Slides of the presentation about Kubernetes practices and learnings at NU.nl.
This presentation was the first of two at the Dutch Kubernetes meetup at the Sanoma Netherlands offices, that took place on Sept. 5th 2019
This is my noob recap of KubeCon 2019, which I transformed into a kubernetes bootcamp. I walked away with a bunch of learnings, so here they are for you :)
London Hashicorp Meetup #22 - Congruent infrastructure @zopa by Ben CoughlanBen Coughlan
Ben Coughlan from Zopa discussed building congruent infrastructure which focuses on immutable infrastructure and avoiding configuration drift. He explained that at Zopa they moved from a monolithic application to microservices running in containers on Kubernetes. Tools used include Terraform for infrastructure provisioning, Vault for secrets, Consul for service discovery, and Prometheus for monitoring. Kafka was deployed using these tools by provisioning servers with Terraform and user data, attaching storage, issuing certificates from Vault, and bootstrapping the Kafka cluster without manual steps. Challenges of the congruent approach include the time needed to build hardened platforms, complexity of adapting some products, cultural shifts, and supporting Windows.
This document summarizes an event-driven architecture presentation using Java. It discusses using Apache Kafka/Amazon Kinesis for messaging, Docker for containerization, Vert.x for reactive applications, Apache Camel/AWS Lambda for integration, and Google Protocol Buffers for data serialization. It covers infrastructure components, software frameworks, local and AWS deployment, and integration testing between Kinesis and Kafka. The presentation provides resources for code samples and Docker images discussed.
This document provides an overview of deploying Ruby on Rails applications. It discusses the evolution of different web servers and technologies used over time, including CGI, Apache with various modules, Lighttpd, and others. It introduces Mongrel as a fast HTTP server library and explains how its use requires clustering to scale Rails applications across multiple processes. The document then covers tools for load balancing and proxying to Mongrel clusters, including Nginx which provides high performance. It also describes an event-driven version of Mongrel called Swiftiply that improves throughput. Overall it presents best practices and optimizations for deploying scalable and high performing Rails applications.
DCSF19 Container Security: Theory & Practice at NetflixDocker, Inc.
Michael Wardrop, Netflix
Usage of containers has undergone rapid growth at Netflix and it is still accelerating. Our container story started organically with developers downloading Docker and using it to improve their developer experience. The first production workloads were simple batch jobs, pioneering micro-services followed, then status as a first class platform running critical workloads.
As the types of workloads changed and their importance increased, the security of our container ecosystem needed to evolve and adapt. This session will cover some security theory, architecture, along with practical considerations, and lessons we learnt along the way.
How DreamHost builds a Public Cloud with OpenStackCarl Perry
This document summarizes DreamHost's presentation on how they built a public cloud using OpenStack. Some key points:
- DreamHost is using OpenStack for compute, storage, and networking in their public cloud offering called DreamCompute.
- For storage, they chose Ceph which provides shared, scalable block and object storage.
- Their network architecture uses 10Gb switches in a spine-leaf topology with logical networking software for tenant isolation.
- Automation is key to managing the cloud infrastructure and providing services to customers.
- DreamHost discussed the considerations and challenges in building the cloud such as scalability, speed, monitoring, security and cost effectiveness.
Apache Kafka's rise in popularity as a streaming platform has demanded a revisit of its traditional at-least-once message delivery semantics.
In this talk, we present the recent additions to Kafka to achieve exactly-once semantics (EoS) including support for idempotence and transactions in the Kafka clients. The main focus will be the specific semantics that Kafka distributed transactions enable and the underlying mechanics which allow them to scale efficiently.
20140708 - Jeremy Edberg: How Netflix Delivers SoftwareDevOps Chicago
Netflix delivers software through fully automated processes and a service-oriented architecture. They hire responsible developers and give them freedom and responsibility. Netflix builds everything to withstand failures through redundancy, automation, and a philosophy of "automate all the things."
"Microservices" is one of the hottest buzzwords and, as usual, everyone wants them, but few know how to build them. In this talk we will offer our interpretation of microservice architecture, and show how we are implementing these ideas: using Scala, Akka, sbt and Docker, we modularized Akka applications, Spark jobs and Play servers.
In the talk we will discuss design trade-offs and challenges that we faced in the process, and how we have overcome them. The focus is not on particular features of Scala language or a library, but on building modern applications using the Typesafe stack and other open-source tools.
