Khaitan

Khaitan

1567828287

The Complete Guide to Webpack 4

Originally published by Esakkimuthu E at https://medium.com

Webpack is a module bundler. In other words, Webpack takes a bunch of different assets or files (like CSS, JS, SASS, JPG, SVG, PNG …) and it combines them into bundles, a separate bundle for each type.

Webpack Setup

We use npm to create a package.json file in a project directory:

npm init

Then we install the webpack by running this command in the terminal:

npm install --save-dev webpack webpack-dev-server webpack-cli

Note: save-dev saves the package as development dependencies, -save saves application dependencies it writes the installed packages into dependencies field in package.json were save-dev writes into devDependencies.

Once package.json is created edit the script part to make a webpack start. The package.json file will look like this:

{

   “name”: “projectName”,

   “version”: “1.0.0”,

   “private”: true,

   “description”: “”,

   “main”: “index.js”,

   “scripts”: {

      “start” : “webpack”

   },

   “keywords”: [],

   “author”: “”,

   “license”: “ISC”,

   “devDependencies”: {

      “webpack”: “^4.2.6”,

      “webpack-cli”: “^3.2.3”

   }

}

Now we can start our application using npm start. When the application starts webpack will search for index.js inside the /src directory. Make sure you have index.js within the /src directory. Webpack bundles index.js and its dependencies into a single file and the bundle file will be created as main.js within /dist directory. We need to add our main.js into index.html.

Webpack reads the entry point and analyses its dependencies and so on. It bundles the entry point and all its dependencies into a single file.

Configuring Webpack

Webpack 4 is a zero-configuration module bundler. It has a set of defaults. If we want to create a configuration file add webpack.config.js in the root directory.

Entry

Webpack needs an entry point. We’re just re-creating what we already have — /src/index.js nothing new for us. We’re explicitly telling webpack to look index.js in the /src directory.

Webpack.config.js

module.exports = {

   entry: ‘./src/index.js’

};

We can have more than one entry point. We will discuss this later.

Output

Where Webpack should bundle our output, /dist/main.js, is the default path. We can rename the output filename and put ‘filename’ inside the output. Path determines where the code split out.

Webpack.config.js

const path = require(‘path’);

module.exports = {

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   }

};

Now edit the script part in the package.json file like this:

“script” : {

   “start” : “webpack --config webpack.config.js”

}

Mode

Mode development will not minify the output file. The bundled output file will be readable for development.

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   }

};

Loaders

Webpack does not only bundle JavaScript files, as we’ve already discussed, it bundles images and other static files as well. Loaders are the key to bundle different types of files — you can see the list of loaders here.

CSS loaders

We can define a loader with an array of rules within the module object. Now create main.css in our project and install style-loader and css-loader using this command:

npm install --save-dev style-loader css-loader

Now update the webpack.config.js as below:

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   module:{

      rules:[

         {

            test: /.css$/,

            use: [“style-loader”, “css-loader”]}]

   }

};

Webpack reads loader from the reverse. It executes css-loader first, then style-loader where css-loader converts the css files into common JavaScript code and style-loader injects the styles into DOM.

For sass, we need to use a sass-loader, the sass-loader requires node-sass. Install a sass-loader using the below command.

npm install sass-loader node-sass webpack --sav-dev

Now update the webpack.config.js file with a sass-loader.

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   module:{

      rules:[

         {

            test: /.css$/,

            use: [

               “style-loader”, // Injects style into DOM

               “css-loader”,   // Turns CSS into JS

               “sass-loader”   // Turns SCSS into CSS

            ]

         }

      ]

   }

};

Don’t forget to import our main.css/main.scss into index.html and the style defined in main.css/main.scss will inject into Html DOM. Now we need to make it in standalone CSS.

Cache Busting and Plugins

The browser can cache our code so that every time you make a change the file cannot be re-downloaded by the user. In our case, main.js will download when a change occurs in our code. We can use a special hashing function that changes the file name to main.blablabla.js

Next time we build without any changes in the code the filename remains the same: main.balbalabla.js. But if even a single character changes in the code, the filename changes into main.vnas28r9ysd.js and the browser has to download it again.

This is very useful when we add vendor code in our project. We won’t need to download the vendor code every time, because we don’t always make any changes in vendor files and the file can be cached in the browser.

output : {

   filename : “main.[contentHash].js”

}

Now the problem is how to link this file into index.html. We can’t predict the dynamically generated filename, for the webpack is going to do this. Webpack creates the scripts tag at the bottom of our Html using plugins.

Plugins

The plugins option is used to customize the webpack build process in a variety of ways. You can see the list of plugins here.

