After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Databricks Genie answers data questions in natural language, but the quality of those answers depends on how the space is configured. In this case study, you take a Genie space through its full lifecycle. As a data analyst at Wanderbricks, you start from a minimal setup and build it out: defining column descriptions and synonyms, adding instructions, connecting tables, and saving example and parameterized queries. You then move into operations, using monitoring to surface weak answers and benchmark questions to measure quality. By the end, you can take a Genie space from setup to production-ready.
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Start Course for FreeWhat you'll learn
- Differentiate how column descriptions, synonyms, instructions, and entity matching influence the SQL that Genie generates.
- Determine when to connect tables and define relationships to support queries that span multiple tables.
- Define shared business metrics using example queries and reusable parameterized queries.
- Detect weak responses through Genie monitoring and identify the appropriate corrective action for each.
- Evaluate the quality of a Genie space using a benchmark suite of questions with known answers.
Prerequisites
Curriculum
Course outline
1
Launching the Sales Pilot
In this chapter, you'll set up a Genie space and make it answer reliably by adding column descriptions, synonyms, default instructions, and entity matching.
2
Department-Wide Adoption
Next, you'll expand the space across multiple tables with relationships, and lock shared formulas using SQL examples and a parameterized Trusted Asset.
3
Enterprise Reliability
In this final chapter, learn to harden a Genie space using monitoring, targeted fixes, and a benchmark suite so its answers stay reliable at scale.
Case Study: Sales Analytics with Databricks Genie
Course
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