Data Engineering Consulting

Data Engineering establishes the technical foundation – Data Science builds on it. Without robust Data pipelines and trustworthy, consistent Data, there is no machine learning, computer vision, natural language processing (NLP), or any other AI solution.

Data Engineering and Consulting are essential in all industries – e.g., finance, retail, manufacturing, telecommunications, and logistics. In today's data-driven world, no company can gain a competitive advantage without Data Engineering.

Yes. Smaller companies, in particular, benefit greatly from lean, well-planned Data infrastructures. We provide scalable and technology-independent consulting.

A Data Maturity Assessment evaluates the maturity of your Data landscape. It starts with an analysis of the status quo – Data quality, processes, infrastructure, competencies. This is followed by classification in a maturity model to clearly identify strengths and weaknesses. Finally, you receive concrete recommendations for action: Data Engineering Consulting provides the strategic roadmap, and Data Engineering implements the technical measures.

In Data Engineering Consulting, two methods are distinguished for integrating Data from various sources: ETL and ELT. The main difference lies in the timing of the transformation. In ETL (Extract, Transform, Load), Data is first extracted, then transformed, and finally loaded into a target system. In ELT (Extract, Load, Transform), Data is first extracted and loaded, and the transformation occurs only in the target system.

In Data Engineering Consulting, three hosting options for IT infrastructure and Data are distinguished:

On-premise: Your IT infrastructure and Data are entirely within your own Data center. You have maximum control and security – but are also responsible for maintenance, scaling, and hardware costs.

Cloud-native: Applications are developed and operated directly for the cloud. You benefit from automatic scaling, high reliability, and rapid innovation cycles – without owning hardware.

Hybrid: A combination of your own infrastructure and cloud services. Sensitive Data or critical systems remain on-site, while other applications run flexibly in the cloud. This combines security and flexibility.

A central storage location for large amounts of raw, unstructured, or structured Data. Ideal for keeping Data flexible for future analyses or AI applications.

A central repository for structured, cleaned, and prepared Data. Optimized for quick queries, reports, and business intelligence analyses.

A combination of Data Lake and Data Warehouse: Stores raw and structured Data, enabling fast analyses and BI evaluations – without duplicate Data storage.

A smaller, topic-specific subset of a Data Warehouse, tailored specifically to the needs of a business unit, e.g., sales or marketing.

An organizational approach where Data is managed decentrally by the respective business units – with shared standards for quality, security, and access.

An architecture that makes Data from various sources – whether cloud, on-premise, or hybrid – accessible, integrable, and manageable via a unified platform.