Data Engineering & AI Data Readiness
Structured data platforms for analytics, automation and artificial intelligence
Data is today the foundation for business decisions, automated processes and intelligent applications. In many organisations, however, it is scattered across numerous systems, structured differently or only usable to a limited extent. Without structured data provisioning, analytics and AI initiatives often fall short of their potential.
CND systematically prepares your data landscape and creates a stable foundation for data analytics, machine learning and AI platforms. We combine data engineering with infrastructure and operational understanding. We structure data sources, connect heterogeneous systems and build scalable data pipelines that provide information reliably and in a controlled manner. In doing so, our focus is not only on technology but also on data quality, governance, security and stable operation.
The result is a data platform that makes information available in a structured way and supports data-driven innovation.
Our services
As part of our data engineering and data readiness services, we support among other things with:
Data Discovery & Assessment
Identification of relevant data sources in existing IT landscapes
Analysis of data quality, degree of structuring and integration capability
Assessment of risks, redundancies and optimisation potential
Preparation of a prioritised catalogue of measures
Data Integration & Ingestion
Building high-performance data pipelines for batch and streaming processing
Integration of heterogeneous data sources from databases, file systems and cloud platforms
Standardised connection to modern analytics and AI platforms
Integration of interfaces such as S3, REST, NFS, SMB or Kafka
Data Preparation & Governance
Building structured ETL and ELT processes
Classification, normalisation and deduplication of data
Implementation of data quality frameworks
Building metadata structures, versioning and data lineage
Operationalisation for AI & Analytics
Preparation and versioning of training data for machine learning
Building DataOps and MLOps processes
Automated provision of data for analytics and AI workflows
Data Lifecycle & Resiliency
Lifecycle management for data across different storage classes
Archiving, retention and automated data movement
Building resilient data structures with snapshots and immutable storage
Recovery strategies for business-critical data platforms
Your benefit
Data becomes usable: raw data is structured, quality-assured and made usable for analytics as well as AI.
Accelerated AI initiatives: clean data structures enable more efficient development and training of machine learning models.
Better decision bases: structured data provides sound information for management, business units and automated systems.
Scalable data platforms: flexible data architectures grow with new data sources and rising requirements.
Resilient data infrastructure: structured lifecycle strategies and resiliency concepts protect business-critical information.
Digital sovereignty: control over data, data structures and data processes remains with the operator.
Typical use cases
Data engineering services are frequently used for:
- Building modern analytics and data platforms
- Preparing data for AI and machine learning initiatives
- Integration of heterogeneous data sources from various system landscapes
- Improving data quality in existing environments
- Building scalable data pipelines for enterprise data
- Structuring and governance of growing data landscapes
Why CND
CND combines data engineering with infrastructure expertise and operational experience. Our specialists understand not only data structures but also the IT infrastructure beneath them. The result is solutions that are analytically powerful and at the same time stable, secure and operable for years. Our data platforms fit in a structured way into existing IT landscapes and support data-driven innovation over the long term.