STACKIT Dremio: Hands-On Lab

Course Overview

This is a fully hands-on lab, not a conceptual course: each of its four modules is a self-contained lab that builds directly on the last, taking you from an empty STACKIT project to a working lakehouse. You’ll provision a STACKIT Dremio instance, connect Object Storage, upload sample CSV and Parquet files, and convert them into Iceberg tables you can query with SQL — the same foundational steps you’d follow in a real deployment.

From there you build out what makes the lakehouse useful in practice: a semantic layer with business-friendly virtual datasets, Raw and Aggregation Reflections to compare accelerated versus unaccelerated query performance side by side, and in the final lab, a second federated data source (PostgreSQL), row-level security, data masking, and Nessie branching so you can experiment on your data without risk. It’s the direct hands-on companion to the Dremio DeepDive and Getting Started courses — where those explain the concepts, this is where you build them yourself.

What You’ll Learn

• Provision a STACKIT Dremio instance and connect Object Storage as a data source
• Create Iceberg tables from uploaded data and query them with SQL
• Build a semantic layer and measure the performance impact of Raw and Aggregation Reflections
• Run federated queries across Object Storage and PostgreSQL with row-level security and data masking
• Use Iceberg time travel and Nessie branching to query history and experiment safely

Modules

• Lab Setup and Instance Creation
• Storage, Iceberg Tables, and Semantic Layer
• Reflections and Federated Queries
• Security, Time Travel, and Nessie Branching

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