Showing 7 out of 7 results


What are Micro-Frontends and How to Use Them

Not sure where micro-frontends would fit in your current architecture? Luca Mezzalira, author of “Building Micro-Frontends,” speaks with Lucas Dohmen, a senior consultant from INNOQ, to share what problems you can solve with micro-frontends and where they can fit within your distributed systems.

March 15, 2022

Distributed Data Stores on Kubernetes

Are containers good for running anything other than stateless ephemeral services? Let’s explore why and how to run a distributed data store like Cassandra on Kubernetes. This approach can be beneficial for development and testing, as cluster creation, destruction, and set up take minutes instead of hours or days. In this talk, we will explore Kubernetes features, such as Stateful Sets to help work with a distributed data store, Jobs to perform data population, and others, all based on Azure Container Service (ACS/AKS). You’ll leave able to create a Container Service cluster in Azure, deploy a Cassandra Stateful Set, and populate it with data using Jobs.


Arrested DevOps Live!

Arrested DevOps is the podcast where we help you achieve understanding, develop good practices, and operate your team and organization for maximum devops awesomeness. Listen to previous Arrested DevOps podcasts (including some from GOTO Chicago 2017) here: [arresteddevops.com](https://www.arresteddevops.com/)


Practicalities of Productionizing Distributed Systems, 2018

Discussing the latest in consensus protocols and container orchestration is fun, but how do we get those things into production? This talk looks at tactics and strategy for productionizing distributed systems today and a little bit of what’s coming in the future. A version of this talk was given in 2013, based on partially on Notes on Distributed Systems for Young Bloods, and this one continues that talk as a recurring series.


Building Distributed Systems with Kubernetes

Kubernetes has taken the container orchestration space by storm, but it can also be used to create higher-level application abstractions that allow you to build your own distributed systems from scratch. We’ll take a look at some of the patterns and tools available for creating these abstractions using Kubernetes, including Custom Resource Definitions and the Operator pattern. You’ll leave understanding how CRDs and the Operator pattern work, and how to create your own application abstractions using these techniques.


Designing Features for Mature Systems: Lessons Learned from Manta

Designing a distributed system (or any system) effectively requires careful design decisions based on tradeoffs within a set of constraints. Making changes to a mature system, whether by adding a new feature or just improving it, constrains the problem further: Changes should not violate existing invariant of the system, and they should work harmoniously in the context of tradeoffs made for the system long ago. This talk will explore a case study of expanding a distributed system: designing a multipart upload API for Manta, Joyent’s highly-scalable distributed object store. We will talk about the design goals of Manta, then discuss the goals of the new API and why users wanted it. From there, we will design the feature from the ground up, discussing how to achieve the goals of the API -- including idempotent distributed commit and abort operations and performance goals for other operations -- within Manta’s established invariants and design goals. Distributed systems can be especially difficult to reason about -- especially when making changes to them -- and the lessons learned from adding a multipart upload API to Manta resonate with me for all systems I work on.


The Sparking Solution That Scales to IoT

<p>This talk will then be code and demonstration focused with&nbsp;a demonstration of spinning up an entire infrastructure on Apache Mesos. Several code&nbsp;examples will show off Spark coding techniques with AKKA, Spark streaming and SparkSQL.</p> <p>&nbsp;</p> <p>The world of is seeing a growing intersection between large scale big data analytics and the Internet of Things (IoT) resulting in:</p> <ol> <li>Architecture such as the Lamdba architecture,</li> <li>The need to dynamical scale with infrastructure such as Apache Mesos and</li> <li>The need for stream processing.</li> </ol> <p>At the center of solving this challenge is Spark and Scala. We will start out with a walk&nbsp;through of the Lamdba architecture and each of it's components (including AKKA, Kafka,&nbsp;Spark Stream and Cassandra). Attendees will leave with a full understanding of to scale Scala to the IoTs.</p>