Modern Data Product Development Training in Copenhagen, Denmark

This training provides a comprehensive understanding of modern data product development, treating data as a product rather than a byproduct. Participants will learn to apply product thinking to data, design data products that are discoverable, addressable, and trustworthy, and implement the technical and organizational practices needed to deliver high-quality data products at scale. The course covers data mesh principles, data contracts, data observability, and the operationalization of data products within an enterprise context.

Introduction to Data Product Thinking

• From data pipeline to data product: the paradigm shift
• Core characteristics of a data product: discoverable, addressable, trustworthy, interoperable, secure
• Business value and stakeholder alignment
• Data product vs. data asset vs. data service

Hands-on exercises

Data Mesh and Domain Ownership

• Principles of data mesh: domain ownership, data as a product, self-serve platform, federated governance
• Identifying data domains and domain boundaries
• Role of domain teams in data product development
• Governance across domains without central bottlenecks

Hands-on exercises

Data Contract Design and Implementation

• What is a data contract: schema, semantics, SLAs, quality
• Writing and versioning data contracts
• Contract enforcement and validation tools
• Integrating contracts into CI/CD pipelines

Hands-on exercises

Data Product Architecture and Components

• Logical architecture: storage, compute, serving layer
• Choosing the right data format (Parquet, Avro, Iceberg)
• Data product APIs (REST, gRPC, event streams)
• Self-serve data infrastructure and platform capabilities

Hands-on exercises

Data Quality and Observability for Data Products

• Defining data quality dimensions: accuracy, completeness, timeliness, consistency
• Implementing data quality checks and monitoring
• Observability tools: logs, metrics, traces for data pipelines
• Alerting and incident response for data product failures

Hands-on exercises

Data Product Lifecycle Management

• Lifecycle stages: design, develop, deploy, operate, retire
• Versioning and backward compatibility strategies
• Deprecation policies and migration plans
• Data product catalogs and discovery

Hands-on exercises

Data Sharing and Federation

• Data product consumption patterns: APIs, events, data sharing
• Cross-domain data product composition
• Security and access control for data products
• Cost allocation and chargeback models

Hands-on exercises

Operationalizing Data Products at Scale

• Automation of data product deployment and testing
• Monitoring and alerting for production data products
• Scaling data product teams and practices
• Real-world case studies and lessons learned

Hands-on exercises