Components and Architecture of Kubernetes | Learn Kubernetes – Part 2

Kubernetes consists of several components, some of them optional, some mandatory for the whole system to function.

There are mainly two nodes. Each nodes have several components.

  1. Master Node

    • Master node is responsible for the management of Kubernetes cluster.
    • This is for all administrative tasks.
    • It is also responsible for taking care of Orchestrating the worker node.
  2. Worker Node

    • Worker node is responsible to run Pods.
    • It contains all the services to:
      • Manage the networking between the container
      • Communicate with master node
      • Assign resources to containers scheduled.

List of Components of Kubernetes: –

Components of Kubernetes

Architecture of Kubernetes Components: –

Kubernetes follows client-server architecture. Wherein, Master Node is installed on one machine and the Worker node is installed on separate Linux machines.

Kubernetes Architecture

That’s is all from this topic. In next articles we will explore more about Kubernetes. Please refer to different Parts of “Learn Kubernetes”.

Related Articles:-

What is Kubernetes? | Learn Kubernetes – Part 1

We will appreciate if you add your inputs in comment box. We will definitely add it to the relevant section.

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Related Articles: –

What is Kubernetes? | Learn Kubernetes – Part 1

Components and Architecture of Kubernetes | Learn Kubernetes – Part 2

Versions of Kubernetes | Learn Kubernetes – Part 3

Kubernetes Terminology every admins need to know | Learn Kubernetes – Part 4

Getting Start to Setup and Configure Kubernetes | Learn Kubernetes – Part 5

How to Install Kubernetes on Windows 10 with Hyper-v using Minikube| Learn Kubernetes – Part 6

What is Kubernetes? | Learn Kubernetes – Part 1

One Liner Definition: –

Kubernetes is a platform to work with Container. It provides Deployment, Scaling, and Monitoring.

Kubernetes_(container_engine)

Origin and meaning of Kubernetes: –

Kubernetes, an Open source project from Google, generally known as K8s. The name Kubernetes originates from Greek, meaning helmsman or pilot, and is the root of governor and cybernetic. K8s is an abbreviation derived by replacing the 8 letters “ubernete” with “8”.

Overview of Kubernetes: –

  • Kubernetes is an orchestration engine for container technologies such as Docker and Rocket container. It orchestrates computing, networking, and storage infrastructure on behalf of user workloads.
  • It makes containerized applications astonishingly easy to deploy, scale, and manage.
  • It is a portable, extensible open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation.
  • It can speed up the development process by making easy, automated deployments, updates, and by managing our apps and services with almost zero downtime.
  • It also provides self-healing. Kubernetes can detect and restart services when a process crashes inside the container.
  • It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.
  • It provides much of the simplicity of Platform as a Service (PaaS) with the flexibility of Infrastructure as a Service (IaaS), and enables portability across infrastructure providers.
  • Kubernetes isn’t a replacement for Docker. However, Kubernetes is a replacement for some of the higher-level technologies that have emerged around Docker.
  • It keeps analyzing your traffic and the load that’s being used by the container. When the traffic is reaching the threshold, it auto scale up containers.
  • It works with Master node and worker nodes. Each worker nodes work with multiple pods.
  • It is originally developed by Google, it is open-sourced since its launch and managed by a large community of contributors. Any developer can package up applications and deploy them on Kubernetes with basic Docker knowledge.

Scenario for using Kubernetes: –

Let’s take an example that you have lot of workloads that is being managed by Containers. When work loads increases then you have to scale up container manually. Here Kubernetes helps by doing the auto scale of containers when workload reaches to the threshold.

That’s is all from this topic. In next articles we will explore more about Kubernetes. Please refer to different Parts of “Learn Kubernetes”.

We will appreciate if you see any inputs to add here, do share in comment box. We will definitely add it to our sections.

Thanks for visiting here. Share this article if you found it useful.

 

Related Articles: –

What is Kubernetes? | Learn Kubernetes – Part 1

Components and Architecture of Kubernetes | Learn Kubernetes – Part 2

Versions of Kubernetes | Learn Kubernetes – Part 3

Kubernetes Terminology every admins need to know | Learn Kubernetes – Part 4

Getting Start to Setup and Configure Kubernetes | Learn Kubernetes – Part 5

How to Install Kubernetes on Windows 10 with Hyper-v using Minikube| Learn Kubernetes – Part 6

How To – Enable NIC Teaming in Windows 10 using PowerShell

Hey All, Today I am going to write a blog on How to Enable NIC teaming in Windows 10 Operating System.

While I was configuring my home lab, I had planned to use existing two Network Adaptors as NIC teaming on a Windows 10 machine. I had explored the same and thought to share with you if you are also planning to configure NIC teaming in your environment. Here is PowerShell command you can use.

PowerShell Command to Enable NIC Teaming: –

New-NetLbfoTeam TeamName "[NIC1_NAME]","[NIC2_NAME]"

Steps: –

  • Login to Windows 10 Machine
  • Click Start and Search PowerShell.
  • Right Click on PowerShell and Open it as Administrator.
  • Type below command and press Enter.
New-NetLbfoTeam TeamName "[NIC1_NAME]","[NIC2_NAME]"
  • It will give a success message. Refer to below example.
  • Now you can check from ncpa.cpl (Type in Run), it will show Team Network Adaptor there.
  • Now configure IP address in Team Network Adaptor.

