Kubernetes, as we know, coordinates a highly available cluster of computers that are connected to work as a single unit. Kubernetes contains a number of abstractions that allows deployment of containerized applications to the cluster without attaching them to individual machines.

In short, Kubernetes is -

  • Portable: public, private, hybrid, multi-cloud
  • Extensible: modular, pluggable, hook able, composable
  • Self-healing: auto-placement, auto-restart, auto-replication, auto-scaling

In this post,we are going to take look at steps on how to back up and restore your Kubernetes cluster resources and persistent volumes using Velero open source tool.

First off, let’s understand the typical scenarios where you…


Today we are going to learn about how to aggregate Docker container logs and analyse the same centrally using ELK stack. ELK stack comprises of Elasticsearch,Logstash, and Kibana tools.Elasticsearch is a highly scalable open-source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time.Kibana is like window into the Elastic Stack. It enables visual exploration and real-time analysis of your data in Elasticsearch. Logstash is the central dataflow engine in the Elastic Stack for gathering, enriching, and unifying all of your data regardless of format or schema.If…


In this post,we take look at most popular courses of 2018 that will help boost your career and expand your knowledge. Check them out, and start enrolling today!

Coursera
Coursera

Image — Coursera

#1.Machine Learning from Stanford University

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. …


Linux containers has been around since the early 2000s and architected into Linux in 2007. Due to small footprint and portability of containers, the same hardware can support an exponentially larger number of containers than VMs, dramatically reducing infrastructure costs and enabling more apps to deploy faster. But due to usability issues, it didn’t kick-off enough interest until Docker (2013) came into picture.

Unlike hypervisor (ex. Xen,hyper-v) virtualization, where virtual machines run on physical hardware via an intermediation layer (hypervisor), containers instead run user space on top of an operating system’s kernel. That makes them very lightweight and fast.

Containers…


In Microservice ecosystem, usually cross-cutting concerns such as service discovery, service-to-service and origin-to-service security, observability and resiliency etc., are deployed via shared asset such as an API gateway or ESB. As microservice grows in size and complexity, it can become harder to understand and manage.

Service mesh technique addresses these challenges where implementation of these cross-cutting capabilities is configured as code. A service mesh provides an array of network proxies alongside containers. Each proxy serves as a gateway to each interaction that occurs, both between containers and between servers. The proxy accepts the connection and spreads the load across the…


According to Gartner,there is huge enterprise-level interest in artificial intelligence projects and their potential to fundamentally change the dynamics of business value. However,biggest pain point that emerged from Gartner’s 2018 CIO survey was the lack of specialized skills in AI, with 47% of CIOs reporting that they needed new skills for AI projects.With Gartner predicting AI as #2 in Top 10 Strategic Technology Trends for 2019.There is need for AI engineers to build, implement, and maintain AI projects.Here’s best compilation of 15 Udemy Artificial Intelligence Courses.

TAKE ACTION AND START ENROLLING TODAY! All of the below listed courses are running…


Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the…


With the increasing adoption of containers and microservices in the enterprises, there is need now to focus on structuring the containers and other distributed building blocks so that we can solve some of the common design challenges.In this post,we take look at some of the key design patterns that could be used in Docker,Kubernetes and other container platforms.The foundation work was actually done by Brendan Burns and David Oppenheimer in their container design patterns paper,this post summarizes the key design patterns.

#1.Side Car Pattern

In this pattern, the sidecar is attached to a parent application and provides supporting features for the application. …


Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. Data Scientists perform sophisticated empirical analysis to understand and make predictions about complex systems. They draw on methods and tooling from probability and statistics, mathematics, and computer science and primarily focus on extracting insights from data. They communicate results through statistical models, visualizations, and data products.

Per IBM study, By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000.The …


Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. Data Scientists perform sophisticated empirical analysis to understand and make predictions about complex systems. They draw on methods and tooling from probability and statistics, mathematics, and computer science and primarily focus on extracting insights from data. They communicate results through statistical models, visualizations, and data products.

Per IBM study, By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000.The …

KarthiKeyan Shanmugam

Experienced IT Professional. Blogger@upnxtblog.com

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