Html webpack plugin

The HtmlWebpackPlugin simplifies the creation of HTML files to serve your webpack bundles. This is especially useful for webpack bundles that include a hash in the filename which changes every compilation.

npm install --save-dev html-webpack-plugin

webpack.config.js

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   plugins: [

      new HtmlWebpackPlugin()

   ]

};

This creates an index.html file for us and drops it in the dist directory with dynamically generated JS file is included at the bottom of the Html.

index.html

<!DOCTYPE html>

<html>

<head>

   <meta charset=”UTF-8">

   <title>Webpack App</title>

</head>

<body>

   <script type=”text/javascript” src=”main.170skb99wrjcb.js”></script>

</body>

</html>

This newly generated Html file doesn’t have our Html code. For this, we need to supply our template. Then the webpack config file looks like this:

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   plugins: [

      new HtmlWebpackPlugin({

         template: ‘./src/index.html’

      })

   ]

};

We used a css-loader to inject our css into DOM, actually, we need stand-alone css. To do that use the mini-css-extract-plugin:

npm install mini-css-extract-plugin

This plugin extracts css into a separate file.

webpack.config.js

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   module:{

      rules:[

         {

            test: /.css$/,

            use: [

               MiniCssExtractPlugin.loader,

               “style-loader”, // Injects style into DOM

               “css-loader”,   // Turns CSS into JS

               “sass-loader”   // Turns SCSS into CSS

            ]

         }

      ]

   },

   plugins: [

      new HtmlWebpackPlugin({

         template: ‘./src/index.html’

      }),

      new MiniCssExtractPlugin()

   ]

};

The newly generated css file will be automatically injected into Html — HtmlWebpackPlugin will take care of it.

Splitting Code Dev and Production Mode

This mode is a parameter that webpack 4 introduced. We’re going to split the webpack configuration into development mode and production mode. At the same time, they share a lot of commonly used things. So, we have three config files: the common file for sharing dev and prod mode, the second for development mode, and the final for production mode.

We’re not going to build dist directory for dev mode — webpack-dev-server will help us to load the files from memory. We only need to minify the final version of the code for production mode.

Create two more files: webpack.dev.js and webpack.prod.js. Rename webpack.config.js to webpack.common.js. These names are up to you — use whatever name you want.

Now we install the required npm packages:

npm install --save-dev webpack-dev-server

Webpack-dev-server provides live reloading. It also provides fast in-memory access to the webpack assets. This should be used for dev mode.

npm install --save-dev webpack-merge

Webpack-merge provides a merge function that concatenates arrays and merges objects creating a new object. It returns wrapped values within a function again.

Now we split out the code for dev and prod modes. For common configurations, we don’t need mode parameter and output. We don’t want to add content hash for development mode either. The final version of all three files will look like this:

webpack.common.js

const path = require(“path”);

var HtmlWebpackPlugin = require(“html-webpack-plugin”); module.exports = {

   entry: “./src/index.js”,

   plugins: [

      new HtmlWebpackPlugin({

         template: “./src/template.html”

      })

   ],

   module: {

      rules: [

         {

            test: /.scss$/,

            use: [

               “style-loader”, // Inject styles into DOM

               “css-loader”,   // Turns css into commonjs

               “sass-loader”   // Turns sass into css

            ]

         }

      ]

   }

};

webpack.dev.js

const path = require(“path”);

const common = require(“./webpack.common”);

const merge = require(“webpack-merge”);module.exports = merge(common, {

   mode: “development”,

   output: {

      filename: “main.js”,

      path: path.resolve(__dirname, “dist”)

   }

});

webpack.prod.js

const path = require(“path”);

const common = require(“./webpack.common”);

const merge = require(“webpack-merge”);module.exports = merge(common, {

   mode: “production”,

   output: {

      filename: “main.[contentHash].js”,

      path: path.resolve(__dirname, “dist”)

   }

});

Now we have to configure the webpack-dev-server. We also need to add development mode and production mode on our package.json. Edit the script part in the package.json like this:

“script” : {

   “start”: “webpack-dev-server --config webpack.dev.js --open”,

   “build”: “webpack --config webpack.prod.js”

}

Use npm start for development mode and npm run build for production mode. Now you can see bundled minified files for production mode inside the build directory. In-memory changes for dev mode. -open will automatically open the browser window when it’s ready.

Oops, we need to saw multiple entry points! Don’t worry, it’s not difficult to set up — we have vendor scripts like bootstrap. We need two separate bundles for main.js and vendor.js. Then the entry point in webpack.common.js will look like this:

module.exports = {

   entry: {

      main: “./src/index.js”,

      vendor: “./src/vendor.js”

   }

}

We also have to make changes in output to generate two different bundles: config files for dev and production mode.

webpack.dev.js

module.exports = merge(common, {

   mode: “development”,

   output: {

      filename: “[name].bundle.js”,

      path: path.resolve(__dirname, “dist”)

   }

});

webpack.prod.js

module.exports = merge(common, {

   mode: “production”,

   output: {

      filename: “[name].[contentHash].js”,

      path: path.resolve(__dirname, “dist”)

   }

});

This generates two different bundles in dev and production mode. You can see the bundles main.bundle.js and vendor.bundle.js in development mode. In production mode main.275hsossba83k.js and vendor.934jsdpaczl83.js.

