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Small Discussion About Confluence?

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What is Confluence?

Confluence is a team collaboration software. Written in Java and mainly used in corporate environments, it is developed and marketed by Atlassian. Confluence is sold as either on-premises software or as software as a service.

Confluence is our collaboration wiki tool used to help teams to collaborate and share knowledge efficiently. With Confluence, your users can create pages and blogs which can be commented on and edited by all members of the team. For example, you will be able to create a roadmap easily, create notes containing checklist, create a knowledge base and centralize everything in one place. You can also attach files, like your excel planning and display it on a page for more convenience. 

Confluence has also been designed to integrate with Jira and they have many integration points, giving Confluence users the ability to view, interact with, and reference Jira issues from a wiki page.

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posted May 31, 2018 by anonymous

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Jira confluence: 

Confluence is project management tool which provide you best platform to create, organize, and discuss work with your team. Confluence makes team to spend less time hunting things down and more time making things happen. Organize your work, create documents, and discuss everything in one place.

Below operation we can perform under Jira confluence tool:

1. Create

Create anything your team needs - meeting notes, product requirements, knowledge base articles - on the web so everyone can contribute.

2. Discuss

Give feedback on any Confluence page or file with inline and pinned comments. No more ridiculous file_names_with_dates.doc or messy track changes.

3. Centralize

Capture all the information that's scattered among email inboxes and countless apps in the same place.

4. Organize

Give every team, project, or department its own space to store work. Confluence keeps everything organized and accessible. 

Confluence gives you the power to create anything - meeting notes, project plans, product requirements, etc. – thanks to a simple, but powerful editor.

You work with files every day – images, PDFs, spreadsheets, and presentations. You can give feedback directly on your files in Confluence, and it keeps tracks of versions automatically, so you're always working on the right one.

Create reports and charts

Reporting on information stored in JIRA is simple in Confluence. In addition to the JIRA Issues Macro, you can use the JIRA Report blueprint or JIRA Chart macro to show information from your JIRA application visually. It's the best way to give your stakeholders a snapshot of your team or project's progress. 

You can:

  • Use the JIRA report blueprint to create a Change Log or Status report.
  • Use the JIRA Chart Micros to display data as a chart, including pie charts, created vs resolved, and two dimensional charts. 
  • Use JIRA Gadgets to display detailed JIRA reports and charts on pages. 

 

 

 

 

 

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What is ThingSpeak?

ThingSpeak is an open-source Internet of Things (IoT) application and API to store and retrieve data from things using the HTTP protocol over the Internet or via a Local Area Network.ThingSpeak was originally launched by ioBridge in 2010 as a service in support of IoT applications.
ThingSpeak™ is an IoT analytics platform service that allows you to aggregate, visualize and analyze live data streams in the cloud. 

ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak you can perform online analysis and processing of the data as it comes in. ThingSpeak is often used for prototyping and proof of concept IoT systems that require analytics.

ThingSpeak allows you to aggregate, visualize and analyze live data streams in the cloud. 

Some of the key capabilities of ThingSpeak include the ability to

  • Easily configure devices to send data to ThingSpeak using popular IoT protocols.
  • Visualize your sensor data in real-time.
  • Aggregate data on-demand from third-party sources.
  • Use the power of MATLAB to make sense of your IoT data.
  • Run your IoT analytics automatically based on schedules or events.
  • Prototype and build IoT systems without setting up servers or developing web software.
  • Automatically act on your data and communicate using third-party services like Twilio® or Twitter®.

Video for Thingspeak 

https://www.youtube.com/watch?v=2XH1bTWkWIE

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What is Minikube?

Minikube is a tool that makes it easy to run Kubernetes locally. Minikube runs a single-node Kubernetes cluster inside a VM on your laptop for users looking to try out Kubernetes or develop with it day-to-day.

