8 posts · 3 companies · newest first
Amazon S3 Tables now offer complete support for the Apache Iceberg V3 spec. Upgrade existing V2 tables or create new V3 tables with built-in compaction, maintenance, and engine compatibility.
Amazon S3 Vectors now supports metadata pre filtering, delivering up to 5x higher recall on filtered searches. Filters evaluate before similarity search so scoped queries return more relevant results. Ideal for RAG, agentic apps, and document search.
By turning compaction into a layered, adaptive pipeline and strengthening our monitoring and controls, we made Magic Pocket more resilient to workload changes.
You may be new to this series; and if so welcome! If so, I encourage you to start at the beginning of our datastore journey and see the blog post “Unlocking Efficiency: A New Era for Datastore Provisioning”.Already up to date in our series? MAGICAL — then let’s continue with a quick re-cap.Where are we?We have introduced you to a multitude of aspects all pertaining to how we make the provisioning and utilisation of datastores quick, no-fuss and simple — as simple as clicking your fingers or making a wish.By continuing our theme of MAGIC, we enabled engineers at Zendesk to:Make a wish (detail…
In the fast-paced world of engineering, the dream of easy infrastructure management and provisioning is a common aspiration. At Zendesk, this sentiment resonates deeply among our engineers. When we talk about infrastructure, we refer to a wide range of tools such as MySQL, S3, DynamoDB, Kafka topics, compute resources, network and routing configurations, security groups, secrets, credentials, configuration settings, dashboards, monitors, and log management.Challenges with self-service provisioningIn our recent blog post, Unlocking Efficiency: A New Era for Datastore Provisioning, we…
Are you ready for more self-service datastore adventures? If you haven’t already, have a look at our previous entries in this series:Unlocking Efficiency: A New Era for Datastore ProvisioningSimplifying Datastore Provisioning with Kubernetes OperatorsResolving Incidents With The Remote Incident ConsoleThey’re a fun read.The story so farLast time, in Simplifying Datastore Provisioning with Kubernetes Operators, we talked about making datastores easy to provision by just writing a few lines of YAML in a file called service.yml, like this:version: 1.0name: "Terrific Tents"description:…
Supporting developers to debug and resolve issues with datastores in the Self-Service ecosystem.Welcome to the third blog post of our Self-Service Datastore series, where we share our journey towards creating a more efficient and reliable way to manage datastores at Zendesk.Previous blog posts:Unlocking Efficiency: A New Era for Datastore ProvisioningSimplifying Datastore Provisioning with Kubernetes OperatorsWe need reliable, fast, and compliant self-serve methods to provision datastores. Furthermore, we need to ways to access those datastores from applications; otherwise, they won’t serve…
IntroductionWelcome to the second blog post of our Self-Service Datastore series, where we share our journey towards creating a more efficient and reliable way to manage datastores at Zendesk. In today’s dynamic application development landscape, the ability to swiftly provision datastores is crucial for maintaining agility and delivering exceptional user experiences.Provisioning encompasses all steps involved in requesting a datastore: configuring it to meet company standards, ensuring security and compliance, and managing access credentials. In this article, we’ll explore how we have…