Conclusion

We also use images and other types of assets in our projects. For this we need to add the required packages to webpack, otherwise you will get the error “You may need an appropriate loader to handle this file type” in webpack.

Thanks for reading

If you liked this post, share it with all of your programming buddies!

Follow us on Facebook | Twitter

Further reading

JavaScript Infrastructure: CLI, NPM, Babel 7 and Webpack 4

Webpack 4.30 Tutorial for Beginners 🚀 🚀 🚀

Best JavaScript Frameworks, Libraries and Tools to Use in 2019

#webpack #javascript #web-development

What is GEEK

Buddha Community

The Complete Guide to Webpack 4
Khaitan

Khaitan

1567828287

The Complete Guide to Webpack 4

Originally published by Esakkimuthu E at https://medium.com

Webpack is a module bundler. In other words, Webpack takes a bunch of different assets or files (like CSS, JS, SASS, JPG, SVG, PNG …) and it combines them into bundles, a separate bundle for each type.

Webpack Setup

We use npm to create a package.json file in a project directory:

npm init

Then we install the webpack by running this command in the terminal:

npm install --save-dev webpack webpack-dev-server webpack-cli

Note: save-dev saves the package as development dependencies, -save saves application dependencies it writes the installed packages into dependencies field in package.json were save-dev writes into devDependencies.

Once package.json is created edit the script part to make a webpack start. The package.json file will look like this:

{

   “name”: “projectName”,

   “version”: “1.0.0”,

   “private”: true,

   “description”: “”,

   “main”: “index.js”,

   “scripts”: {

      “start” : “webpack”

   },

   “keywords”: [],

   “author”: “”,

   “license”: “ISC”,

   “devDependencies”: {

      “webpack”: “^4.2.6”,

      “webpack-cli”: “^3.2.3”

   }

}

Now we can start our application using npm start. When the application starts webpack will search for index.js inside the /src directory. Make sure you have index.js within the /src directory. Webpack bundles index.js and its dependencies into a single file and the bundle file will be created as main.js within /dist directory. We need to add our main.js into index.html.

Webpack reads the entry point and analyses its dependencies and so on. It bundles the entry point and all its dependencies into a single file.

Configuring Webpack

Webpack 4 is a zero-configuration module bundler. It has a set of defaults. If we want to create a configuration file add webpack.config.js in the root directory.

Entry

Webpack needs an entry point. We’re just re-creating what we already have — /src/index.js nothing new for us. We’re explicitly telling webpack to look index.js in the /src directory.

Webpack.config.js

module.exports = {

   entry: ‘./src/index.js’

};

We can have more than one entry point. We will discuss this later.

Output

Where Webpack should bundle our output, /dist/main.js, is the default path. We can rename the output filename and put ‘filename’ inside the output. Path determines where the code split out.

Webpack.config.js

const path = require(‘path’);

module.exports = {

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   }

};

Now edit the script part in the package.json file like this:

“script” : {

   “start” : “webpack --config webpack.config.js”

}

Mode

Mode development will not minify the output file. The bundled output file will be readable for development.

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   }

};

Loaders

Webpack does not only bundle JavaScript files, as we’ve already discussed, it bundles images and other static files as well. Loaders are the key to bundle different types of files — you can see the list of loaders here.

CSS loaders

We can define a loader with an array of rules within the module object. Now create main.css in our project and install style-loader and css-loader using this command:

npm install --save-dev style-loader css-loader

Now update the webpack.config.js as below:

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   module:{

      rules:[

         {

            test: /.css$/,

            use: [“style-loader”, “css-loader”]}]

   }

};

Webpack reads loader from the reverse. It executes css-loader first, then style-loader where css-loader converts the css files into common JavaScript code and style-loader injects the styles into DOM.

For sass, we need to use a sass-loader, the sass-loader requires node-sass. Install a sass-loader using the below command.

npm install sass-loader node-sass webpack --sav-dev

Now update the webpack.config.js file with a sass-loader.

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   module:{

      rules:[

         {

            test: /.css$/,

            use: [

               “style-loader”, // Injects style into DOM

               “css-loader”,   // Turns CSS into JS

               “sass-loader”   // Turns SCSS into CSS

            ]

         }

      ]

   }

};

Don’t forget to import our main.css/main.scss into index.html and the style defined in main.css/main.scss will inject into Html DOM. Now we need to make it in standalone CSS.

Cache Busting and Plugins

The browser can cache our code so that every time you make a change the file cannot be re-downloaded by the user. In our case, main.js will download when a change occurs in our code. We can use a special hashing function that changes the file name to main.blablabla.js

Next time we build without any changes in the code the filename remains the same: main.balbalabla.js. But if even a single character changes in the code, the filename changes into main.vnas28r9ysd.js and the browser has to download it again.