Example: –

Existing Network Adaptors

NIC

Command in PowerShell

NIC Teaming Success

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CCA175 – Cloudera Spark and Hadoop Developer

The certification is conducted by Cloudera and can be a masterstroke in boosting career in Big Data field. Every certification needs a proper study plan and a well-planned D-Day strategy. In this blog I am going to give idea how you can plan for the certification and clear it very easily. I cannot reveal exam question types since it is against the Cloudera policy. Let me give some overview about the exam first.

About the exam

There are other certification exam also offered by different vendors like Hortonworks etc. but in this blog I will focus on CCA175 Cloudera Spark and Hadoop developer offered by Cloudera. In the current scenario, Spark and Hive becomes the core of big data analytics. Every business solution provider is focusing on Spark and Hive to find the solution for client problem scenarios. CCA175 also revolves around Spark transformations and actions, Hive operations, Real time data ingestion and Sqoop import and export concepts along with handling compression and different file formats.

Exam overview

  • No of Questions: 8-12 performance-based scenarios (hands-on) tasks on Cloudera Enterprise cluster.
  • Questions based on: Spark transformations and actions, Spark RDD and dataframes, Sqprk SQL, Compression codecs, handling different file formats, Sqoop import and export, Hive concepts, Real time data ingestion.
  • Time Limit: 120 minutes
  • Passing Score: 70%
  • Language: English
  • Price: USD $295

Required Skills

[table id=2 /]

The above table specifies all the required skills suggested by Cloudera. It is better if you know all these but from my experience give more focus on Spark RDD, Saprk DF, Spark SQL, Hive, HDFS command line, Compression codec and handling different file formats.

Where to start

Here are the pre-requisites before you start your learning journey and also for practicing these technologies.

  • Basic knowledge of any programming language. If you have scala or python background then it makes it much easier. It is true that exam scores are only based on scenario output file and you can use any technique to solve problem statements but it will save time if you solve in scala or python.
  • Good Understanding of what data and database means. Some knowledge of SQL querying also helps.
  • Finally, the most import aspect of this practical learning is to have an environment. It may take hours or days for you to build a hadoop environment with all these combination of technologies. Cloudera makes it easier for you by providing a quickstart VM that you can install on your machine. Please read the instructions carefully and watch some YouTube videos on how to setup the quickstart VM for your practice.

Get started with Prep plan

In my experience 5-6 weeks are enough to clear the certification if you spend 2-3 hours of time daily. If you can devote 6-7 hours, then 3-4 weeks are more than enough. This can also vary based on whether you are a newbie to big data or an experienced professional. If you are newbie, first start with Hadoop basic fundamentals, learn basic HDFS commands and be familiarize with Cloudera quickstart VM. After that you can follow this blog for certification purpose.

For preparation plan, Please follow below steps to get started:

  • Motive should be gain knowledge. Try to gain knowledge first then go for certification.
  • Follow a good study material or videos from where you can learn the concepts first. ITversity youtube videos can be a good source to start with.
  • After you are good with your concept, Practice as many as scenarios as you can. PRACTICE IS THE KEY.
  • Apply different logics in datasets or tables. Practice by framing questions from your own with the datasets available.
  • Go to the required skills section and ensure that you are good with all topics.
  • Follow a good source to practice for the exam where you can find good dummy questions.
  • Make a good strategy before registering for exam.
  • PRACTICE, PRACTICE and PRACTICE till you get the confidence that I can handle any type of questions in exam.
  • Ask a question to yourself – “Are you up to the challenge?”

D-Day strategy

  • Don`t be nervous. Go with free mindset. Have a confidence on your hard work.
  • Be mentally stable and prepared.
  • Skip the problem question if you stuck somewhere. There are very easy questions too.
  • Try to implement problem with different logic if one is not working.
  • Practice with dataframe if you want but do not skip RDDs also. Sometimes a combinations of two can be very helpful.

Registering for exam

  • Create account in Cloudera official webiste, then purchase certification exam (valid for 1 year) there you will give your card details.
  • Next step is, goto examslocal and create account there with same email id you used for cloudera website.
  • In examslocal search bar, you can give ‘CCA175’, & automatically recognizes your exam voucher & asks for slot booking.
  • You can book at any available slot but it should be minimum 24 hrs difference from time of booking.
  • On D-Day, Proctor will ask you for a valid ID. You can give the exam from anywhere but the place should be free. No one expect you will be there. Proctor will check the environment using webcam.

Evaluation, Scoring and Certification

  • Each CCA question requires you to solve a particular scenario.
  • You are not required to use the template and may solve the scenario using a language you prefer.
  • Your exam is graded immediately upon submission and you are e-mailed a score report the same day as your exam. Your score report displays the problem number for each problem you attempted and a grade on that problem.
  • If you pass the exam, you receive a second e-mail within a few days of your exam with your digital certificate as a PDF, your license number, a Linkedin profile update, and a link to download your CCA logos for use in your personal business collateral and social media profiles.

This is all from our side. GOOD LUCK for the exam from Team Cloud Pathshala!

ALL THE BEST for your shining future!

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