Data Management Services

We design and implement scalable data architectures that comprehensibly map data from its origin to analytical and AI-supported use.

End-to-end architectures for analytics, data science and AI

We support you throughout the entire life cycle of your data platform – from the strategic architecture decision to stable implementation during operation. We consistently rely on open source technologies to ensure transparency, expandability and long-term independence.

Modern data landscapes are not created by isolated tools, but by the interaction of specialized layers. Quality assurance, streaming, governance, lineage, analytical storage and AI workloads are designed by us as an integrated overall system and technically dovetailed.

On this basis, we develop an end-to-end data management architecture that consistently maps data flows from their origin to analytical and AI-supported use – open, scalable and adaptable to new requirements.

Beyond the architecture and conception phases, we accompany the implementation through to stable productive use. We implement architectures, data pipelines and operating models ready for production and ensure that they function reliably and can be further developed in everyday use.

From the data source to the AI agent

All components are part of a common architecture.

Data integration for mastering heterogeneous data landscapes

The sheer multitude and diversity of data sources in companies often represents major challenges that can only be dealt with through complex integration projects. We deliver customized solutions for the integration of many data sources into complex enterprise architectures, regardless of data type or format.

Data warehouse and lakehouse as the analytical core

Checked and domain-specific harmonized data forms the analytical core of modern data architectures. While data warehouse architectures provide this data in a persistent and structured manner, lakehouse approaches also enable virtual, federated access. The layer is optimized for BI, advanced analytics and machine learning and remains closely interlinked with real-time and operational data flows.

Data streaming as the central operational backbone

Streaming is not the starting point, but can be the connecting backbone of modern data architectures. Events are distributed decoupled and provided in real time for different consumers - from operational services to analytics and AI applications. The focus is on scalability, reliability and low latency.

Data quality as a consistent architectural principle

Data quality is not an isolated discipline, but part of every processing layer. We embed validations, schema checks and quality metrics directly into ingestion, streaming and analysis processes. As a result, data remains consistent, trustworthy and reproducible - even with increasing volumes and speeds.

Data governance as a technically implemented set of rules

We do not describe governance, but implement it using data lineage. Roles, access rights, data protection and compliance requirements apply consistently across all layers of the data architecture. For all data engineers, BI teams and platform managers for whom traceability, maintainability and clean further development are important.

Agentic AI for Data with controlled autonomy

AI agents require qualitatively assured, contextualized and comprehensible data. The combination of ingestion, streaming, data quality, lineage, governance and the Model Context Protocol (MCP) creates a platform on which agents can act independently - within clearly defined guard rails, with controlled autonomy and complete transparency regarding data origin, context and decisions.

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