Wie gelingt ein strukturierter Einstieg in ein Data Lakehouse, auch im Mittelstand?

Der Einstieg beginnt mit priorisierten Use Cases und dem benötigten Datenumfang, nicht mit der vollen Plattform. Typischer Pfad: kostenloser Data Readiness Check , dann Assessment oder Discovery und ein klar abgegrenzter Proof of Value. Erst wenn Nutzen und Architektur belegt sind, folgt die Implementierung, und das interne Team wird für Betrieb und Weiterentwicklung befähigt. So bleibt der Scope planbar, auch wenn interne Data-Engineering-Kapazität begrenzt ist.

Was ist der Unterschied zwischen Data Lake, Data Warehouse und Data Lakehouse?

Ein Data Lake speichert Rohdaten flexibel, ein Data Warehouse strukturierte Analyse-Daten. Das Data Lakehouse verbindet beides: skalierbare Speicherung plus strukturierte Qualität über Medallion Architecture, als Basis für Single Source of Truth, Reporting und KI.

Wie helfen Data Lineage und Data Governance im Data Lakehouse?

Data Lineage macht Herkunft und Verwendung nachvollziehbar. Data Governance sichert Ownership, Policies und Compliance. Beides ist Bestandteil der Solution und geht über reines Plattform-Setup hinaus.

Was ist der Unterschied zwischen Data Lakehouse und Data Intelligence?

Das Data Lakehouse ist die technische und organisatorische Basis: Es verbindet Quellen, sichert Datenqualität und stellt verlässliche Datenprodukte bereit. Data Intelligence setzt darauf auf und nutzt diese Daten für KPI-Dashboards, Analytics und KI. Auf dieser Seite liegt der Schwerpunkt auf Plattform, Pipelines und Governance.

Brauchen wir zwingend Databricks oder Microsoft Fabric?

Nein. Wir sind technologieoffen. Wir verwenden den Technologiestack, der am besten zu Ihren Anforderungen passt.

Ersetzt das Data Lakehouse unser ERP oder unser BI-Tool?

Nein. Die Plattform ergänzt bestehende Produktivsysteme als analytische Schicht und liefert qualitätsgesicherte Daten an Reporting- und Analytics-Werkzeuge.

Wie lange dauert ein Proof of Value, und was kostet der Einstieg?

Bei klarem Scope rechnen wir mit etwa 4 bis 6 Wochen bis zur lauffähigen End-to-End-Pipeline. Viele starten mit dem kostenlosen Data Readiness Check und einem Proof of Value ab 9.900 €. Eine Übersicht der Orientierungspreise finden Sie unter Data Driven Solutions: Ihr Direkteinstieg .

How can a company—even a small-to-medium-sized business—successfully implement a structured approach to a data lakehouse?

The process begins with prioritized use cases and the required data scope, not with the full platform. Typical path: a free Data Readiness Check , followed by an assessment or discovery phase, and a clearly defined proof of value. Only once the benefits and architecture have been demonstrated does implementation follow, and the internal team is empowered to handle operations and further development. This keeps the scope manageable, even if internal data engineering capacity is limited.

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

A data lake flexibly stores raw data, while a data warehouse stores structured analytical data. The data lakehouse combines both: scalable storage and structured data quality through Medallion Architecture, serving as the foundation for a single source of truth, reporting, and AI.

How Do Data Lineage and Data Governance Help in the Data Lakehouse?

Data lineage ensures traceability of data origin and usage. Data governance ensures ownership, policies, and compliance. Both are integral parts of the solution and go beyond a simple platform setup.

What is the difference between a data lakehouse and data intelligence?

The Data Lakehouse serves as the technical and organizational foundation: It connects sources, ensures data quality, and provides reliable data products. Data Intelligence builds on this foundation and uses this data for KPI dashboards, analytics, and AI. This page focuses on the platform, pipelines, and governance.

Do we absolutely need Databricks or Microsoft Fabric?

No. We are open to all technologies. We use the technology stack that best fits your needs.

Will the data lakehouse replace our ERP or our BI tool?

No. The platform complements existing production systems by serving as an analytical layer and provides quality-assured data to reporting and analytics tools.

How long does a Proof of Value take, and how much does it cost to get started?

With a clearly defined scope, we estimate it will take about 4 to 6 weeks to have a fully operational end-to-end pipeline. Many start with the free Data Readiness Check and a Proof of Value starting at €9,900. You can find an overview of the estimated prices under Data-Driven Solutions: Your Quick Start .

Data Lakehouse | Data Driven Solutions | Services & Products

Data Products and Scalable Data Pipelines

With the Data Lakehouse Solution, you can overcome data silos, conflicting metrics, and a lack of data trust: We create a technology-agnostic data platform that serves as a single source of truth, featuring Medallion Architecture, automated data pipelines, data lineage, and data governance.

