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Running Database-as-a-Service (DBaaS) in the cloud is a common practice for organizations, and more are seeking to offer DBaaS on Kubernetes. Benefits include cost efficiencies, as well as providing a faster, more scalable development environment. While it has many benefits, managing a DBaaS on Kubernetes can be challenging. In this panel, database experts from the Data on Kubernetes Community will discuss how to get started with Kubernetes and operators to run DBaaS, storage and security requirements, common patterns for deployment and Day 2 operations, how to leverage AI for DBaaS, and pitfalls to avoid. They will also share real world experiences from users running DBaaS on Kubernetes.
Melissa Logan has worked in tech for 24 years and is currently director of the Data on Kubernetes and Data Mesh Learning communities, and founder of Constantia.io - a tech community and communications company. Constantia works with data and open source companies to provide marketing... Read More →
Gabriele, a co-founder of 2ndQuadrant and open-source advocate, has been instrumental in PostgreSQL's global growth. Focused on enhancing business continuity for large-scale databases, he has championed stateful workloads in cloud-native environments since 2019. As a co-founder and... Read More →
Deepthi is the Technical lead for Vitess, a CNCF graduated open source project. She also leads the Vitess engineering team at PlanetScale which offers a database service built on Vitess. She brings over 20 years of experience building scalable systems to this role. She enjoys speaking... Read More →
Sergey is a passionate technology “driver”. After graduation worked in various fields: internet service provider, financial sector and M&A business. Main focal points were infrastructure and products around it. At Percona as a Group Product Manager drives forward Kubernetes and... Read More →
This isn't your typical technical talk. We'll take you on a step-by-step adventure, starting from a humble single database in a container and adding components one by one, just like we did. You'll witness firsthand how we tackled real-world challenges, from storage and scaling to monitoring and UI design, to create an Open Source Cloud Native database platform. You'll walk away with a deep understanding of how Kubernetes can be used to orchestrate complex and stateful applications (like databases clusters). Join us and discover how you can break free from vendor lock-in, save costs, and build a database that's truly yours. This is your chance to learn from our triumphs and tribulations, and be inspired to create your own open source success story.
Sergey is a passionate technology “driver”. After graduation worked in various fields: internet service provider, financial sector and M&A business. Main focal points were infrastructure and products around it. At Percona as a Group Product Manager drives forward Kubernetes and... Read More →
As deep learning continues to evolve, the demand for handling petascale datasets efficiently becomes paramount. Current cloud storage solutions often struggle with the speed (throughput) and cost-effectiveness required for these massive datasets, particularly due to the random access needs of machine learning workloads. This talk introduces AIStore (AIS) as a fast-tier storage solution designed to overcome these challenges by offering a fast, scalable, cost-effective tier for deep learning data. AIS features linear scalability with each added storage node - in fact, with each added drive. In this presentation, we will explore the architecture and benefits of AIStore, focusing on its linear scalability and high performance. This session will feature detailed benchmarks and use cases comparing the performance of accessing cloud datasets with and without AIStore, highlighting AIS's ability to deliver high per-GPU throughput and stable latencies.
Abhishek Gaikwad is a Software Engineer at NVIDIA with a Master of Science degree in Computer Science from San Jose State University. As a key developer and maintainer of AIStore, Abhishek has played a crucial role in its design, development, and management. His contributions include... Read More →
The presentation will focus on - * The architecture of the data streaming platform which we built in Palo Alto Networks using open source tools like Strimzi, Kafka, Kafka Connect, Confluent Community licensed tools like Schema Registry and KSqlDB on K8s for supporting corp risk intelligence, health and compliance. * Application of core software engineering principles in architecting open source data platforms and its benefits * Some drawbacks which were identified in the data platform solutions and how we overcame those.
Principal Software Engineer, Palo Alto Networks Inc
Priyanka J. Naik is at Palo Alto Networks Inc. where she works on secure networking software. In her career of 17 years, she has worked in Citrix Systems, Appfolio, with projects and work ranging on products like GoToMeeting, GoToTraining, GoToWebinar, and on data platforms. Interests... Read More →
As generative AI revolutionizes industries, reliance on vector databases - crucial for managing and querying high-dimensional data - has skyrocketed. These databases are often deployed on Kubernetes for its scalability and orchestration capabilities. However, ensuring robust backup and disaster recovery for these stateful applications presents unique challenges. Join Pavan and Shwetha as they discuss the critical need for an effective data protection strategy for vector databases in Kubernetes environments, emphasizing its importance in maintaining data integrity and availability. Attendees will learn about the growing significance of vector databases driven by AI applications and the specific considerations for their reliable deployment and management in cloud-native settings. Through a practical demonstration, this session will introduce Kanister, a CNCF Sandbox project, showcasing how it simplifies the complex process of backing up and recovering vector databases on Kubernetes.
Pavan joined Kasten by Veeam in March 2018, where he leads the open-source efforts and manages a team of cloud-native engineers developing innovative solutions for data protection in Kubernetes. He has previously worked in data protection and networking at NetApp and Aryaka. Pavan... Read More →
Shwetha Subramanian is a 2+ year experienced software professional, armed with a Master’s in Computer Science (Machine Learning track) from Columbia University, currently working as an SWE in the Kasten team at Veeam. An inherently curious individual, she is on a journey of learning... Read More →
Did you know that major Cloud providers cap the amount of volumes you could attach to a node? You may be tempted to use a ConfigMap or a Secret instead; however, did you know that Kubernetes caps the size of all resources to 1 MiB? What if you need arbitrarily large data? After all, reaching these limits may effectively render your application completely useless and for commonly used operators, constraints are exacerbated. In this talk, we cover all built-in storage mechanisms and their pitfalls. Not only can your data be large, but what about auto-scaled workloads which access the same data? We explore patterns that we at Google explored while working on our open source Prometheus operator, including: variable expansion, compression, sharding, projected volumes and dynamically mounting resources. We discuss how to shape the user configuration surface and how to make your data available at scale. Especially a must-see for anyone distributing configurations in their operator!
Daniel Hrabovcak is a software engineer at Google working within Cloud Monitoring to build Google Cloud’s Managed Service for Prometheus. Daniel’s love of coding has lasted a decade, touching on open-source game development and a previous career in full stack development, giving... Read More →
