A central data layer on which your BI, dashboards, machine learning models and RAG applications can run. Built vendor-independently on Snowflake, BigQuery, Databricks or your own lakehouse: modular, with code ownership on your side, and GDPR-compliant from day one.
A data warehouse is the central storage and processing layer where you bring together all relevant data from your operational systems, transform it into reliable models, and make it available for analytics, BI, dashboarding and, increasingly, AI and machine learning applications. It is not a copy of your operational database; it is a purpose-built data model in which definitions are explicit, history is preserved and queries stay fast even across billions of rows.
We build data warehouses for mid-market and enterprise organisations that run multiple source systems (CRM, ERP, e-commerce, product analytics, marketing stacks) and want to consolidate their reporting and AI foundations into one reliable base. The conversation often starts with "our figures don't add up" or "every department calculates revenue differently" and ends with a data model in which business definitions are captured once and applied consistently through every dashboard, KPI report and LLM prompt.
A data warehouse rarely stands alone. It is usually the heart of a broader data engineering platform, with ingestion, orchestration, transformation and observability as surrounding layers. For the visualisation layer on top, one of two routes is usually sensible: a commercial BI tool such as Power BI or Tableau, or a custom BI layer that runs in your own brand style and can be embedded in your product. In the first conversation we advise which route suits, based on who will use it and which compliance requirements apply.
Traditional data warehouse (Inmon or Kimball): the classic approach using normalised data vault or dimensional star schemas. Suited to stable business processes where data models last for years and governance requirements are strict, such as financial reporting, annual accounts and regulatory data. Kimball, with fact and dimension tables, remains a strong choice for analytics-heavy workloads where speed and consistency outweigh flexibility. We build this on Postgres with Citus for on-premises deployments, or on a cloud warehouse such as Snowflake or Azure Synapse as data volumes grow.
Modern data stack (ELT and dbt): the standard of recent years. Raw data is loaded via Airbyte, Fivetran or Meltano into a cloud warehouse, transformations are built in dbt, orchestration runs through Airflow, Dagster or Prefect, and a BI layer sits on top. The ELT approach (extract, load, transform, rather than extract, transform, load) suits cloud warehouses that have the compute capacity to run transformations at query time. dbt has democratised the transformation layer: SQL with version control, tests, lineage and documentation as a by-product. For most mid-market organisations, this is the right starting architecture.
Lakehouse (Iceberg or Delta): the architecture that combines the best of a data lake and a data warehouse. It uses open file formats (Parquet) beneath a transactional table format layer (Apache Iceberg, Delta Lake, Apache Hudi) that delivers ACID guarantees without vendor lock-in. Suited to organisations with both structured and unstructured data that want several compute engines running on the same storage. Databricks delivers this with Delta Lake, Snowflake with Iceberg tables, or you can run your own open-source stack on S3 or GCS with Trino, Spark or DuckDB.
Data mesh (federated): for enterprise organisations with multiple business units or subsidiaries that want to keep managing their own data but need to collaborate across it. Each domain team owns its own data products, and a central team provides governance, the data catalogue and the federated query layer. It works well when the organisation is mature enough to carry decentralised ownership; for mid-sized companies it is usually overkill. We build mesh implementations on Snowflake Data Sharing, BigQuery Analytics Hub or a custom Trino layer.
Which route is right depends on your scale, use cases and organisational maturity. We deliberately don't always recommend the same thing, as one architecture for everyone is the wrong answer.
Most warehouse projects fall into one of these three profiles. Which one fits depends on the number of source systems, the data volume, and how much you want to keep in-house. In the first conversation we'll discuss which route suits you and where the right cloud choice lies.
A warehouse project in which we set up one cloud warehouse (BigQuery, Snowflake or Azure Synapse) with three to five source systems connected via Airbyte or Fivetran, dbt transformations with version control and tests, and Airflow or Dagster as orchestrator. Suited to mid-market organisations consolidating their data for the first time or building a first reporting layer. We deliver a central data model with core tables for your key entities (customers, orders, products, leads) plus the first mart layer for BI purposes. From there, your team can build further on it themselves, or you can have us continue development under a managed arrangement.
For organisations that have both structured and semi- or unstructured data (logs, JSON events, documents, images) and want their warehouse ready from the outset as a foundation for AI and LLM applications. We build a lakehouse on Databricks Delta Lake, Snowflake with Iceberg tables, or an open-source stack on S3 or Google Cloud Storage with Trino and DuckDB. On top come vector tables for RAG applications, feature stores for ML models, and a metadata catalogue that your AI development team can use without having to build data pipelines themselves. AI Act compliance layer included.
For enterprise organisations with multiple business units, subsidiaries or strict regulatory regimes (banks, insurers, healthcare groups, government). We build a federated architecture in which each domain team owns its own data product, with a central governance layer that manages the data catalogue, lineage, access control and quality monitoring. Components such as Snowflake Data Sharing, BigQuery Analytics Hub, or a Trino layer across multiple warehouses are combined with a data catalogue (DataHub, OpenMetadata, Collibra) and observability (Monte Carlo, Soda). Full GDPR and DPIA documentation, EU data residency, and a per-query audit trail.
A production-ready data warehouse that your team manages itself, plus everything around it so you can keep building without vendor lock-in. Code ownership is the starting point: all SQL, all DAGs, all infrastructure-as-code and all documentation sit with you in Git.