This is very useful when we add vendor code in our project. We won’t need to download the vendor code every time, because we don’t always make any changes in vendor files and the file can be cached in the browser.

output : {

   filename : “main.[contentHash].js”

}

Now the problem is how to link this file into index.html. We can’t predict the dynamically generated filename, for the webpack is going to do this. Webpack creates the scripts tag at the bottom of our Html using plugins.

Plugins

The plugins option is used to customize the webpack build process in a variety of ways. You can see the list of plugins here.

Html webpack plugin

The HtmlWebpackPlugin simplifies the creation of HTML files to serve your webpack bundles. This is especially useful for webpack bundles that include a hash in the filename which changes every compilation.

npm install --save-dev html-webpack-plugin

webpack.config.js

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   plugins: [

      new HtmlWebpackPlugin()

   ]

};

This creates an index.html file for us and drops it in the dist directory with dynamically generated JS file is included at the bottom of the Html.

index.html

<!DOCTYPE html>

<html>

<head>

   <meta charset=”UTF-8">

   <title>Webpack App</title>

</head>

<body>

   <script type=”text/javascript” src=”main.170skb99wrjcb.js”></script>

</body>

</html>

This newly generated Html file doesn’t have our Html code. For this, we need to supply our template. Then the webpack config file looks like this:

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   plugins: [

      new HtmlWebpackPlugin({

         template: ‘./src/index.html’

      })

   ]

};

We used a css-loader to inject our css into DOM, actually, we need stand-alone css. To do that use the mini-css-extract-plugin:

npm install mini-css-extract-plugin

This plugin extracts css into a separate file.

webpack.config.js

const path = require(‘path’);

module.exports = {

   mode: “development”,  

   entry: ‘./src/index.js’,

   output: {

      path: path.resolve(__dirname, ‘dist’),

      filename: ‘main.js’

   },

   module:{

      rules:[

         {

            test: /.css$/,

            use: [

               MiniCssExtractPlugin.loader,

               “style-loader”, // Injects style into DOM

               “css-loader”,   // Turns CSS into JS

               “sass-loader”   // Turns SCSS into CSS

            ]

         }

      ]

   },

   plugins: [

      new HtmlWebpackPlugin({

         template: ‘./src/index.html’

      }),

      new MiniCssExtractPlugin()

   ]

};

The newly generated css file will be automatically injected into Html — HtmlWebpackPlugin will take care of it.

Splitting Code Dev and Production Mode

This mode is a parameter that webpack 4 introduced. We’re going to split the webpack configuration into development mode and production mode. At the same time, they share a lot of commonly used things. So, we have three config files: the common file for sharing dev and prod mode, the second for development mode, and the final for production mode.

We’re not going to build dist directory for dev mode — webpack-dev-server will help us to load the files from memory. We only need to minify the final version of the code for production mode.

Create two more files: webpack.dev.js and webpack.prod.js. Rename webpack.config.js to webpack.common.js. These names are up to you — use whatever name you want.

Now we install the required npm packages:

npm install --save-dev webpack-dev-server

Webpack-dev-server provides live reloading. It also provides fast in-memory access to the webpack assets. This should be used for dev mode.

npm install --save-dev webpack-merge

Webpack-merge provides a merge function that concatenates arrays and merges objects creating a new object. It returns wrapped values within a function again.

Now we split out the code for dev and prod modes. For common configurations, we don’t need mode parameter and output. We don’t want to add content hash for development mode either. The final version of all three files will look like this:

webpack.common.js

const path = require(“path”);

var HtmlWebpackPlugin = require(“html-webpack-plugin”); module.exports = {

   entry: “./src/index.js”,

   plugins: [

      new HtmlWebpackPlugin({

         template: “./src/template.html”

      })

   ],

   module: {

      rules: [

         {

            test: /.scss$/,

            use: [

               “style-loader”, // Inject styles into DOM

               “css-loader”,   // Turns css into commonjs

               “sass-loader”   // Turns sass into css

            ]

         }

      ]

   }

};

webpack.dev.js

const path = require(“path”);

const common = require(“./webpack.common”);

const merge = require(“webpack-merge”);module.exports = merge(common, {

   mode: “development”,

   output: {

      filename: “main.js”,

      path: path.resolve(__dirname, “dist”)

   }

});

webpack.prod.js

const path = require(“path”);

const common = require(“./webpack.common”);

const merge = require(“webpack-merge”);module.exports = merge(common, {

   mode: “production”,

   output: {

      filename: “main.[contentHash].js”,

      path: path.resolve(__dirname, “dist”)

   }

});

Now we have to configure the webpack-dev-server. We also need to add development mode and production mode on our package.json. Edit the script part in the package.json like this:

“script” : {

   “start”: “webpack-dev-server --config webpack.dev.js --open”,

   “build”: “webpack --config webpack.prod.js”

}

Use npm start for development mode and npm run build for production mode. Now you can see bundled minified files for production mode inside the build directory. In-memory changes for dev mode. -open will automatically open the browser window when it’s ready.