FunctionalConf '16 Robert Virding Erlang EcosystemRobert Virding
The document discusses the background and origins of the Erlang ecosystem. It describes how Erlang was originally developed at Ericsson to address the challenges of programming telephone switching systems, which required handling a large number of concurrent processes, distributed systems, continuous operation, and fault tolerance. It outlines the principles that guided the design of Erlang, including lightweight concurrency, asynchronous messaging, and error handling through process supervision. Finally, it discusses how the Erlang ecosystem has expanded through additional languages like Elixir and LFE that maintain Erlang's principles, as well as integrations with other languages like Lua.
The document discusses making a stateless service-oriented application highly available using GlusterFS. It describes setting up a GlusterFS cluster with replicated volumes to provide a centralized data store. The application is configured to mount the GlusterFS volume and an update mechanism is built to notify the application when data changes by monitoring the volume for modifications. This allows making the application redundant and aware of data changes for high availability.
This presentation discusses the history and evolution of cloud computing models from bare metal servers to serverless computing. It begins with an overview of the different eras including IaaS, PaaS, SaaS, and the introduction of containers and Kubernetes for managing containers at scale. The presentation then discusses concepts like Docker, containers, container orchestrators, Kubernetes networking models, and conclusions about abstraction levels. It provides several links to additional resources on topics like containers, serverless computing, pets vs cattle, and Kubernetes.
Sascha Möllering gave a presentation on deploying applications to the AWS cloud. He began with an overview of AWS services like EC2, S3, RDS and explained how to initially create a simple cloud service with one instance each for a web application and database. He then described how to improve the architecture by separating components, adding redundancy and elasticity using services like ELB, autoscaling and read replicas. Sascha demonstrated deploying a sample application built with JHipster and Docker to AWS Elastic Beanstalk, which handles running the containers and mapping environment variables for the database connection.
Cloud providers like Amazon or Goggle have great user experience to create and manage PaaS and IaaS services. But is it possible to reproduce same experience and flexibility locally, in on premise datacenter? This talk describes success story of creation private cloud based on DC/OS cluster. It is used to host and share different services like hadoop or kafka for development teams, dynamically manage services and resource pools with GKE integration.
SaltConf14 - Justin Carmony, Deseret Digital Media - Teaching Devs About DevOpsSaltStack
Let's set aside the buzzwords for a moment and have an honest discussion about DevOps. There is the idea of putting more Dev into Ops, but just as crucial (if not more crucial) is getting your Devs to think more like Ops. Most developers have little to no experience dealing with production environments, and helping them add value to DevOps efforts can be difficult. This talk will cover practical ways of mentoring Devs into more DevOps skills and responsibilities. Ultimately, the goal is to help your Devs gain the skills leading to better production health, application performance and uptime. Of course, we'll also consider how SaltStack can help.
All the troubles you get into when setting up a production ready Kubernetes c...Jimmy Lu
Have you ever try to set up a Kubernetes cluster manually by your own? It may be a small dish to you to set one up on your laptop. However, things are getting harder and harder once you have more nodes to handle, not to mention you also want security, monitoring, auto-scaling, and federated cluster enabled in the production environments. With more features added, the situation gets even worse and more complicated. We developers in Linker Networks had put in a tremendous amount of time in investigating on how to set up Kubernetes clusters efficiently. We designed and built our own tools to automate and facilitate such the painful processes. In this talk, I'll go through all the details and pitfalls in setting up a production ready cluster. Hopefully, the experience I shared could keep you out of these troubles, saving your precious time.
Latest (storage IO) patterns for cloud-native applications OpenEBS
Applying micro service patterns to storage giving each workload its own Container Attached Storage (CAS) system. This puts the DevOps persona within full control of the storage requirements and brings data agility to k8s persistent workloads. We will go over the concept and the implementation of CAS, as well as its orchestration.
Sergey Dzyuban "To Build My Own Cloud with Blackjack…"Fwdays
Cloud providers like Amazon or Google have a great user experience to create and manage PaaS. But is it possible to reproduce the same experience and flexibility locally, in the on-premise datacenter? What if your own infrastructure grows to fast and your team can’t deal with it in the old way? What does Jenkins, .NET microservices and TVs for daily meetings have in common?
This talk shares our experience using DC/OS (datacenter operating system) for building flexible and stable infrastructure. I will show the evolution of private cloud from the first steps with Vagrant to the hybrid cloud with instance groups in Google Cloud, the benefits it gives us and the problems we get instead.
Database as a Service (DBaaS) on KubernetesObjectRocket
Learn about ObjectRocket's adventures in Kubernetes. We'll cover why we chose Kubernetes for our DBaaS platform, the challenges we faced, and how we overcame them. A presentation for DevWeek Austin 2018.
