Ryan Boyd
Co-founder at MotherDuck
Ryan Boyd is a Boulder-based software engineer, data + authNZ geek and technology executive. He's currently a co-founder at MotherDuck, where they're making data analytics fun, frictionless and ducking awesome. He previously led developer relations teams at Databricks, Neo4j and Google Cloud. He's the author of O'Reilly's Getting Started with OAuth 2.0. Ryan advises B2B SaaS startups on growth marketing and developer relations as a Partner at Hypergrowth Partners.
All Sessions by Ryan Boyd
Data Infrastructure through the Lens of Scale, Performance and Usability
<span class="etn-schedule-location"> <span class="firstfocus">Data Engineering</span>DE Summit: Silicon Valley engineers and engineering challenges have ruled the data world for the last 20 years. The net result is data infrastructure companies focusing on being the highest scale, fastest systems to process enormous amounts of data– usability be damned. We don’t all have movie libraries the size of Netflix, search indexes the size of Google or social graphs the size of Meta. This talk explores the changes in hardware and mindsets enabling a new breed of software that is optimized for the 95% of us who do not have petabytes to process daily. I worked on Google BigQuery in 2012. At the time, the max size of memory on an EC2 machine was 60.5GB. Today, we have EC2 machines with 25TB of RAM. Our software design for data services, focused on distributed architectures, hasn’t taken into account that massive 400x change in the amount of memory available. At the same time, our laptops have gotten so much more powerful - with 16x the amount of RAM available in today’s Macbook Pro vs the ones offered in 2012. Shouldn’t our data infrastructure be adapted to take advantage of this local compute? What does this change in hardware and software mean for the user experience? Instead of focusing on consensus algorithms for large-scale distributed compute, can our engineers instead focus on making data more accessible, more usable and reduce the time between “problem statement” and “answer?” That’s the dream that I’m exploring and where I want to push our industry over the next 5 years.
Small Data Manifesto: Data infrastructure to build bigger with less
<span class="etn-schedule-location"> <span class="firstfocus">Data Engineering</span> <span class="firstfocus">All Levels</span> </span>Have you been force to write a Spark job to process a few gigabytes of data? Or, worse yet, a few hundred megabytes? Unfortunately, many data engineers and analysts have had that sad task. We’ve been sold the idea that our data is BIIIGGGG and will grow incredibly HUUUUUGEE. Why? Silicon Valley geeks [like myself] told us that over the last 20 years. The reality is though, this hasn’t happened and distributed data processing is often unnecessary and wasteful with the advancement of memory density and CPU performance. Why does this matter? We’re now in a post-ZIRP world where we need to focus on doing more with less. Luckily, the technological advancements in hardware are converging with advancements in highly-efficient data engines (like DuckDB), distributed storage and bandwidth. This talk will explore the simple joys of small data, how more data != better results, how single machines are efficient and powerful and developing locally just works. Technologies discussed: DuckDB, Web Assembly (Wasm), Small Language Models (SLMs), hybrid local <-> cloud compute and more, with analysis based on industry and academic papers. Learning Objectives: Attendees will learn to re-evaluate how they're choosing and deploying data infrastructure to focus on the most appropriate technologies for their problem set. DuckDB, Web Assembly (Wasm),
Making Big Data Feel Small: A Systems Perspective on Modern OLAP at Scale
<span class="etn-schedule-location"> <span class="firstfocus">Data Engineering</span> <span class="secfocus">Beginner-Intermediate</span> </span>This talk will explore a variety of architectures to enable high-scale analytics with an in-process analytics database like DuckDB, while making every day work more delightful. We’ll cover capabilities, ergonomics and scalability of these architectures and how they’re being used in the real world.
Making Big Data Feel Small: A Systems Perspective on Modern OLAP at Scale
<span class="etn-schedule-location"> <span class="firstfocus">Data Engineering</span> <span class="secfocus">Beginner-Intermediate</span> </span>This talk will explore a variety of architectures to enable high-scale analytics with an in-process analytics database like DuckDB, while making every day work more delightful. We’ll cover capabilities, ergonomics and scalability of these architectures and how they’re being used in the real world.
Small Data Manifesto: Data infrastructure to build bigger with less
<span class="etn-schedule-location"> <span class="firstfocus">Data Engineering</span> <span class="firstfocus">All Levels</span> </span>Have you been force to write a Spark job to process a few gigabytes of data? Or, worse yet, a few hundred megabytes? Unfortunately, many data engineers and analysts have had that sad task. We’ve been sold the idea that our data is BIIIGGGG and will grow incredibly HUUUUUGEE. Why? Silicon Valley geeks [like myself] told us that over the last 20 years. The reality is though, this hasn’t happened and distributed data processing is often unnecessary and wasteful with the advancement of memory density and CPU performance. Why does this matter? We’re now in a post-ZIRP world where we need to focus on doing more with less. Luckily, the technological advancements in hardware are converging with advancements in highly-efficient data engines (like DuckDB), distributed storage and bandwidth. This talk will explore the simple joys of small data, how more data != better results, how single machines are efficient and powerful and developing locally just works. Technologies discussed: DuckDB, Web Assembly (Wasm), Small Language Models (SLMs), hybrid local <-> cloud compute and more, with analysis based on industry and academic papers. Learning Objectives: Attendees will learn to re-evaluate how they're choosing and deploying data infrastructure to focus on the most appropriate technologies for their problem set. DuckDB, Web Assembly (Wasm),
Data Infrastructure through the Lens of Scale, Performance and Usability
<span class="etn-schedule-location"> <span class="firstfocus">Data Engineering</span>DE Summit: Silicon Valley engineers and engineering challenges have ruled the data world for the last 20 years. The net result is data infrastructure companies focusing on being the highest scale, fastest systems to process enormous amounts of data– usability be damned. We don’t all have movie libraries the size of Netflix, search indexes the size of Google or social graphs the size of Meta. This talk explores the changes in hardware and mindsets enabling a new breed of software that is optimized for the 95% of us who do not have petabytes to process daily. I worked on Google BigQuery in 2012. At the time, the max size of memory on an EC2 machine was 60.5GB. Today, we have EC2 machines with 25TB of RAM. Our software design for data services, focused on distributed architectures, hasn’t taken into account that massive 400x change in the amount of memory available. At the same time, our laptops have gotten so much more powerful - with 16x the amount of RAM available in today’s Macbook Pro vs the ones offered in 2012. Shouldn’t our data infrastructure be adapted to take advantage of this local compute? What does this change in hardware and software mean for the user experience? Instead of focusing on consensus algorithms for large-scale distributed compute, can our engineers instead focus on making data more accessible, more usable and reduce the time between “problem statement” and “answer?” That’s the dream that I’m exploring and where I want to push our industry over the next 5 years.