Oops, we need to saw multiple entry points! Don’t worry, it’s not difficult to set up — we have vendor scripts like bootstrap. We need two separate bundles for main.js and vendor.js. Then the entry point in webpack.common.js will look like this:

module.exports = {

   entry: {

      main: “./src/index.js”,

      vendor: “./src/vendor.js”

   }

}

We also have to make changes in output to generate two different bundles: config files for dev and production mode.

webpack.dev.js

module.exports = merge(common, {

   mode: “development”,

   output: {

      filename: “[name].bundle.js”,

      path: path.resolve(__dirname, “dist”)

   }

});

webpack.prod.js

module.exports = merge(common, {

   mode: “production”,

   output: {

      filename: “[name].[contentHash].js”,

      path: path.resolve(__dirname, “dist”)

   }

});

This generates two different bundles in dev and production mode. You can see the bundles main.bundle.js and vendor.bundle.js in development mode. In production mode main.275hsossba83k.js and vendor.934jsdpaczl83.js.

Conclusion

We also use images and other types of assets in our projects. For this we need to add the required packages to webpack, otherwise you will get the error “You may need an appropriate loader to handle this file type” in webpack.

Thanks for reading

If you liked this post, share it with all of your programming buddies!

Follow us on Facebook | Twitter

Further reading

JavaScript Infrastructure: CLI, NPM, Babel 7 and Webpack 4

Webpack 4.30 Tutorial for Beginners 🚀 🚀 🚀

Best JavaScript Frameworks, Libraries and Tools to Use in 2019

#webpack #javascript #web-development

I am Developer

1611112146

Codeigniter 4 Autocomplete Textbox From Database using Typeahead JS - Tuts Make

Autocomplete textbox search from database in codeigniter 4 using jQuery Typeahead js. In this tutorial, you will learn how to implement an autocomplete search or textbox search with database using jquery typehead js example.

This tutorial will show you step by step how to implement autocomplete search from database in codeigniter 4 app using typeahead js.

Autocomplete Textbox Search using jQuery typeahead Js From Database in Codeigniter

  • Download Codeigniter Latest
  • Basic Configurations
  • Create Table in Database
  • Setup Database Credentials
  • Create Controller
  • Create View
  • Create Route
  • Start Development Server

https://www.tutsmake.com/codeigniter-4-autocomplete-textbox-from-database-using-typeahead-js/

#codeigniter 4 ajax autocomplete search #codeigniter 4 ajax autocomplete search from database #autocomplete textbox in jquery example using database in codeigniter #search data from database in codeigniter 4 using ajax #how to search and display data from database in codeigniter 4 using ajax #autocomplete in codeigniter 4 using typeahead js

A Complete Guide to the Stages of Penetration Testing

Different Stages of Penetration Tests

The typical penetration testing is broken out in various phases, alike the cyberattack lifecycle. Every single phase has a goal that they require to achieve to further the attack.

  1. Gathering of Crucial Information
  2. Enumeration & Identification
  3. Vulnerability Scanning
  4. Determines the best method of attack
  5. Penetration as well as Exploitation
  6. Risk Analysis as well as Recommendations
  7. Report Preparation (Goals)

#testing #penetration #penetration testing guide #a complete guide

Lisa joly

Lisa joly

1624089840

Big Data Resume: Complete Guide & Samples [2021]

Thanks to the rapidly piling amounts of Big Data, the job profile of a Big Data Engineer is peaking.

In recent years, there has been such unprecedented growth in the demand for Big Data Engineers that it has become one of the top-ranking jobs in Data Science today. Since numerous companies across different industries are hiring Big Data Engineers, there’s never been a better time than now to build a career in Big Data. However, you must know how to present yourself as different from the others; you need to stand out from the crowd. Read the blog to have a better understanding of the scope of Big Data in India.

And how will you do that?

By designing and crafting a detailed, well-structured, and eye-catching Big Data resume!

When applying for a Big Data job, or rather for the post of a Big Data Engineer, your resume is the first point of contact between you and your potential employer. If your resume impresses an employer, you will be summoned for a personal interview. So, the key is to make sure you have a fantastic resume that can get you job interview calls.

Usually, Hiring Managers have to look at hundreds of resumes, be it for any job profile. However, when it comes to high-profile jobs like that of the Big Data Engineer, you must be able to grab the attention of the Hiring Manager by highlighting your skills, qualifications, certifications, and your willingness to upskill.

Let’s begin the resume-building process with the job description and key roles and responsibilities of a Big Data Engineer.

Table of Contents

#big data #big data resume: complete guide & samples #big data resume #big data resume #data science resume #guide

Dylan  Iqbal

Dylan Iqbal

1630408920

Big Data Visualization: What, Why, Tips and Tools

Wondering what is big data visualization and how you can apply it for your business? Here's a guide to help you get started.

