Four Storage Models, Different Workloads

Today’s data teams are under increasing pressure to support analytics, reporting, AI, and other emerging workloads, while keeping data trusted, governed, and accessible. But as data estates grow, many organisations find themselves building and maintaining separate pipelines for different use cases. The result can be duplicated effort, increased maintenance, and complexity.

It doesn’t have to be that way.

Join Tom Cotterill, Senior Business Intelligence Consultant at Climber UK, to explore how Qlik Talend Cloud can support four different storage models: Data Warehouse Automation, Knowledge Marts, Iceberg, and Mirroring.

We will look beyond the technology to explain the challenge each model is designed to solve, where each fits and why, and how a more flexible approach to data delivery can help reduce unnecessary duplication and complexity.

Whether you’re modernising an existing data platform or looking to simplify how data is delivered across different workloads, you’ll leave with practical guidance to help you make more informed architectural decisions.

Watch now on-demand!

What you’ll learn

During this webinar, you’ll discover:

• How Qlik Talend Cloud can support different storage models and workloads, helping reduce duplicated development and ongoing maintenance.
• Where Data Warehouse Automation, Knowledge Marts, Iceberg and Mirroring each fit, and the workloads and requirements they’re designed to support.
• How automated data integration can help deliver trusted data more efficiently while maintaining governance and consistency.
• How to support different storage requirements while reducing unnecessary pipelines, processes and complexity.

Who should watch?

This webinar is ideal for professionals responsible for designing, managing, or modernising data platforms, including:

• Heads of Data
• Data Architects
• Data Engineering Leads
• Platform Leads
• Data & Analytics Leaders
• Business Intelligence and Data Professionals

Watch on-demand

If you’re looking to reduce duplicated pipeline work and better understand which storage model is right for each workload, join us to get practical guidance you can apply to your own data architecture.