2025-09-14 –, Ballroom 2
Every software system can benefit from being "faster".
But what does “faster” mean, exactly? How do we get to “faster”? If everyone is trying to build performant systems, then why are so many of them so slow?
Let's talk about the fundamentals of performance engineering, and how to use them design and build systems that feel "faster" - even when that's not quite what you thought.
This talk will examine the fundamentals of performance engineering and optimisation:
• What do we mean by “faster”, or “performant”? Why do they mean different things to different people?
• What core trade-offs do we make (consciously or unconsciously) when designing and optimising systems? Why do systems feel slow? Are the engineers just bad, or are there potentially good reasons?
• How do we tell what’s important? How can we tell when we’ve done enough? What are the conversations we can have as a team at the start of a project that will save us pain down the line?
• Which core principles of computing do you need to understand to write fast and efficient code?
• How can we access those concepts from Python?
We’ll use Python-based examples to demonstrate these concepts, and you’ll also learn about some of the improvements that have been made in recent PEPs and Python releases, which make it easier than ever to write performant code.
While this talk is primarily drawn from experience building data processing pipelines, the concepts and principles of performance are universally applicable across the entire Python and software development ecosystem. You should attend this talk if you’re interested in designing and building performant software and systems, and it will contain content relevant to individual contributors of all levels.
You’ll leave this talk with an improved way to discuss performance with your fellow engineers, product managers, project teams, and management. Not only that, but you’ll learn concrete techniques for making your code run faster and more efficiently, regardless of what that means for your team.
Nic is a Senior Performance Engineer in Snowflake's Applied Performance Group, working with product engineering teams to identify and implement performance improvements to real-world customer use cases. He specialises in Snowpark, which brings Python and other non-SQL languages to the Snowflake AI Data Cloud.
Prior to his work with Snowflake's engineering team, he has over 10 years of experience in data engineering and analytics, primarily using Python and SQL.
Nic was the Sponsorships Lead for PyCon AU '23 and '24, and this year is co-lead of the Data & AI Specialist Track. You might know him as "the jacket".