Performance optimization, also known as performance tuning, involves making changes to the current state of the semantic model so that it runs more efficiently. Essentially, when your semantic model is optimized, it performs better.

Learning objectives

By the end of this module, you learn how to:

• Review the performance of measures, relationships, and visuals.
• Use variables to improve performance and troubleshooting.
• Improve performance by reducing cardinality levels.
• Optimize DirectQuery models with table level storage.
• Create and manage aggregations.

Next unit: Check your knowledge

What I learn from 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 Today🎙️ I just finished listening to this podcast:

• 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 Data Insights with Marcus Chen
• 𝗘𝗽𝗶𝘀𝗼𝗱𝗲: Optimizing Data Models for Peak Performance

𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Performance optimization isn't just about speed - it's about fundamentally rethinking how data flows through your system. The most impactful changes happen at the source level, where proper indexing and aggregation can reduce processing time by up to 70%. Instead of working with millions of individual records, strategic aggregation lets you work with hundreds while maintaining analytical integrity.

𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: In real-world data environments, performance bottlenecks can cripple decision-making. When dashboards take minutes to load instead of seconds, stakeholders lose trust and engagement drops. By implementing proper aggregation strategies, you not only improve user experience but also reduce infrastructure costs and maintenance overhead.

𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 🧠 I've been guilty of building complex models without considering performance until problems arise. This episode reminded me that optimization isn't something you bolt on at the end - it needs to be part of the initial design philosophy. I'm revisiting our sales analytics platform tomorrow to implement aggregation tables that should cut our refresh times in half.

Follow up To get the full insight, checkout the podcast!

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