Slide deck for the Kubernetes Manchester meetup December 2018 talk. Jim introduces a little about moneysupermarket, the direction we're heading and historical problems we've had.
I (David) then walk through the technology choices we've made and how they fit together to form our Istio service mesh on an auto-scaling AWS EC2 kubernetes platform.
Sanger, upcoming Openstack for Bio-informaticiansPeter Clapham
Delivery of a new Bio-informatics infrastructure at the Wellcome Trust Sanger Center. We include how to programatically create, manage and provide providence for images used both at Sanger and elsewhere using open source tools and continuous integration.
Distributed Tensorflow with Kubernetes - data2day - Jakob KaralusJakob Karalus
This document discusses using Distributed Tensorflow with Kubernetes for training neural networks. It covers:
- The need for distributed training to handle large datasets, deep models, and high accuracy requirements.
- Kubernetes as an orchestration tool for scheduling Tensorflow across nodes with GPUs.
- Key concepts like parameter servers, worker replicas, and synchronous/asynchronous training modes.
- Steps for setting up distributed Tensorflow jobs on Kubernetes including defining the cluster, assigning operations, creating training sessions, and packaging into containers.
- Considerations for enabling GPUs, building Docker images, writing deployments, and automating with tools like the Tensorflow Operator.
How DreamHost builds a Public Cloud with OpenStackCarl Perry
This document summarizes DreamHost's presentation on how they built a public cloud using OpenStack. Some key points:
- DreamHost is using OpenStack for compute, storage, and networking in their public cloud offering called DreamCompute.
- For storage, they chose Ceph which provides shared, scalable block and object storage.
- Their network architecture uses 10Gb switches in a spine-leaf topology with logical networking software for tenant isolation.
- Automation is key to managing the cloud infrastructure and providing services to customers.
- DreamHost discussed the considerations and challenges in building the cloud such as scalability, speed, monitoring, security and cost effectiveness.
Apache Kafka's rise in popularity as a streaming platform has demanded a revisit of its traditional at-least-once message delivery semantics.
In this talk, we present the recent additions to Kafka to achieve exactly-once semantics (EoS) including support for idempotence and transactions in the Kafka clients. The main focus will be the specific semantics that Kafka distributed transactions enable and the underlying mechanics which allow them to scale efficiently.
20140708 - Jeremy Edberg: How Netflix Delivers SoftwareDevOps Chicago
Netflix delivers software through fully automated processes and a service-oriented architecture. They hire responsible developers and give them freedom and responsibility. Netflix builds everything to withstand failures through redundancy, automation, and a philosophy of "automate all the things."
"Microservices" is one of the hottest buzzwords and, as usual, everyone wants them, but few know how to build them. In this talk we will offer our interpretation of microservice architecture, and show how we are implementing these ideas: using Scala, Akka, sbt and Docker, we modularized Akka applications, Spark jobs and Play servers.
In the talk we will discuss design trade-offs and challenges that we faced in the process, and how we have overcome them. The focus is not on particular features of Scala language or a library, but on building modern applications using the Typesafe stack and other open-source tools.
FunctionalConf '16 Robert Virding Erlang EcosystemRobert Virding
The document discusses the background and origins of the Erlang ecosystem. It describes how Erlang was originally developed at Ericsson to address the challenges of programming telephone switching systems, which required handling a large number of concurrent processes, distributed systems, continuous operation, and fault tolerance. It outlines the principles that guided the design of Erlang, including lightweight concurrency, asynchronous messaging, and error handling through process supervision. Finally, it discusses how the Erlang ecosystem has expanded through additional languages like Elixir and LFE that maintain Erlang's principles, as well as integrations with other languages like Lua.
The document discusses making a stateless service-oriented application highly available using GlusterFS. It describes setting up a GlusterFS cluster with replicated volumes to provide a centralized data store. The application is configured to mount the GlusterFS volume and an update mechanism is built to notify the application when data changes by monitoring the volume for modifications. This allows making the application redundant and aware of data changes for high availability.
This presentation discusses the history and evolution of cloud computing models from bare metal servers to serverless computing. It begins with an overview of the different eras including IaaS, PaaS, SaaS, and the introduction of containers and Kubernetes for managing containers at scale. The presentation then discusses concepts like Docker, containers, container orchestrators, Kubernetes networking models, and conclusions about abstraction levels. It provides several links to additional resources on topics like containers, serverless computing, pets vs cattle, and Kubernetes.
Sascha Möllering gave a presentation on deploying applications to the AWS cloud. He began with an overview of AWS services like EC2, S3, RDS and explained how to initially create a simple cloud service with one instance each for a web application and database. He then described how to improve the architecture by separating components, adding redundancy and elasticity using services like ELB, autoscaling and read replicas. Sascha demonstrated deploying a sample application built with JHipster and Docker to AWS Elastic Beanstalk, which handles running the containers and mapping environment variables for the database connection.
