solutions.

data automation.

Traditional data management and warehouse initiatives have long been time-consuming and resource-intensive, burdened by manual processes, fragmented systems, and complex workflows. However, an automation-first approach transforms this landscape by applying advanced automation technologies, AI-driven orchestration, and intelligent process design to every stage of the data lifecycle.

What is Data Warehouse Automation and What Does It Mean for Your Analytics Team?

In simple terms, data warehouse automation is the process of automating data warehouse design, development, deployment, and maintenance. This process replaces traditional manual methods with automated ones, speeding up the entire data warehousing process and eliminating manual coding.

Courses

Dimensional Modeling: The Kimball Approach

Don't miss the opportunity to learn directly from Joy Mundy, co-author with Ralph Kimball and other members of Kimball Group, of many of the popular “Toolkit” books. Learn techniques for developing your dimensional model, from the basics to the most advanced practices.

Template Management in Data Warehouse Automation

When building and maintaining a modern data warehouse, automation is key to keeping pace with growing data demands. But automation alone isn’t enough — the way you design, manage, and evolve your templates can make or break your efficiency.

Using Data Automation to Migrate Your Data Warehouse to the Cloud

Cloud-native data architectures are rapidly becoming the standard, but for many organizations, legacy data warehouses remain the bottleneck. Traditional migration approaches are often slow, brittle, and manually intensive - particularly when porting complex ETL jobs and schema logic. This is where Data Warehouse Automation (DWA) becomes a game-changer. If you're a data engineer tasked with modernizing infrastructure, here’s what you need to know about automating your DW migration.

Courses

Modern Data Architecture: Building a Foundation for the Data Driven Enterprise

Learn how a modern data architecture allows you to use a data catalog, data fabric and data observability to build resilient DataOps pipelines to create a data mesh of reusable data products published in a data marketplace.

Quest for Knowledge welcomes WhereScape into its modern data architecture vision

Since 1999, Quest for Knowledge has been dedicated to helping organizations maximize the return on their data. We were among the first in Europe to collaborate with thought leaders like Ralph Kimball, bringing innovative practices to the Benelux and Sweden. More than two decades later, our mindset remains the same: relentlessly pursuing the best methods and solutions in analytics.

This commitment has led us to master and implement modern data and analytics architectures that meet the highest standards of performance and agility. Our new partnership with WhereScape is the next step in that journey — enabling us to deliver fast, reliable, and scalable data automation solutions for data warehouses of every kind.

Beyond ETL and ELT: The Broader Power of Data Warehouse Automation

Although ELT is a more advanced approach compared to ETL, both data movement solutions only address a limited portion of the data warehousing lifecycle. As a result, organizations must depend on various disparate tools to support the numerous other aspects of designing, developing, deploying, documenting, and operating their data warehouses and other data infrastructure.

Talk to a Solution Architect

Book your free 30-minute call with a Solution Architect to discuss how data warehouse automation can streamline every stage of your lifecycle - so you can deliver projects faster, with greater confidence.