Data Warehouse Automation

This complimentary half-day learning lab explores how Data Warehouse Automation (DWA) and metadata-driven automation tools help simplify and accelerate the development, modernization, and management of data warehouses, data marts, and data pipelines.

description.

Building and modernizing data pipelines and analytical platforms is often a complex, resource-intensive, and continuously evolving challenge. Organizations must coordinate many interconnected activities, including requirements analysis, source system discovery, metadata management, source-to-target mapping, data ingestion, transformation logic, ETL/ELT pipeline development, orchestration, testing, scheduling, documentation, governance, and operational support. Even after successful deployment, evolving business demands, new data products, and cloud modernization initiatives can quickly expose the limitations of traditional development approaches.

This learning lab explores how metadata-driven Data Warehouse Automation tools help organizations accelerate the design, development, extension, and modernization of data warehouses, data marts, and data pipelines - both on-premises and in the cloud. Learn how automated approaches streamline large parts of the end-to-end lifecycle, from source discovery and schema generation to testing, deployment, governance, and operational management.

To demonstrate these concepts in practice, the learning lab includes two live demonstrations of leading automation technologies:

  • dbt - showcasing analytics engineering, transformation automation, testing, lineage, documentation, and CI/CD-driven warehouse development
  • WhereScape - demonstrating metadata-driven automation for rapid warehouse and Data Vault generation, orchestration, and operational management


Through practical demonstrations, this session showcases Data Warehouse Automation in action and highlights how automation can significantly reduce development effort, accelerate delivery cycles, simplify change management, and improve agility across modern data and analytics platforms.

Why attend

Attendees will learn:

  • Core concepts, principles, and best practices of Data Warehouse Automation (DWA)
  • The current state and evolution of DWA technologies and platforms
  • How metadata-driven automation accelerates the design, development, and evolution of data warehouses, data marts and data pipelines
  • Methods for rapidly generating schemas, ETL/ELT processes, and data engineering pipelines
  • How automation supports modern data modelling techniques and data mesh implementations
  • Approaches for simplifying cloud migration and modernization initiatives using DWA
  • How tools like dbt and WhereScape enable rapid development and operational efficiency
  • Common implementation pitfalls, lessons learned, and best practices for successful adoption

 

Who should attend

This learning lab is exclusively intended for data and analytics professionals working at end-user organizations. It is designed for:

  • BI and analytics program and project managers
  • Data warehouse and data platform architects
  • Data integration architects, designers, and developers
  • Data engineers and ETL/ELT developers
  • Data warehouse operations, maintenance, and support personnel
  • Cloud data platform teams and modernization leaders
  • Enterprise and technology architects involved in data strategy and governance

 

Prerequisites

A basic understanding of data management, metadata, data warehousing, data cleansing, and data integration concepts is recommended.

instructor.

Frank Martens

Frank Martens is a Senior Consultant Data & Analytics at Quest for Knowledge, specializing in Data Management, Business Intelligence (BI), and Process Mining. With over 15 years of experience, he designs and implements BI and analytics solutions with a strong sense of enthusiasm, combining traditional BI frameworks with modern data technologies.
 
Frank leads the data automation initiative within Quest for Knowledge, driving innovation and efficiency in data and analytical processes for clients. He has extensive technical expertise in ETL/ELT, Data Warehousing, Data Quality and Profiling and Data Governance. His skillset also includes data visualization, dashboarding, reporting, self-service BI, and defining and tracking Key Performance Indicators (KPIs).
 
Beyond the technical domain, Frank has significant experience in information analysis, designing data architectures, and deploying end-user applications. He has worked in agile/scrum teams in various roles, including product owner and developer. Additionally, he is a certified and seasoned trainer in SAP Data management & Analytical tools.

dates & price.

Course

Delivery Method

Dates

Location

Price

Data Warehouse Automation
Learning Lab
4 hours
17 Sep - 17 Sep '26
Show Class Times
12:30 - 16:30 (CET)
Breda
Show Address
quest for knowledge
Gravinninen van Nassauboulevard 81
4811 BN Breda, Netherlands
EUR
0

pricing.

This learning lab is offered free of charge and is exclusively available to professionals working at end-user organizations in the previously listed roles. As the number of seats is limited, registration is restricted to a maximum of two attendees per company. All registrations will be reviewed and verified before confirmation is provided.

If attendance is no longer possible, cancellation is requested at least 48 hours in advance. A no-show fee of EUR 50 (excluding VAT) will apply to confirmed participants who do not attend and do not cancel in time. The proceeds will be donated to a charitable organization. This policy helps ensure that available seats can be offered to other interested participants.