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Data Warehouse / Lake / Lakehouse

Data storage and architecture concepts of modern data platforms

Choosing the right data storage concept is not a purely technical decision. It depends largely on the company’s objectives, who works with the data and what requirements are placed on governanceanalysis and scalability.

Data Warehouse

When reporting, compliance and transactional data take center stage

Data Lake

When AI experiments, exploration and raw data analysis dominate

Data Lakehouse

If both worlds are to be brought together.

In practice, the data architecture and operatingconcept Lakehouse is the most modern and future-proof most modern and future-proof approachespecially if data is seen as a strategic asset.

Data Warehouse

Focus on structured analyses, reporting and compliance

A data warehouse is the right choice if the focus is clearly on business intelligence, reporting and regulatory security.

Application scenarios

Structured business decisions

Central data source for specialist departments such as controlling, accounting or risk management - with consistent key figures and reproducible reports.

High governance and compliance requirements

Particularly suitable for regulated environments in which traceability, data origin and data quality are essential (e.g. financial or audit reports).

Standardized queries and BI usage

Efficient access via SQL and established BI tools such as Power BI or Tableau - without in-depth technical expertise.

Important note:

Without clear governance-mechanisms, a data lake can quickly become a data silo or “data swamp”.
It is therefore only suitable for traditional business users to a limited extent.

Data Lake

The preferred model for data-driven innovation

A data lake shows its strengths wherever raw data, flexibility and experimental analyses are in the foreground.

Application scenarios

Research & Development

Central collection point for structured, semi-structured and unstructured data - such as texts, images, log data or IoT sensor data

Machine learning & forecasts

Ideal basis for data scientists who develop, train and iteratively improve complex models with Python, R or Spark

Automated, expert-driven workflows

Particularly suitable for organizations with experienced data science teams and a high degree of automation

Data Lakehouse

The answer to the hybrid requirements of modern data organizations

The data lakehouse combines the flexibility of a data lake with the governance and structural mechanisms of a data warehouse. It avoids data silos and creates a common data architecture for analysis, reporting and AI.

Application scenarios

Cost efficiency and scalability

Use of inexpensive object storage with simultaneous introduction of metadata management, versioning and schema control

Data quality, security and GDPR compliance

Granular access controls, clean data models and controlled changes - even for personal data

Versatile data access

Everyone works on the same database: business users with SQL and BI tools and data scientists with snapshots and experimental environments

Our offers

Regardless of which data storage and architecture concept your individual requirements demand – whether data warehouse, data lake, lakehouse or a hybrid solution. hybrid solution. We support you both with new construction as well as with the further development of existing architectures – open to technology, methodically sound and practical.

Your added value

  • Model-agnostic consulting instead of tool fixation
  • Clear separation between business, governance and technology perspectives
  • Practical implementation with a focus on long-term usability

Requirements analysis & implementation consulting

Data warehouse, data lake or lakehouse?

Together, we develop a sound basis for decision-making for your future data platform – evaluated in a structured manner and aligned with business objectives, user groups and governance requirements.

These are the advantages:

  • Clear classification of data warehouse, data lake and lakehouse
  • Target image and architectural guidelines before tool definition
  • Investment security through structured requirements analysis
Learn more about the offer

New setup & implementation

From target image to productive data platform

Together, we develop and implement a data platform that fits the chosen model from the outset: Data Warehouse, Data Lake or Lakehouse.

These are the advantages:

  • Clear target architecture for each data management model
  • Cleanly integrated data pipelines and platform structures
  • Governance, quality and security considered from the outset
Find out more about the offer

Modernization & migration

Future-proof development of existing data storage solutions

Together, we analyze the architecture, maturity level and potential and define a clear path for modernization or transformation towards Lakehouse.

These are the advantages:

  • Structured evaluation of your existing architecture
  • Clear modernization or migration roadmap
  • Preparation for new analytics, AI and governance requirements
Find out more about the offer

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Offer: Modernization & Migration

Future-proof development of existing data storage solutions

This offer is aimed at companies with existing data warehouses or data lakes that want to modernize, expand or transform their architecture in the direction of a lakehouse.

Typical initial situations:

  • Traditional data warehouse reaches its limits with AI or streaming use cases
  • Data lake without clear governance or business usability
  • High operating costs, long development cycles or growing data silos

Result: A future-proof data storage and architecturesolutionthat protects existing investments while enabling new use cases.

Start architecture check and migration strategy

Our service modules

Architecture and maturity analysis

Evaluation of the existing platform in the context of modern requirements

Migration and modernization strategy

Gradual transition (e.g. Warehouse → Lakehouse) without jeopardizing ongoing processes

Technical migration and refactoring

Transfer of data, pipelines and models - including quality and governance assurance

Enablement of the user groups

Customization of access models for specialist departments, data teams and developers

Offer: New setup & implementation

From target image to productive data platform

This offer is aimed at organizations that want to set up a new data warehouse, data lake or data lake house – tailored to business objectives, user groups and future requirements.

The result: A productive, scalable data platform that is geared towards the selected use case right from the start – instead of having to be expensively retrofitted later.

Start setup and introduction now

Our service modules

Requirements analysis and target architecture

Clarification of the functional, technical and regulatory requirements for each DM model (e.g. BI focus, AI use cases, depth of governance)

Architecture and tool concept

Model-based architecture (warehouse, lake or lakehouse) including data flows, metadata, access models and security concept

Implementation and data integration

Development of the platform, connection of relevant data sources and establishment of stable data pipelines

Governance, quality and operations

Introduction of data quality rules, access controls and operating processes

Offer: Requirements analysis & implementation consulting

Clarity before technology decision

This offer is aimed at organizations that want to set up a modern data platform – but are not yet sure whether a data warehouse, data lake or lakehouse is the right approach.

The focus is not on implementation, but on a sound basis for decision-making.

Result: A reliable architecture decision and a clearly defined target image – as a secure basis for investment as a secure basis for investment, tool selection and implementation.

Set the course for your data architecture now

Our service modules

Requirements analysis

Structured collection of functional, technical and regulatory requirements (e.g. BI reporting, AI use cases, self-service, governance, performance).

Data concept evaluation and decision-making framework

Systematic classification of data warehouses, data lakes and lakehouses according to your specific objectives - technology-neutral and vendor-neutral.

Target image and architectural guidelines

Definition of a viable target architecture with clear principles for data flows, role models, security and scalability.

Implementation strategy and roadmap

Derivation of a realistic implementation strategy with prioritized use cases and gradual expansion.