Minikube supports Kubernetes features such as:

  • DNS
  • NodePorts
  • ConfigMaps and Secrets
  • Dashboards
  • Container Runtime: Docker, rkt, CRI-O and containerd
  • Enabling CNI (Container Network Interface)
  • Ingress

When using a single VM of Kubernetes, it’s really handy to reuse the Minikube’s built-in Docker daemon; as this means you don’t have to build a docker registry on your host machine and push the image into it - 

We can just build inside the same docker daemon as minikube which speeds up local experiments. Just make sure you tag your Docker image with something other than ‘latest’ and use that tag while you pull the image. Otherwise, if you do not specify version of your image, 

it will be assumed as :latest, with pull image policy of Always correspondingly, which may eventually result in ErrImagePull as you may not have any versions of your Docker image out there in the default docker registry (usually DockerHub) yet.

Video for Minikube

https://www.youtube.com/watch?v=dhJi5_J7ztY

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What is Polymer.Js?

Polymer.js is a JavaScript library created by Google that allows reusing the HTML elements for building applications with components. Polymer is an open-source JavaScript library developed by Google developers

Polymer provides a number of features over vanilla Web Components:

  • Simplified way of creating custom elements
  • Both One-way and Two-way data binding
  • Computed properties
  • Conditional and repeat templates
  • Gesture events

Polymer.js places a hefty set of requirements on the browser, relying on a number of technologies that are in still in the process of standardization (by W3C) and are not yet present in today’s browsers. 

Examples include the shadow dom, template elements, custom elements, HTML imports, mutation observers, model-driven views, pointer events, and web animations. These are marvelous technologies, but at least as of now, that are yet-to-come to modern browsers.

The Polymer strategy is to have front-end developers leverage these leading-edge, still-to-come, browser-based technologies, which are currently in the process of standardization (by W3C), as they become available. 

The recommended polyfills are designed in such a way that (at least theoretically) will be seamless to replace once the native browser versions of these capabilities become available.

Video for Polymer.Js
https://www.youtube.com/watch?v=tvafAyxkuVk

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What is React Navigation?

React Navigation is a popular library for routing and navigation in a React Native app.React Navigation is born from the React Native community's need for an extensible yet easy-to-use navigation solution written entirely in JavaScript 

Features

  1) Easy-to-use

          Start quickly with built-in navigators that deliver a seamless out-of-the-box experience.

   2) Components built for iOS and Android

           Platform-specific look-and-feel with smooth animations and gestures.

    3) Completely customizable

            If you know how to write apps using JavaScript you can customize any part of React Navigation.

     4) Extensible platform

            React Navigation is extensible at every layer— you can write your own navigators or even replace the user-facing API.

Npm and Yarn Command

npm install --save react-navigation
yarn add react-navigation

Video for Reach Navigation

https://www.youtube.com/watch?v=C96piR3FRww

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What is MLlib?

MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

Main Benefits

  • Ease of Use
  • Performance
  • Runs Everywhere

MLlib contains many algorithms and utilities.

ML algorithms include:

  • Classification: logistic regression, naive Bayes,...
  • Regression: generalized linear regression, survival regression,...
  • Decision trees, random forests, and gradient-boosted trees
  • Recommendation: alternating least squares (ALS)
  • Clustering: K-means, Gaussian mixtures (GMMs),...
  • Topic modeling: latent Dirichlet allocation (LDA)
  • Frequent itemsets, association rules, and sequential pattern mining

Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.

Video for MLib 

https://www.youtube.com/watch?v=HaNoUnrQWd0

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What is Statsmodels ?

Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface.

Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy[3] for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries. Statmodels in free software released under the Modified BSD (3-clause) license.

Features

  • Linear regression models:
  • Mixed Linear Model with mixed effects and variance components
  • GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
  • Bayesian Mixed GLM for Binomial and Poisson
  • GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
  • Discrete models:
  • Multivariate:
  • Nonparametric statistics: Univariate and multivariate kernel density estimators
  • Datasets: Datasets used for examples and in testing
  • Statistics: a wide range of statistical tests
  • Imputation with MICE, regression on order statistic and Gaussian imputation
  • Mediation analysis
  • Tools for reading Stata .dta files, but pandas has a more recent version
  • Table output to ascii, latex, and html
  • Miscellaneous models​

Video for Statsmodels

https://www.youtube.com/watch?v=RWRsxhUzpxk

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