senior architect leadership
You're considering a modern data stack (dbt, Airbyte, Dagster) and looking for a partner with hands-on experience
You need to automate regulatory or compliance reporting (NMHH, MNB)
You're planning an executive dashboard strategy or self-service BI rollout, with a trusted data foundation