What we're about

Kubernetes has won – and is being more and more deployed. Nonetheless, as Kubernetes becomes the defacto standard API for infrastructure and application orchestration, there are some areas that remain unclear and confusing to many in and around the cloud native community.

After discussions with thousands of companies and individuals running data workloads on Kubernetes we’ve come to see that there is a need for a sharing of patterns and concerns about how to build and operate data-centric applications on Kubernetes.

With that in mind, we’re happy to announce a new community – the Data on Kubernetes Community. DoK.community is an openly governed and self-organizing group of curious and experienced operators and engineers concerned with running data-intensive workloads on Kubernetes. DoKC takes inspiration from the CNCF and Apache foundations and aims to be open, vendor-neutral, and extremely inclusive.

Upcoming events (5)

DoK #69 To Certify or Not to Certify, is Kubernetes Certification Worth it?

https://go.dok.community/slack
https://dok.community/

ABSTRACT OF THE TALK

As an engineer, should I consider getting a certification? What makes a certification valuable to me or my employer? How do I pick which one to get? Will these really help me build stateful applications on Kubernetes? In this talk, we will discuss the relative value of certifying on different technologies, with a specific focus on CNCF certifications for administration of k8s and developing Kubernetes-native applications. In this session we will discuss: - The pros and cons of getting certified - Why your current and future employers might care about your certifications - What are other things you can do to make yourself a more attractive candidate in this cloud-native landscape And of course, since Keith is a long-time database geek, we'll talk about how these might help you (or not) build stateful applications on Kubernetes.

BIO

Keith McClellan is the Director of Partner Solutions Engineering at Cockroach Labs. He is responsible for building CockroachDB-based solutions with our largest technology partners, including Kubernetes and the broader open-source ecosystem. He spearheaded Cockroach Labs' Kubernetes operator project, acting as the technical lead on the project and being a primary contributor to making that the best way to run CockroachDB on Kubernetes. Prior to Cockroach Labs, Keith has held technical leadership positions in cloud-native and big data companies including DataStax, Mesosphere (now D2IQ), and Platfora.

DoK Talks #70 - YugabyteDB - Distributed SQL Database on Kubernetes

https://go.dok.community/slack
https://dok.community/

ABSTRACT OF THE TALK

Kubernetes has hit a home run for stateless workloads, but can it do the same for stateful services such as distributed databases? Before we can answer that question, we need to understand the challenges of running stateful workloads on, well anything. In this talk, we will first look at which stateful workloads, specifically databases, are ideal for running inside Kubernetes. Secondly, we will explore the various concerns around running databases in Kubernetes for production environments, such as: - The production-readiness of Kubernetes for stateful workloads in general - The pros and cons of the various deployment architectures - The failure characteristics of a distributed database inside containers In this session, we will demonstrate what Kubernetes brings to the table for stateful workloads and what database servers must provide to fit the Kubernetes model. This talk will also highlight some of the modern databases that take full advantage of Kubernetes and offer a peek into what’s possible if stateful services can meet Kubernetes halfway. We will go into the details of deployment choices, how the different cloud-vendor managed container offerings differ in what they offer, as well as compare performance and failure characteristics of a Kubernetes-based deployment with an equivalent VM-based deployment.

BIO

Amey is a VP of Data Engineering at Yugabyte with a deep passion for Data Analytics and Cloud-Native technologies. In his current role, he collaborates with Fortune 500 enterprises to architect their business applications with scalable microservices and geo-distributed, fault-tolerant data backend using YugabyteDB. Prior to joining Yugabyte, he spent 5 years at Pivotal as Platform Data Architect and has helped enterprise customers across multiple industry verticals to extend their analytical capabilities using Pivotal & OSS Big Data platforms. He is originally from Mumbai, India, and has a Master's degree in Computer Science from the University of Pennsylvania(UPenn), Philadelphia. Twitter: @ameybanarse LinkedIn: linkedin.com/in/ameybanarse/

DoK #71- Introducing Kubestr: A new way to benchmark your kubernetes storage

https://go.dok.community/slack
https://dok.community/

ABSTRACT OF THE TALK

Benchmarking storage is not a new concept, this has been happening on storage for a long time. But have we overlooked the benchmarking capabilities or at least the ease in which to achieve this in a cloud-native, container-based, Kubernetes landscape?

There has been a rise in stateful workloads and support around persistent storage in Kubernetes is improving. Now we can take our traditional workloads such as SQL Server, Oracle and SAP alongside our data stores for microservices with the same storage system for MongoDB, Cassandra, Redis, MySQL and PostgreSQL. With each of these stateful applications having different performance requirements, it becomes necessary to benchmark the storage backing these Persistent volumes.

The CSI (Container Storage Interface) is the standard for creating custom components to work with data storage. This has enabled many more storage vendors to adopt their platforms to the cloud-native approach and offerings.

All of this is great, but how do we ensure that the right datastore is used to achieve the performance required for our microservices running these stateful workloads?

BIO

A community first technologist for Kasten by Veeam Software. Based in the UK with over 16 years of industry experience with a key focus on technologies such as cloud-native, automation & data management.

His role at Kasten is to act as a technical thought leader, community champion and project owner to engage with the community to enable influencers and customers to overcome the challenges of Cloud-Native Data Management and be successful, speaking at events sharing the technical vision and corporate strategy whilst providing ongoing feedback from the field into product management to shape the future success.

KEY TAKE-AWAYS FROM THE TALK

Kubestr can assist here in three ways:

· Identify the various storage options present in a cluster.

· Validate if the storage options are configured correctly.

· Evaluate the storage using common benchmarking tools like FIO.

DoK #72- Highly available, pluggable and long term storage metrics for everyone

https://go.dok.community/slack
https://dok.community/

ABSTRACT OF THE TALK

Prometheus was initially made for short metric retention to answer questions on “what is happening ‘now’”. It is a strong project that solves certain problems really well, but still as a monolith when doing so. Thanos has been made to enable scaling, highly available setups and long term (cheap) storage for Prometheus. Everyone could leverage Thanos for these features. It does not stop there; Thanos has multiple components that could be used for multi-cluster telemetry, remote writes, and multi-tenancy. We want to introduce everyone to Thanos. Explaining the use-cases and how it could benefit your stack now observability becomes such an important factor in tech.

BIO

Wiard van Rij’s main focus is in the field of observability at Fullstaq. As a consultant he is helping people, teams, and organizations with various cloud-native challenges with a strong focus on Kubernetes and Observability. Wiard is a Thanos team member, open source enthusiast and has extra fun with security and hacking.

KEY TAKE-AWAYS FROM THE TALK

Introduction to Prometheus and Thanos
How to extend your stack(s) for highly available and long term metrics
By leveraging the right tools and services, one can have a rich set of features which are also cost effective

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