A white paper for anyone who wants to modernize their data infrastructure and accelerate data-driven decisions with a modern tech stack, data warehouse and data lake

A modern data stack is a modern, mostly cloud-based data architecture that flexibly combines data integration, storage, transformation, analysis, and governance. The aim is to make data available faster, more usable and scalable. This is particularly relevant for companies because data-driven decisions, modern BI applications and AI initiatives are only successful if the underlying data infrastructure is powerful, integrable and future-proof.

A modern data stack comprises the entire data journey: the connection of source systems, central storage in a data warehouse, data lake or data lakehouse, the transformation and modelling of the data, and the use in BI, reporting and AI applications. This architecture is complemented by orchestration, documentation, deployment, governance, and data science and AI components. It is not a single tool that is decisive, but the interplay of the components in a consistent architecture.

The key benefits include scalability, flexibility, faster delivery of data, reduced reliance on monolithic systems, and a better basis for self-service analytics. Companies can use computing power and storage as needed, combine specialized tools via interfaces and make data available significantly faster for operational and strategic decisions. At the same time, a modern data stack creates the conditions for productively placing AI applications on a reliable database.

A data lakehouse is useful when companies want to use structured and unstructured data in a common architecture. It combines the flexibility of a data lake with the analytical capabilities of a data warehouse. This is particularly relevant for AI applications. As soon as content such as PDFs, documents, websites or knowledge bases are to be processed in addition to classic tables, a lakehouse architecture is often the most sensible way to make this data centrally available and usable for analysis and AI.

Typical challenges include the integration of distributed data sources, the selection of appropriate technologies, the development of know-how, ensuring data quality and governance, and organizational change. A modern data stack is not just a technical project. It is also changing processes, responsibilities, and collaboration between business, IT and data controllers. A step-by-step approach is therefore usually successful. Start with a clear use case, create benefits quickly and then expand the architecture in a targeted manner.