Modern
Data Platforms

Modern data platforms integrate data from various sources, deliver it reliably, and create a robust foundation for analytics, automation, and artificial intelligence.

We design and modernize data warehouse, data lake, and lakehouse environments – tailored to existing systems, data volumes, usage scenarios, and future requirements.

Challenge

When Legacy Data Environments Reach Their Limits

Many companies work with data platforms that have been expanded over the years. New sources, technologies, and requirements have been added on an ad hoc basis. This results in complex dependencies, long deployment times, and increasing operational overhead.

Typical challenges include:

• isolated data sets and redundant data storage,
• ETL and reporting structures that are difficult to maintain,
• limited scalability and performance,
• long development cycles for new analyses,
• lack of support for real-time data,
• high infrastructure and operating costs,
• unclear responsibilities and quality issues,
• inadequate foundations for advanced analytics and AI.

A modern data platform creates a flexible roadmap without unnecessarily discarding existing investments.

Platform
Models

The Right Architecture for Each Specific Need

Not every company needs the same platform. The key factors are data types, usage, timeliness requirements, governance, and the existing system landscape.

Cloud Data Warehouse

A cloud data warehouse is suitable for structured data, standardized metrics, and high-performance BI and reporting applications. Computing power and storage can be scaled flexibly.

Data Lake

A data lake accommodates large volumes of structured, semi-structured, and unstructured data. It offers high flexibility for data science, machine learning, and exploratory analytics.

Data Lakehouse

A lakehouse combines the openness and scalability of a data lake with the capabilities of traditional data warehouses, such as transactions, metadata management, and controlled access.

Hybrid Data Platform

In many companies, a combination of different platforms makes sense. Cloud and on-premises systems, operational applications, and existing data warehouses are integrated into a coordinated architecture.

Selection

Architectural Decisions with a Long-Term Perspective

The choice of a platform should not be based on a trend or a single vendor. Together, we evaluate which approach makes the most sense from a business, technical, and economic perspective.

Key criteria include:

• Type, volume, and velocity of data,
• Requirements for reporting, analytics, and AI,
• Batch or real-time processing,
• Data protection, security, and regulatory requirements,
• Integration with existing systems,
• Scalability and performance,
• Operating model and available expertise,
• Costs, vendor lock-in, and long-term maintainability.

For larger, decentralized organizations, domain-oriented data products and federated governance can be beneficial. However, a data mesh approach is only suitable if the organizational structure, accountability, and platform maturity align with it.

Modernization

Gradually Enhance Existing Data Platforms

Modernization doesn’t have to start with a complete overhaul. Often, a phased approach is more cost-effective and carries less risk.

Possible measures include:

• Replacing or optimizing existing ETL processes,
• Migrating to cloud or hybrid architectures,
• introducing ELT, streaming, or change data capture,
• consolidating redundant platforms,
• modernizing data models and semantic layers,
• automating testing, deployments, and monitoring,
• improving performance and cost control,
• and gradually introducing data products.

This allows for the resolution of specific bottlenecks in the short term while simultaneously laying a solid foundation for further development.

Our Approach

Business
Value