Because we live in a data-driven society, it’s likely that you’re constantly bombarded with complex sets of data that you need to transmit to your coworkers in an easy-to-grasp way.

The challenge is that almost no one wants to look at large lists of numbers and data, and important information can be easily lost within the midst of chaotic spreadsheets. But there is a solution, and that is big data visualization.

Today, we’ll be covering what big data visualization is and why it’s important, different big data visualization techniques you can use, tips and tricks for creating easily intelligible large data sets and the best big data visualization tools you can use.

By the end of this article, you’ll feel like a real data scientist and be competent in creating pie charts, bar charts, heat maps, histograms, interactive charts and more for big data visualization.

So let’s get into it, shall we?

Table of Contents

What is Big Data Visualization?

Why is Data Visualization Important in Big Data?

What Are the Types of Big Data Visualization?

5 Big Data Visualization Tips for Beginners

4 Tools for Big Data Visualization

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What is Big Data Visualization?

Big data visualization is the representation of large sets of data through visual aids, whether that be through pie charts, heat maps, bar charts or any other kind of chart types or visual representation.

Analyzing and understanding large data sets and data analytics is no easy task and it can be especially difficult trying to relay that same information to colleagues who are not data-driven or data scientists.

That’s where big data visualization comes in. By transforming your large data sets into visually appealing infographics or interactive charts, you can easily convey your data points to fellow decision-makers.

When your data is plotted out on graphs in a visual way and metrics are made easily readable, no data gets lost in the mix, no matter how large or small, and it makes decision-making for the future a breeze.

Because you can’t make adequate decisions or advance significantly without analyzing your raw data, it’s important that companies use great data visualization methods to keep everyone in the loop.

Let’s take BMW for example.

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In 2020, BMW was able to track the number of sales for electric cars that they had and then compare it to other car companies’ sales, but not only.

They also were able to track the countries that bought the highest amount of their electric cars.

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This is a prime example of big data visualization in action. When you track your analytics and data, you can see where your wins are and when to celebrate or where your losses are and how you can make adjustments for the future.

Now, imagine for a moment that all this information was just written out plainly on a spreadsheet and had unstructured data all over it.

It would be hard to understand and assess how the company is doing and would take a long time to communicate to employees how their work has affected the sales of the cars.

This is why visualizing big data is so important. With just a glance and within seconds, you can easily see what cars are selling best and in what countries.

No time is wasted going through spreadsheets and trying to make sense of unstructured data — just visual analytics laid out for all to see and understand.

 

Why is Data Visualization Important in Big Data?

We live in a time where the internet and social media have exploded at an extraordinary rate, and information can be gathered within seconds and at the tips of anyone’s fingers.

With the rise of this technological era, it’s important that data can be visualized and consumed quickly and efficiently — especially since the human brain now has an attention span of about 8 seconds, according to this study by the Technical University of Denmark.

Because companies, businesses and organizations can gather data more quickly than ever, this means that they need to be able to visualize that data in an equally quick and easily consumable way.

The best way to efficiently communicate your ever-coming, new data is through visualizing big data. This will bring your complex data to life and anyone who looks at it will be able to understand and grasp it with just a glance.

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Take the image above as an example. With just a quick look at the statistics that are clearly visualized, you can make a data-based assumption.

Now, imagine if this data was just written out plainly on a spreadsheet. It would take much longer to understand and make an assumption based on the numbers.

By using big data visualization techniques, you’ll be able to get the most value from your data and analytics and make sure that everyone who says your data analysis will be able to interpret, understand and use your data. This, in turn, will help your company excel.

When you use data visualization techniques, it will optimize your use of data, help decision making and planning go smoothly, you’ll be able to identify and mitigate risk, extract loads of useful data and insights and improve your overall strategy and direction of your company.

There are no losses to using a visual representation of data, only wins. But there are lots of different types of data visualization that you can use.

Let’s discuss the different types of big data visualization and assess which one will work best for you.

 

What Are the Types of Big Data Visualization?

There are lots of different types of data visualization that data analysts like to use and depending on the amount of data. A data analyst may choose to use a pie chart to express their numerical data or a bar chart.

When looking at big data analytics regarding locations, one might choose to use an interactive heat map or maybe a pivot table.

We’re going to look at 8 common types of big data visualization and some data visualization examples for each to help you decipher which one will work best for you.

 

Type #1: Line Charts

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A line chart, also known as a line graph, is a graphic representation of data that plots a fixed value on one side and a variable on the other.

A line chart is a fantastic way to represent the relationship of data. You can use a line chart to represent changes and fluctuations of things within a certain period of time.

 

Type #2: Bar Charts

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A bar chart, also known as a bar graph, uses bars to compare different data points or data sets.

Many data scientists will use bar charts to visually represent their data analysis. You can use a bar chart to compare large amounts of data, fluctuations of quantities or different categories.