Cloud providers like Amazon or Goggle have great user experience to create and manage PaaS and IaaS services. But is it possible to reproduce same experience and flexibility locally, in on premise datacenter? This talk describes success story of creation private cloud based on DC/OS cluster. It is used to host and share different services like hadoop or kafka for development teams, dynamically manage services and resource pools with GKE integration.
SaltConf14 - Justin Carmony, Deseret Digital Media - Teaching Devs About DevOpsSaltStack
Let's set aside the buzzwords for a moment and have an honest discussion about DevOps. There is the idea of putting more Dev into Ops, but just as crucial (if not more crucial) is getting your Devs to think more like Ops. Most developers have little to no experience dealing with production environments, and helping them add value to DevOps efforts can be difficult. This talk will cover practical ways of mentoring Devs into more DevOps skills and responsibilities. Ultimately, the goal is to help your Devs gain the skills leading to better production health, application performance and uptime. Of course, we'll also consider how SaltStack can help.
All the troubles you get into when setting up a production ready Kubernetes c...Jimmy Lu
Have you ever try to set up a Kubernetes cluster manually by your own? It may be a small dish to you to set one up on your laptop. However, things are getting harder and harder once you have more nodes to handle, not to mention you also want security, monitoring, auto-scaling, and federated cluster enabled in the production environments. With more features added, the situation gets even worse and more complicated. We developers in Linker Networks had put in a tremendous amount of time in investigating on how to set up Kubernetes clusters efficiently. We designed and built our own tools to automate and facilitate such the painful processes. In this talk, I'll go through all the details and pitfalls in setting up a production ready cluster. Hopefully, the experience I shared could keep you out of these troubles, saving your precious time.
Latest (storage IO) patterns for cloud-native applications OpenEBS
Applying micro service patterns to storage giving each workload its own Container Attached Storage (CAS) system. This puts the DevOps persona within full control of the storage requirements and brings data agility to k8s persistent workloads. We will go over the concept and the implementation of CAS, as well as its orchestration.
Sergey Dzyuban "To Build My Own Cloud with Blackjack…"Fwdays
Cloud providers like Amazon or Google have a great user experience to create and manage PaaS. But is it possible to reproduce the same experience and flexibility locally, in the on-premise datacenter? What if your own infrastructure grows to fast and your team can’t deal with it in the old way? What does Jenkins, .NET microservices and TVs for daily meetings have in common?
This talk shares our experience using DC/OS (datacenter operating system) for building flexible and stable infrastructure. I will show the evolution of private cloud from the first steps with Vagrant to the hybrid cloud with instance groups in Google Cloud, the benefits it gives us and the problems we get instead.
Database as a Service (DBaaS) on KubernetesObjectRocket
Learn about ObjectRocket's adventures in Kubernetes. We'll cover why we chose Kubernetes for our DBaaS platform, the challenges we faced, and how we overcame them. A presentation for DevWeek Austin 2018.
Slide deck for the Kubernetes Manchester meetup December 2018 talk. Jim introduces a little about moneysupermarket, the direction we're heading and historical problems we've had.
I (David) then walk through the technology choices we've made and how they fit together to form our Istio service mesh on an auto-scaling AWS EC2 kubernetes platform.
Sanger, upcoming Openstack for Bio-informaticiansPeter Clapham
Delivery of a new Bio-informatics infrastructure at the Wellcome Trust Sanger Center. We include how to programatically create, manage and provide providence for images used both at Sanger and elsewhere using open source tools and continuous integration.
Distributed Tensorflow with Kubernetes - data2day - Jakob KaralusJakob Karalus
This document discusses using Distributed Tensorflow with Kubernetes for training neural networks. It covers:
- The need for distributed training to handle large datasets, deep models, and high accuracy requirements.
- Kubernetes as an orchestration tool for scheduling Tensorflow across nodes with GPUs.
- Key concepts like parameter servers, worker replicas, and synchronous/asynchronous training modes.
- Steps for setting up distributed Tensorflow jobs on Kubernetes including defining the cluster, assigning operations, creating training sessions, and packaging into containers.
- Considerations for enabling GPUs, building Docker images, writing deployments, and automating with tools like the Tensorflow Operator.