This results in business-ready data products for reporting, analytics, and AI, efficiently implemented using blueprints and Infrastructure as Code (IaC).

Data Trust:
A shared, transparent foundation for key metrics and ownership
Less Manual Work:
Data pipelines for processing that can keep up with growing data volumes
AI-ready:
Quality-assured data products as the foundation for insights and AI

Find out if your company is ready for the Data Lakehouse Solution:
Start a free Data Readiness Check

Challenges

Clear use cases
without a data foundation

Many companies want automated reporting, self-service analytics, or AI. In practice, however, data is scattered across CRM, ERP, e-commerce platforms, IoT, and Excel; key metrics are inconsistent; and pilot projects fail due to a lack of quality, ownership, and traceability.

Business units then wait for reliable figures, and new initiatives risk creating additional data silos. Regulations such as the GDPR, the EU AI Act, or CSRD/ESG increase the pressure whenever auditable data flows are lacking.

Typical signs:

Data silos between CRM, ERP, the online store, IoT, and Excel, with no shared view
Inconsistent metrics without a single source of truth and clear ownership
Transformation logic in Excel or a BI tool outside of stable data pipelines

Manual cleanup and updates that tie up resources
Stalled data and AI initiatives despite pilot projects that work locally

A robust data foundation for reporting, analytics, and AI

The Data Lakehouse Solution is the first building block. It provides the foundation upon which data intelligence is built. We integrate heterogeneous data sources and consolidate them into quality-assured data products.

An analytical layer complements your existing production systems.
CRM, ERP, and your online store remain in use; at the same time, we create a single source of truth for metrics, data products, and AI use cases. Blueprints, an IaC toolbox, and accelerators speed up setup and reduce implementation risk. Your team will be empowered to maintain the platform and data products going forward.

Here's what you'll get:

Single Source of Truth and Data Trust:
Common Definitions, Ownership, and Data Lineage
Data Products:
Business-ready assets for reporting, analytics, and AI

Automation and Scaling:
Data Pipelines and IaC for Data Infrastructure
Governance with Lineage:
Policies and Lineage for Compliance

Where are you in your journey?

For Data Explorer & Data Champion

Data Explorer

The initial use cases are clear, but a central platform is still needed. The focus is on integrating distributed sources, harmonization, and reliable data quality—often with fixed-price entry options and a proof of value.

More about Data Explorer

Data Champion

Analytics reaches its scaling limits when logic is tied to BI tools or Excel. That’s when scalable pipelines, clear data structures, and robust platform solutions—including multi-cloud—are needed.

More About the Data Champion

Data Lakehouse Solution

Building Blocks

IaC Toolbox for the Data Platform

Pre-built Infrastructure-as-Code components accelerate the repeatable deployment of a scalable platform based on the Medallion Architecture.

Multi-Source Integration

We integrate diverse data sources—from CRM and ERP to e-commerce platforms, IoT, and Excel—and provide unified access without requiring business units to overcome technical hurdles on their own.

Intelligent Data Pipelines

Validation, cleansing, profiling, and rule-based KPI calculation run in integrated pipelines. This results in up-to-date, reliable data products that require less manual maintenance.

Data Asset Management

Data catalogs, metadata, and data discovery make data collections discoverable and manageable. This allows data to be managed as assets and developed in a targeted manner.

Data Lineage & Data Governance

Origin, policies, and compliance audits are all part of the solution. This provides transparency for business units, IT, and audit.

Graphic showing a lakehouse as a central platform for data integration, analytics, and delivery.

Our Approach

Here's How We Build Your Data Lakehouse

We start with data assessment and data discovery: Together, we identify and prioritize your use cases, determine your data requirements, and use this information to develop the appropriate platform concept. During the proof of value phase, with a clearly defined scope, we create a working end-to-end use case with measurable benefits in about 4 to 6 weeks.

The implementation phase includes IaC setup, production-ready data pipelines, data contracts, and a data catalog. At the same time, we empower your team to further develop the platform and data products.

Request a Proof of Value Now

Typical Use Cases

Central Data Platform:
Master and live data from CRM, ERP, e-commerce, and IoT flow into a shared database for management and AI.
Replacing manual Excel and BI processing:
Data transformations are moving from spreadsheets and visualization tools to automated data pipelines.

Multi-Cloud Data Lakehouse:
Medallion Architecture with integration into existing BI landscapes, even across cloud boundaries.
Regulatory Compliance and Business Domains:
Robust data foundation for CSRD/ESG, customer journey, after-sales, and activation in marketing and sales.

Data Lakehouse

Data Products and Scalable Data Pipelines

Clear use cases
without a data foundation

A robust data foundation for reporting, analytics, and AI