The taller the bar, the larger the numerical value and vice versa.

 

Type #3: Pie Charts

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Pie charts, donut charts, circle graphs or whatever you choose to call them, are representations of data that are split into smaller segments and sizes to represent their numerical value.

When you use a pie chart, it becomes easy to see and compare how the different segments relate and differ from each other.

When using a pie chart, try not to overload it with too many different values. When you split the pie chart into more than 7 segments, it can become difficult to understand the data.

 

Type #4: Heat Maps

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A heat map is a visual representation of data that is laid out on a map or table and uses different nuances and intensities of colors to represent its data.

Using a heat map can be especially helpful when you need to analyze data that seems to be never-ending. When you have an extremely wide value range, using a heat map makes it much more simple to quickly visualize and analyze large amounts of complex data at a glance.

 

Type #5: Histograms

histogram - weights of newborns

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A histogram is a graphical and visual representation of complex data sets and the frequency of said numerical data displayed through bars.

Histograms are very similar to bar graphs but vary in the fact that they mostly focus on the repeated frequency of numerical data.

Type #6: Scatter Plots

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A scatter plot, scatter chart or scatter graph, is a diagram that uses dots to represent and emphasize the different values of two or more numeric variables on an X and Y-axis.

Scatter plots are extremely useful to use when you have multiple large data sets and you want to know how they relate to each other and compare the importance of each value.

 

Type #7: Treemaps

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Treemaps are the visual representation of hierarchical data by using color-coded rectangles.

Users can use treemaps as a method to compare multiple sets of data and reflect the weight of each value in a project.

 

Type #8: Funnel Charts

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Funnel charts are typically used in sales and represent the different stages that your users or customers go through during the sales process and demonstrate decreasing values as they move through your funnel.

By using a funnel chart, you can accurately see where you are losing or gaining your customers during the sales process.

 

5 Big Data Visualization Tips for Beginners

Now that we’ve covered what big data visualization is, its importance and 9 different types of data visualization, you may feel like you’re a professional in data science.

Now that you’re familiar with the basics of data visualization, it’s time that we equip you with some of our best data visualization techniques.

Here are our top 5 best data visualization techniques for you to use when creating a visual representation of your data.

 

Tip #1: Use a Powerful Data Visualization Tool

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You can’t create powerful graphs without a powerful data visualization tool.

Sure, you could use something like Google charts, but to create unique, engaging charts, you’ll want to use a data visualization tool like Visme that's packed with amazing functionality.

Visme is a powerful data visualization tool with many integration functionalities. As you can see in the image above, you can create everything from funnel charts and tables to interactive data maps and graphs in this editor.

When you need to visualize big data, Visme is the way to go. When you create a graph in our big data visualization tool, your data can be updated in real-time with our integration tools.

You can import all your data from Google Sheets, Microsoft Excel, Google Analytics and other data sources, then see it come to life automatically on your project while you sit back and relax.

Visme also has many open-source elements and graphics for you to use to keep your infographic intriguing. To have the perfect interactive data visualization, you can use word clouds, tables, treemaps, animated characters and graphic design elements and more to implement into your design.

They’re also a powerhouse filled with lots of useful and educational tutorials on how to create the perfect chart for your raw data. Visme also has lots of tutorials for all things graphic design.

So why not use a tool that has everything you need for creating visuals for your data analysis and tons of tutorials to go with it? You can start your free account with Visme today and start living out your data analyst dreams.

It’s important to use a strong data visualization software for your data analysis and presentations. Stick around and soon we’ll get into our list of best tools for big data visualization.

 

Tip #2: Pick the Correct Form of Big Data Visualization

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When it comes to visualizing your data, you need to make sure that you choose the correct chart type.

Because there are so many different ways to display your data, you need to weigh out the cons and pros of each and find out which one will work best for your infographic or presentation.

Take for example pie charts and bar graphs.

When you analyze data that is very different, you might want to use a pie chart. But if you want to represent data entries that are close together, you could use a bar chart for that.

If you’re trying to create data visualization for sales, you could use a funnel chart, pyramid chart or cone chart for that.

Each different visualization method has its time and place, and you need to analyze your data and think about what method will work best for your respective data.

Refer above to the “Big Data Visualization Types” section above to see which one will suit you best.

 

Tip #3: Make Sure Your Data is Easily Comprehensible

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The whole point of big data visualization is to make it easy to understand at a glance.

It won’t be easily intelligible if you just start piling in large amounts of unstructured data and simply hope for the best. Or imagine you have tens of tiny little numbers on a bar graph that no one can see or read.

You need to make sure that anyone on your team, whether a data scientist or not, can understand what you’re trying to convey at a glance.

You can do this by using clear and bold text, contrasting font colors and background colors, not adding too many values to one chart and using compelling images to highlight your point, just like in the example above.

By adding too much text or too many values to a single graph, you risk confusing your audience even more. So keep it as simple and concise as possible.