Scalable and Reliable Logging at PinterestKrishna Gade
Pinterest uses Kafka as the central logging system to collect over 120 billion messages per day from thousands of hosts. They developed Singer, a lightweight logging agent, to reliably upload application logs to Kafka with low latency. Data is then moved from Kafka to cloud storage using systems like Secor and Merced that ensure exactly-once processing. Maintaining high log quality requires monitoring for anomalies, auditing new features, and catching issues both before and after releases through automated tooling.
Make It Cooler: Using Decentralized Version Controlindiver
A commonly used version control system in the ColdFusion community is Subversion -- a centralized system that relies on being connected to a central server. The next generation version control systems are “decentralized”, in that version control tasks do not rely on a central server.
Decentralized version control systems are more efficient and offer a more practical way of software development.
In this session, Indy takes you through the considerations in moving from Subversion to Git, a decentralized version control system. You also get to understand the pros and cons of each and hear of the practical experience of migrating projects to decentralized version control.
Version control is often used in conjunction with a testing framework and continuous integration. To complete the picture, Indy walks you through how to integrate Git with a testing framework, MXUnit, and a continuous integration server, Hudson.
This document provides an overview of an Amazon EKS hands-on workshop. It introduces the workshop agenda which includes deploying example microservices, logging with Elasticsearch Fluentd and Kibana, monitoring with Prometheus and Grafana, and continuous integration/continuous delivery using GitOps with Weave Flux. Key concepts covered are Kubernetes pods, services, deployments, container networking with CNI plugins, observability tools, and CI/CD approaches.
This document provides an overview of Kubernetes concepts and best practices for evolving applications to run on Kubernetes. It discusses initial "lift and shift" approaches as well as principles for container and application design that are well-suited for Kubernetes. These include having containers do one thing, making images immutable, ensuring self-containment and runtime confinement, and designing for process disposability and observability. The document also covers how people and processes need to change, such as adopting new deployment strategies, defining interaction contracts, and being comfortable with change and new approaches to debugging.
Engage 2020 - Kubernetes for HCL Connections Component Pack - Build or Buy?panagenda
HCL Connections V7 will be based on Kubernetes only! A parallel WebSphere environment won't be necessary any longer. Martin and Christoph collected the basics and differences in building a Kubernetes environment of your choice. They show you a comparison of an on-premises deployment versus a hosted cloud environment (Amazon EKS). After this session you have the basics to size and build a Kubernetes cluster for Component Pack, so you can start learning the new technology to take off with Connections V7 and become a Kubernaut.
Kubernetes for HCL Connections Component Pack - Build or Buy?Martin Schmidt
HCL Connections V7 will be based on Kubernetes only! A parallel WebSphere environment won't be necessary any longer. Martin and Christoph collected the basics and differences in building a Kubernetes environment of your choice. They show you a comparison of an on-premises deployment versus a hosted cloud environment (Amazon EKS). After this session you have the basics to size and build a Kubernetes cluster for Component Pack, so you can start learning the new technology to take off with Connections V7 and become a Kubernaut.
Simplify Your Way To Expert Kubernetes ManagementDevOps.com
Kubernetes is a deep and complex technology that is evolving fast with new functionality and a growing ecosystem of cloud-native solutions. While the public cloud delivers an almost frictionless user experience, configuring and managing a production Kubernetes environment is an enormous technical challenge for the majority of enterprises that choose to do so on premises. Without the right approach, operationalizing Kubernetes in the data center can take upwards of 6 months, jeopardizing developer productivity and speed-to-market.
In this webinar, you’ll learn from Nutanix cloud native experts on how to fast-track your way to operationalizing a production-ready Kubernetes environment on-prem.
Specifically, we’ll talk about:
How containerized applications use IT resources (and why legacy infrastructure isn’t built for Kubernetes);
The main advantages of running Kubernetes on prem (as part of a multi-cloud strategy);
Key aspects of Kubernetes lifecycle management that greatly benefit from automation.
Monitoring kubernetes across data center and cloudDatadog
This document summarizes a presentation about monitoring Kubernetes clusters across data centers and cloud platforms using Datadog. It discusses how Kubernetes provides container-centric infrastructure and flexibility for hybrid cloud deployments. It also describes how monitoring works in Google Container Engine using cAdvisor, Heapster, and Stackdriver. Finally, it discusses how Datadog and Tectonic can be used to extend Kubernetes monitoring capabilities for enterprises.
Spinnaker is an open source continuous delivery platform started by Netflix that helps release software changes quickly and confidently across multiple clouds including Kubernetes. It provides automated promotion of changes through pipelines that represent the delivery process in a single view along with notifications and triggers. This improves on Jenkins which requires moving changes between jobs manually and lacks a unified view of infrastructure. Setting up Spinnaker involves installing it on a Kubernetes cluster using Helm and exposing the interface. A typical pipeline starts changes in development and promotes them through user acceptance testing to production environments.