Six patterns in which organisations come to us for a warehouse project. If you recognise one of them, a conversation is usually worthwhile. We are honest about when a lighter alternative will do: sometimes a central dashboard is enough, and a full warehouse is over-investment.
You have CRM, ERP, e-commerce, product analytics and marketing stacks, and each department exports separately to Excel. Definitions drift apart (sales calculates revenue differently from finance), reports differ by team, and nobody trusts the numbers any more. A central data layer in which everything comes together in one data model fundamentally solves this.
RAG applications, customer-specific chat assistants, predictive models for churn or fraud. All of these require a reliable data foundation on which feature stores and vector tables run. A warehouse is the typical prerequisite for serious AI development; without one there is no reproducibility, no lineage and no confidence in model outputs.
Operations, finance, marketing and management each want their own dashboard with their own KPIs. A central warehouse with a semantic layer is the natural foundation for a custom KPI dashboard per department, with consistently calculated metrics. No more conflicting definitions between reports.
Logistics, energy, e-commerce, gaming: environments where a daily refresh is too slow and you need to steer on live status. A warehouse is often not enough in those cases; you need a real-time analytics platform alongside or on top of the warehouse, fed by event streams on Kafka or Materialize.
Healthcare organisations handling patient data, financial institutions with DORA reporting, energy companies with regulatory logging. Governance, audit and data residency requirements become so stringent that ad hoc spreadsheets are no longer legally tenable. A warehouse with PII redaction, retention policies and EU data residency then becomes a necessity rather than a luxury.
You are locked into a SaaS reporting tool that does not give your data back, or into a legacy data warehouse whose licence costs rise faster each year than the value it delivers. A modular setup built on open formats (Parquet, Iceberg, dbt) gives you the option to switch compute engines in future without losing your data model.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →A conversation to understand which decisions your organisation wants to support with data, which sources are involved, and which compliance requirements apply. We check straight away whether a full warehouse is the right investment; sometimes a lighter alternative is sufficient, and that is more honest advice than a heavier project.
A workshop with your team, plus interviews with the end users of the data (analysts, finance, operations). We draw up an architecture blueprint (warehouse choice, ELT pipeline, semantic layer, governance layer) and an initial data model for the key entities. At the end you have a defined scope, a technology stack choice and a realistic estimate of production cloud costs.
A working build every two weeks. We start with one source, one data model layer (staging, intermediate and the first mart) and basic orchestration. From sprint three onwards your team can already see first results in Power BI, Metabase or another BI tool. Subsequent sprints add sources and extend the data model.
dbt tests on all models, freshness and volume checks via Soda or Monte Carlo, setting up lineage and a data catalogue, and implementing PII redaction and retention policies. For AI applications, we also add the feature store and vector tables, with access controls that fit your position under the AI Act.
Before go-live: a penetration test on the cloud environment, a review of row-level security and encryption, an audit trail check, and data flow mapping for the DPIA. For sector-specific regimes we deliver the additional documentation that your data protection officer or regulator expects.
Phased rollout to user groups, a hands-on dbt workshop for your analytics team, and ongoing management of security, performance and cost optimisation. Cloud warehouses can become significantly more expensive if nobody monitors query patterns, so we provide the tooling and runbook as standard to keep this under control.
Our stack choices are pragmatic rather than religious. For ingestion, Airbyte and Fivetran are both good options, and the choice depends on your source portfolio and budget: Fivetran is more expensive but needs less maintenance, while Airbyte is open source and works excellently self-hosted. For custom-built connectors, we work with Meltano or our own Python layer. For orchestration, Airflow remains the de facto standard, but Dagster and Prefect have strengths in data asset tracking and developer experience respectively. We advise based on your team's skills and operational needs.
For transformation, dbt is the starting point: virtually all modern warehouse projects use dbt for SQL models with tests, lineage and documentation. For Python-based transformations (denoising, feature engineering, NLP) we use dbt-python, Polars or Spark, depending on scale. For the warehouse layer itself, we have extensive experience with Snowflake, BigQuery, Azure Synapse, Databricks, Redshift and ClickHouse, plus on-premises Postgres with Citus for situations where cloud is not an option.
For BI on top, we can work with any common tool (Power BI, Tableau, Looker, Metabase) or build a custom BI tool when embedded analytics or multi-tenancy is needed. For observability we use Monte Carlo or Soda, for the data catalogue DataHub or OpenMetadata. For AI integrations we work with LangChain, LlamaIndex or our own RAG orchestration, and we build vector tables on pgvector, Weaviate, Pinecone or Snowflake Cortex.
The thinking behind these choices is the same: you get a warehouse built on open standards (SQL, Parquet, Iceberg, dbt), so your team can switch vendors in future without losing your data model. Vendor independence is not a marketing slogan but an architectural principle. We build warehouses that you can maintain yourselves, or that another team of yours can take over.
What clients usually want to know before starting a warehouse project.
A no-obligation introductory call of half an hour. We listen to who uses the data, which sources are involved and which compliance requirements apply, and we tell you honestly whether your own warehouse is the right route or whether a lighter alternative is enough. No sales funnel, just genuine sparring.
Appfront uses cookies and similar technologies to keep the website working properly, for analytics and for marketing. You choose what you allow. Read more in our privacy policy.
