A story of how our infrastructure evolved over time to accommodate an increasing number of users - from on-premise to cloud and back down.
How does one make an infrastructure to handle more than a couple of users?
How do you go from 100 to 1000 to 100,000 to tens of millions?
What happens when due to popular demand hundreds of thousands of users hit your servers at the same time?
I'll tell you a story of how a small team of people managed to move software and services from one server to two, and then to dozens on cloud and then back to on-premise. What we encountered on the way, where we failed, and how we solved it.
Josip has been involved with computers for the better part of his life. Started with web development back in high school. Since then he's moved to backend and DevOps. Loves security stuff and is obsessed with optimising everything. Works in Zagreb as CTO @ Sofascore
There are too many possibilities for base images in cloud environments and not enough time to validate which ones fulfill all of my requirements. Worse yet, I know that I have policies set by my security team that my infrastructure will be beholden to. So now what? I built an open source policy engine and image generator to implement directly into my build pipelines. I can start with any base image, apply my security policies that are verified by my security team, and now have a baseline that I can rapidly scale my infrastructure from.
Dedicated to optimization and designing effective workflows for continuous integration and continuous delivery, Kendall understands the melding of operations and development to quickly deliver code to customers. Has experience with the Cloud and monitoring processes as well as DevOps... Read More →
Mike Simon, Splunk, Staff Developer Evangelist - Observability
In cloud-native environments, embedding observability as part of your infrastructure-as-code strategy is no longer optional—it’s essential. This session explores the value and necessity of adopting an observability-inclusive as-code approach, allowing teams to automate monitoring, reduce blind spots, and accelerate incident response. We’ll dive into the opportunities this brings for enhancing visibility, ensuring system reliability, and empowering teams to take proactive, data-driven actions without manual overhead.
Mike Simon is a Staff Developer Evangelist at Splunk, where he has been part of the team for two years. In his tenure at Splunk, Mike has held roles as an Observability Strategist and Technical Architect.Before joining Splunk, Mike built a distinguished 16-year career as an Observability... Read More →
In this talk, I focus on addressing the high latency issues associated with Cloud storage in data processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of object storage latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing.
We will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of Cloud storage latency on data processing. By the end of the talk, attendees will have gained insights into how to optimize their data processing systems for reduced latency and improved cost-efficiency.
Rayees Pasha is the Head of Product at RisingWave Labs, a startup pioneering the development of Streaming Database. Rayees is responsible for all areas of Product management and Marketing. His expertise is in the areas of data management and big data analytics. He has held product... Read More →