This document discusses moving a Magento e-commerce platform to the AWS cloud to improve scalability, availability, and cost efficiency. It provides an overview of traditional Magento infrastructure and proposes an alternative infrastructure design on AWS using services like EC2, ELB, RDS, S3, CloudFront, Route53, and Elasticache. It highlights key considerations for each technology and steps to automate the infrastructure and deployment process.
Lc3 beijing-june262018-sahdev zala-guangyaSahdev Zala
Our slides deck, used at the LinuxCon+ContainerCon+CLOUDOPEN China 2018, on Kubernetes cluster design considerations and our journey to 1000+ node single cluster with IBM Cloud.
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma
My talk for the Indian School of Business (ISB) Emerging Leaders Program Cohort 9. In this talk, I discussed key issues around adoption of GenAI in business - benefits, opportunities and limitations. I also discussed how my research on Theory of Cognitive Chasms helps address some of these issues
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB
Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
TrsLabs - Fintech Product & Business ConsultingTrs Labs
Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
How Can I use the AI Hype in my Business Context?Daniel Lehner
𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨?
Everyone’s talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know 𝗵𝗼𝘄.
✅ What exactly should you ask to find real AI opportunities?
✅ Which AI techniques actually fit your business?
✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
HCL Nomad Web – Best Practices and Managing Multiuser Environmentspanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-and-managing-multiuser-environments/
HCL Nomad Web is heralded as the next generation of the HCL Notes client, offering numerous advantages such as eliminating the need for packaging, distribution, and installation. Nomad Web client upgrades will be installed “automatically” in the background. This significantly reduces the administrative footprint compared to traditional HCL Notes clients. However, troubleshooting issues in Nomad Web present unique challenges compared to the Notes client.
Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
In this webinar, we will explore effective strategies for diagnosing and resolving common problems in HCL Nomad Web, including
- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
- Utilizing Client Clocking
4. NU.nl
About
• First dutch digital news platform.
• Unique visitors:
• 7 mln. / month
• 2.1 mln. / day
• Page hits: ~12 mln / day
• API: ~150k rpm / 2500rps
5. NU.nl
Sanoma
• Part of Sanoma
• NL: NU.nl, Viva, Libelle, Scoopy
• FI: Helsingin Sanomat
• Reaching ~9.8 mln dutch people / month
6. IT organization
Teams
• NU.nl teams
• Web 1 (application / front-end-ish)
• Web 2 (application / back-end-ish / infra)
• Feature 1 & 2 (cross-discipline)
• iOS
• Android
• Sanoma teams
• DevSupport, Mediatool, Content Aggregation
7. NU.nl
Growing number of teams
• Increased number of parallel workflows
• Testing
• Releasing
• Roadmaps
• Knowing about everything no longer possible
• Aligning ‘procedures by agreement’ increasingly hard
11. Development workflow
From code to release
• Code
• Automated tests
• Code review
• Manually initiated deploy to test
• Feature test
• Manually initiated deploy to staging
• Exploratory test
• Manually initiated deploy to production
12. DevOps practices
Solid foundation
• All infra in code
• Terraform
• Terrible providing mechanisms:
• Authorization
• Managing TF state files
13. DevOps practices
But…
• Setting up additional test environments slow
• Slow feedback loop
• Terraform plan vs apply (surprise surprise, it didn’t work)
• Ansible (~20 minutes)
• Vagrant? (but not fully representative of EC2)
• Config drift
• Hard to nail down every system package version
• EC2 instances having different lifecycle
14. DevOps practices
But… (part 2)
• No scaling infra*
• Heavily invested in Ansible
• Config & secrets management problematic
• GUIs time consuming
• No change history
• Or highly detached from code history
• No context
• Not overly secret
*Yes, we know it’s 2019
15. DevOps practices
But… (part 3)
• Current deployment system assumes fixed set of servers
• Possible alternatives include:
• ASG rolling updates (can get slow)
• Pull current application code on start-up (even slower)
• Bake AMI
• Periodically poll for application version to be deployed
• Works quite well
• …as long as new code combined with config doesn’t break.
• So a certain level of orchestration would be needed.
17. Timing
What direction to move?
• DevOps challenges
• Desire to improve delivery process, having true artifacts
• Early 2018
• Containers are a well-established way of ‘packaging’ an application
• Kubernetes getting out of early-adopters phase
• NU.nl (re-)launching a new product: NUjij
18. Improvement layers
A journey or a destination?