 

Tip #4: Always Use Legends to Further Explain Your Data

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Using legends is absolutely vital for making your data easy to understand, so whether you’re creating a pie chart or bar graph, make sure you’re using a legend.

A legend is an area of your design that further explains each segment of your chart.

Many times people will assign a color to a segment in their chart, just like in the example above, and on the side add a little graphic element that explains what each color represents.

The legend is responsible for keeping the audience engaged and understanding everything you’re trying to convey.

 

Tip #5: Use Multiple Charts for Big Data

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If you have a large amount of data that needs to be conveyed to your team, try using multiple graphs to do so.

Incorporating tons of data into a single chart will only make it hard for the human brain to stay on track and focus to try and understand what you want to share.

The best rule of thumb to follow here is KISS — keep it simple, stupid.

So instead of simply adding all your data to one pie chart and making it have 30 pie slices, why not create multiple graphs and break it down into bite-sized pieces? Pun intended.

By creating multiple matching charts, you can keep your data easily intelligible, cohesive and right on brand.

Just like in the example above, you can clearly understand all the data that’s being displayed because it is written out on two different donut charts.

You want to make sure your information is understandable by anyone at a glance, and you can do so by breaking down your data.

 

4 Tools for Big Data Visualization

Now that you know essentially all there is to know about big data visualization, it’s time you choose a tool that will help you create those visuals.

We’ll be covering 4 data visualization software you can use to get the job done.

Let’s jump right into it.

 

Tool #1: Visme

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If you want to create compelling and professional data visualization, then you need a tool like Visme.

Visme makes it easy for both designers and non-designers alike to visualize their data in interactive and engaging ways.

For example, you can create incredible animated charts, add your own audio files to them that you can record right within the editor, add tons of professionally design data widgets and import all your data from third-party websites such as Microsoft Excel, Google sheets etc.

The best part? You can save endless amounts of time and effort by using one of our hundreds of customizable templates for displaying your data.

Simply scroll through tons of professionally design templates for charts and data choose one that suits your style.

Everything can be customized on each and every template and you can even add your own brand colors, logo and font to keep everything right on-brand with your other designs.

Not only can you create loads of beautiful charts, graphs and infographics with Visme, but you can also create anything else design-related. You can create presentations, infographics, multi-page reports and proposals, branded social graphics and more.

If you’re looking for a powerful data visualization tool with high functionality for many other types of designs, Visme is the one for you. Plus, you can create a free account and use for as long as you like — no trial period or hidden costs!

 

Tip #2: Tableau

Tableau is an interactive data visualization software with a focus on business intelligence. Their goal is to help people make data that can be easily understood by anyone.

Tableau is a tool that is used in the business intelligence industry and it can help you simplify raw data into a simple format. With drag and drop functionalities, you can create data visualization fairly quickly and then share it with others.

In Tableau you can create lots of different data visualizations, from a correlation matrix to a simple bar graph.

Another plus for the software is that you can infuse the Tableau dashboard with artificial intelligence and machine learning from Aible.

You can start a free trial with Tableau, but it is a bit pricey after your trial is up. At $70/month billed annually, you’ll have to make sure you absolutely love the product before buying it.

 

Tool #3: Microsoft’s Power BI

Power BI by Microsoft is a business analytics service that helps you create interactive data visualizations.

Whether your data is on an Excel spreadsheet an on-premises hybrid data warehouses, Power BI will help you bring that data together to create reports and graphs to share with your team.

There are three versions of Power BI that you can use: the desktop app, the mobile app or their website.

You can use Power BI to help you visualize big data with your team by using some of their other popular apps like Microsoft Excel and work together in real-time to create compelling data.

Power BI has some basic templates that you can use to get a jump start on creating your data.

Power BI is quite affordable, coming in at $9.99/month.

If you’re not completely sold on using Power BI, let’s move on to our next tool.

 

Tool #4: Datawrapper

Datawrapper is an online tool that you can use to create data visualizations that are interactive and responsive, with no code or programming languages like python or javascript required.

With big users like the New York Times and the UN, they do have quite a few things to boast about.

Data wrapper is an open-source and easy-to-use data visualization software where you can create basic charts and graphs, maps and line charts that can be embedded into your website.

As for the price, you can use their free plan and create lots of charts, maps and tables, but they will be watermarked and there are a few other inconveniences that come with the free plan.

The next plan comes in at $599/month, which is definitely on the pricey side.

And that concludes our list of 4 tools for data visualization.

 

Now Over to You

If you want a data visualization software that will help you convey your data in a fun and engaging way, then you most likely will love using Visme.

Not only is Visme a powerful data visualization tool, but it’s so much more. You can use Visme to create all of your graphic design needs, from sales presentations to pitch decks, social media posts, infographics, videos, eBooks and more.

What are you waiting for? Create your free account today and free your inner data scientist.

Originally published at https://visme.co

#datavisualization #bigdata