Install Node.js, npm, and Angular on CentOS 7.x

Node.Js

Node.js is a cross-platform, open-source JavaScript library for server-side, contrary to previous practice of using JavaScript primarily for client-side scripting by embedding the scripts in a webpage’s HTML. Node.js provides asynchronous I/O capability out of the box with it’s event-driven architecture. Node.js has been adopted by major corporations, including IBM, LinkedIn, Microsoft, Netflix, Paypal, GoDaddy, Walmart, Cisco systems, a lot more.

npm

NPM is the package management utility for the JavaScript programming language i.e. Node.js, Angular. It provides a command-line client for consuming and distributing the JavaScript modules from the remote registry . Easiest (most common way) to install npm is installing the Node.js, it has it as the default package manager. Currently npm registry has 347,184 packages, and there’s no approval process for submission of the package, you have to check the number of downloads and depending packages as a assurance of a good quality module. For more you can check the npm page.

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Apache Spark and PySpark on CentOS/RHEL 7.x

What is Apache Spark

You may have noticed, wherever there is a talk about big data the name Apache Spark eventually comes up, in simplest words it’s a large-scale data processing engine. Apache Spark is a fast data processing framework with provided APIs to connect and perform big data processing. Spark being the largest open-source data processing engine, has been adopted by large companies – Yahoo, eBay, Netflix, have massive scale Spark deployments, processing multiple petabytes of data on clusters of over 8,000 nodes.
Apache Spark can be started as a standalone cluster (which we’ll be doing for this tutorial), or using Mesos or YARN as cluster managers. Spark can work with data from various sources, AWS S3, HDFS, Cassandra, Hive (structured data), HBase, or any other Hadoop data source. Above all what makes Spark high in-demand is the included libraries MLib, SQL and DataFrames, GraphX, and Spark Streaming, to cater the main data processing use-cases, such that users can combinely use all these libraries in the same application.

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Ruby Kernel for Jupyter Notebook

Jupyter notebooks are nice way to keep your code, diagrams, documentation together, mostly in a single file, which is also executable i.e. can run/interpret your code in it, and also have the result saved as it is. Here’s blogpost for installing Jupyter Notebook – today I’ll share how to use Ruby kernel with Jupyter Notebook i.e. executing Ruby code inside the notebooks.
To create notebooks that can execute Ruby code we need to integrate Ruby kernel, the 3 simple steps are:
  • Install Jupyter
  • Install Ruby
  • Install iruby

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Install latest Ruby version using rbenv

Ruby is a dynamic, open source programming language with a focus on simplicity and productivity. Yukihiro “Matz” Matsumoto created it in the mid-1990s, using his influence from other prpgramming languages i.e. Perl, Ada, Lips, Eiffel, and Smalltalk. Ruby was released in 1995. Like Python (released few years earlier), ruby also has dynamic typing and implicit memory management

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Install Jupyter Notebook

What is Jupyter Notebook

If you’re a Python developer, or someone who has to interact with Python, you may be hearing or seeing the term Jupyter Notebook quite lot, while reading articles, or looking for some solution on-line.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

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