1: Containers as artifacts
• Versatile
• Forces us to do certain things right
• 12factor
• Centralized logging
• Easily moved through a pipeline
• Lots of tooling
19. Improvement layers
A journey or a destination?
2: A flexible platform to deploy and run containerized applications on
• Tackling challenges at platform level instead of per-application:
• Scaling
• Security updates
• Observability
• Deployment & configuration process
20. Improvement layers
A journey or a destination?
2: A flexible platform to deploy and run containerized applications on
• Kubernetes
• Rapidly increasing adoption
• Short feedback loop
• Ability to run locally (unlike, say, ECS)
• Easily stamp out deployments for:
• feature testing/demo-ing
• e2e tests
21. Narrowing the scope
Lets not get carried away
The goal is not:
• To chop up change all of our applications into nano- micro-services
• They’re not that monolithic anyway
• To put everything in Kubernetes
• Managed AWS services where possible
• Redis, RDS
Focus on agility and efficiency of what we change most frequently: Code
32. Components
Autoscaling
• Horizontal Pod Autoscaler
• Scales number of pods based on
(CPU) utilization
• Cluster autoscaler
• Running on master nodes
• Scales asg out when pods pending
• Scales asg in when nodes
underutilized
35. Jenkins
Temporary deployment for running tests
• Deploy to temp. namespace
• Jenkins-SU
• Run tests in deployment
• Deploy to test/staging/production
• By bumping image version
• Production: Jenkins-SU
• Clean up temp. namespace
• Jenkins-SU
36. Jenkins
Jenkins-SU
• Sets up namespace
• Adding RBAC for Jenkins
• Only if ns name matches pattern ‘Jenkins-*’
• Deletes namespace
• Only if ns name matches pattern ‘Jenkins-*’
• Avoids need for Jenkins to be able to delete every namespace
curl -X POST --user ${JENKINS_SU_AUTH} --data '{"name": "${K8S_BUILD_NS}"}' http://su.jenkins-su/ns/
curl -X DELETE --user ${JENKINS_SU_AUTH} --data '{"name": "${K8S_BUILD_NS}"}' http://su.jenkins-su/ns/
38. Kubernetes in action
Questions
• Will it be stable?
• Will we be able to operate?
• Should we wait for EKS?
• Do we actually want EKS? What will EKS be like?
41. Incident 1
Accidentally trying to load a ElasticSearch index of 90Gb
• Misconfigured elast-alert (trying to read entire index)
• No memory limit configured
42. Incident 1
Accidentally trying to load a ElasticSearch index of 90Gb
• Required manual intervention: Yes
• Stopping the bleeding:
• Remove elast-alert
• Permanent fixes:
• Don’t load entire index
• Apply limits
49. Incident 2
Rapid traffic increase affecting core components
pod
pod
kubelet
skipper
node
Pods:
0.4 CPU req.
0.8 CPU limit
80% CPU utilization
pod
kubelet
skipper
node
pod
Pods:
0.4 CPU req.
0.8 CPU limit
120% CPU utilization
problems
50. Incident 2
Rapid traffic increase affecting core components
• Required manual intervention: No
• Fixes:
• Reduce CPU burstable amount of pods
• Increase resource requests of skipper
• Mind QoS: Guaranteed, Burstable, Best effort
• Reserve cpu & memory for kubelet
• --kube-reserved
• --system-reserved
53. Incident 3
Application update increasing memory footprint
• Upgrade including moving from MongoDB 3 to MongoDB 4
• HorizontalPodAutoscaler based on CPU
• Scaling based on CPU not kicking in
• New increased memory footprint causing OOMkilled
55. Incident 3
Application update increasing memory footprint
• Required manual intervention: Yes
• Stopping the bleeding:
• Increase memory limit of Talk pods
• Permanent fixes:
• Adjust CPU request/limit & HPA thresholds
• Scale on both CPU and memory
• Note: Not all applications ‘give back’ memory
• Set memory limit higher than request to prevent ‘snowball effect’
58. That’s not fine
Is it?
• On the positive side:
• All are result of (lack of) resource limit configuration
• This can be learned
• On the negative side:
• This needs to be learned
• Note: ‘Availability bias’
60. Automation
Improving the pipeline
• Automating setting the image version is not enough
• Rolling out Kubernetes manifests still manual task
• Updating configuration & secrets still manual task
• Duplication in manifests between stages
• Not easily seen what parts are different
• Differences intentional or accidental?
• This actually slows us down
• Does git represent the current state?
kubectl -n talk get secrets env -o json |jq -r '.data | map_values(@base64d) | to_entries | .[] | .key + "="" + .value +"""'
61. Helm
The package manager for Kubernetes
• Charts
• Configured via values
• It’s like Terraform modules
• Or Ansible group_vars
• Leveraging community knowledge and efforts
• E.g. prometheus-operator
• No need to copy charts, able to reference.
• Helm v3
62. SOPS: Secrets OPerationS
Secrets management stinks, use some sops!
• By Mozilla
• Manage AWS API access, not keys
• Versatile
• YAML, JSON, ENV, INI, binary (plain text)
• Not limited to Kubernetes
• Meaningful diffs
• Alternatives considered:
• Kamus
• Bitnami SealedSecrets
63. Helmfile
Wiring it together
• Charts
• Referenced from online chart sources or local
• Environments
• Test, staging, production
• Referencing values and secrets
• Releases
• Release name
• Reference to chart
• Values (can be a templated file, using vars and secrets from environment)
65. Helmfile
Wiring it together
• Advantages:
• Meaningful git diffs
• Easily manage multiple releases in single pipeline, e.g.:
• Everything related to monitoring and logging
• Kube-system
• Declarative definition
• Of what would otherwise be numerous helm args and steps in CI/CD pipeline
66. Helmfile
Wiring it together
• Advantages (continued):
• Ability to pass in ENV vars
• E.g. build result image tags
• Ability to reference complex charts created by community
• Charts as a building block allows re-use. Example:
• Instead of plain yaml you write a chart
• If fitting workflow, the chart can be a published artifact
• Chart can be re-used e.g. in e2e tests
67. Helmfile
Wiring it together
• Disadvantages:
• 2 levels of templating
• Chart itself
• Only if writing own charts
• Environment & release values into Helm values
• Template error message not overly clear
• Or even misleading
• At least it breaks
73. Helmfile
Final words
But tiller?
• Helm as a templating engine
• Option: Using Helm 2 ‘Tillerless’
• Tiller outside of cluster, not by-passing RBAC
• Start using Helm as package manager when Helm 3 settles down
• Easy removal of temp. per-feature deploys
• Diffs
81. Cluster auto-scaler
Bag of tricks
• Mix predictive and reactive
• Add asg instances without telling cluster-autoscaler
• Traffic expected to arrive by the time cluster-autoscaler starts to scale in,
leaving plenty of resources as needed.
• Pause pods
• Lower priority pods that can safely be evicted
• Effectively ‘creating headroom’ in cluster
82. Considerations
When engaging ‘ludicrous mode’™
Can control-plane handle scale?
• KOPS
• Size master nodes for max. cluster size
• Overhead cost
• EKS
• What’s behind the abstraction?
• ELB 503s exist after all
• Plan: Proof of concepts
84. Consider EKS
Managed control plane
EKS Kops
Managed control plane Total control over setup
Easier: EKS IAM roles for pods
• Launched 2019-09-04 (yesterday)*
Smooth rolling upgrade process
Probably cheaper (2/3 of 3x m4.large) No VPC CNI Pod density limitations
* https://aws.amazon.com/blogs/opensource/introducing-fine-grained-iam-roles-service-accounts/
85. EKS IAM roles for pods
Also possible on DIY clusters, officially launched yesterday
• OIDC federation access (OpenID Connect identity provider)
• Assume role via Secure Token Service (STS)
• Projected service account tokens (JWT) in pod
• STS can validate JWT tokens against OIDC provider
• Boils down to:
• Enable/set-up prerequisites in cluster
• Add ServiceAccount having IAM role annotation to pod
• Use recent AWS SDK
86. Multiple clusters per AWS account
Don’t lock ourselves in a corner.
api.<aws-account-name>.<k8s-sanoma-domain>
api.<cluster-name>.<aws-account-name>.<k8s-sanoma-domain>
Route53 zone 1
Route53 zone 1Route53 zone 2
NS records
87. CI/CD to separate cluster
Similar flows
• No more taints and tolerations
• Similar authorization mechanism to all deploy targets
• Possibly IAM
• No need for Jenkins-SU
• Clusters should be cattle anyway
89. System applications
Small improvements
• Prometheus-operator
• PrometheusRule resource type
• Default dashboards
• EFS
• https://github.com/previousnext/k8s-aws-efs
• Current. Works well but not a lot of active development.
• 2 contributors. 46 stars.
• https://github.com/kubernetes-incubator/external-storage
• De facto EFS provisioner. 146 contributors. 1630 stars.
• Bonus: No more time-consuming initial volume set-up
90. Expand
Increase Return on Investment
• Add more applications
• Facilitate parallel testing & development workflows
• Feature testing
• Mobile app development
• E2e tests