Big Data vs Hadoop | Big Data and Hadoop Differences | Intellipaat

Big Data vs Hadoop | Big Data and Hadoop Differences | Intellipaat

🔥Intellipaat Big Data Hadoop Course: https://intellipaat.com/big-data-hadoop-training/ In this video on Big Data vs Hadoop you will understand about the Big ...

In this video on Big Data vs Hadoop you will understand about the Big Data and Hadoop differences which most of the people find confusing and how it is related to each other. So in this Hadoop vs Big Data comparison some important parameters have been taken into consideration to tell you the difference between Hadoop and Big Data in detail.

Why Hadoop is important

Big data hadoop is one of the best technological advances that is finding increased applications for big data and in a lot of industry domains. Data is being generated hugely in each and every industry domain and to process and distribute effectively hadoop is being deployed everywhere and in every industry.

Why Spark is important

Today there is a widespread deployment of Big Data. With each passing day the requirements of enterprises increases and therefore there is a need for a faster and more efficient form of processing data. Most of the data is in unstructured format and it is coming in thick and fast as streaming data.Apache Spark is seeing widespread demand with enterprises finding it increasingly difficult to hire the right professionals to take on increasingly challenging roles in real world scenarios. It is a fact that today the Apache Spark community is one of the fastest Big Data communities with over 750 contributors from over 200 companies worldwide.

Angular 9 Tutorial: Learn to Build a CRUD Angular App Quickly

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React Hooks - How to Creat an App with Embedded Analytics

React Hooks - How to Creat an App with Embedded Analytics

See how to use Hooks in React. How to Creat an App with Embedded Analytics. Hope this article will satisfy your intellectual curiosity in full and inspire to create your own analytical application that assists in making important business decisions.

You may have noticed that React Hooks, introduced in the React’s 16.8.0 release, have been received by the web dev community very diversely. Some warmly embraced this new way to reuse stateful logic between components, while some strongly criticized it. One thing can be said for sure — React Hooks are an incredibly hot topic now. This is confirmed by the number of articles, tutorials, video courses, and project samples on the subject.

My goal is to briefly introduce to you this powerful concept (if you are not familiar with it yet) and show how it can be applied to building a simple analytical app. Note that we’ll focus more on getting a hands-on experience rather than on debating on the pros and cons of using Hooks.

I do hope this article will satisfy your intellectual curiosity in full and inspire to create your own analytical application that assists in making important business decisions.

Why React Hooks

The central idea behind React Hooks is that you can use React’s features (like states and effects) from functions, without writing classes. Hooks are meant to help you arrange logic inside a component into separate pieces. Ergo, they present a powerful mechanism for code reuse.

Plus, there's no need to worry — you don’t have to rewrite your code from scratch and add hooks everywhere. React Hooks are an experimental and backward-compatible feature that are currently being adopted among web developers.

☝️ Now that we figured out what React Hooks are all about, let’s go ahead and try building an analytical app with React Hooks.

As a data analysis tool, I’ve chosen Flexmonster Pivot Table & Charts, since it provides a ready-to-use React wrapper based on React Hooks.

To make the process more engaging, we’ll explore the data set from Kaggle, namely Spanish High-Speed Rail tickets pricing - Renfe. To be precise, we’ll aggregate, filter, and sort the data in the pivot table. Afterward, we’ll visualize it with pivot charts. This kind of exploration will help us to capture trends in the data and get a basic understanding of working with a web pivot table. And we’ll do it all in our React app!

Results

After tutorial completion, we’ll end up with a dynamic web-based dashboard displaying metrics inside your React app:

Creating a React App and Adding Dependencies

The first step is completely straightforward. All you need to do is to download or clone the project from GitHub. At this point, you should select between ES6 or TypeScript versions of the project. Next, run npm install to download its dependencies.

Alternatively, you can build a single-page app from scratch using the create-react-app environment and go the full way of integrating it with Flexmonster.

If you prefer the first variant, let’s take a closer look at the structure of the sample project and a couple of JS components from the src folder that we'll be using.

  • App.js is the main JavaScript component that is responsible for setting routes of other components and running your app.
  • PivotTableHooks.js renders the instance of a pivot table using the functional approach with hooks.

I'd like to draw your attention to how easy it is to hook React features of Flexmonster into your functional component.

It's as simple as changing the import statement from

import * as FlexmonsterReact from 'react-flexmonster';

to

import * as FlexmonsterReact from 'react-flexmonster/hooks';

Afterward, you can use the pivot table within a function. You can also do this inside a class component, which is PivotTable.js in our sample.

In my mind, such a seamless approach corresponds to the very nature of hooks as a concept and speeds up the development process and helps to keep the code clear.

Note that PivotTable.js and PivotTableHooks.js can be used interchangeably, depending on the logic you like better — a class-based or with hooks.

Empowering the App With a Data Visualization Solution

Now, it’s time to figure out how to work with Flexmonster Pivot. First, we need to configure a report.

Flexmonster’s report has a clear JSON structure. It incorporates information about a data source, a slice (a subset of data that is currently displayed on the grid), visual and functional options, formatting, etc.

Open the PivotTableHooks.js file and define a variable that contains our JSON data:

const data = [
              {
                "insert_date": "2019-04-11 21:49:46",
                "origin": "MADRID",
                "destination": "BARCELONA",
                "start_date": "2019-04-18 05:50:00",
                "end_date": "2019-04-18 08:55:00",
                "train_type": "AVE",
                "price": 68.95,
                "train_class": "Preferente",
                "fare": "Promo"
              },
              {
                "insert_date": "2019-04-11 21:49:46",
                "origin": "MADRID",
                "destination": "BARCELONA",
                "start_date": "2019-04-18 06:30:00",
                "end_date": "2019-04-18 09:20:00",
                "train_type": "AVE",
                "price": 75.4,
                "train_class": "Turista",
                "fare": "Promo"
              },
// …
]

Next, create a variable that holds the report itself:

const report = {
    dataSource: {
        data: data
    },
    "slice": {
        "reportFilters": [{
                "uniqueName": "train_class"
            },
            {
                "uniqueName": "train_type"
            }
        ],
        "rows": [{
            "uniqueName": "origin"
        }],
        "columns": [{
                "uniqueName": "destination"
            },
            {
                "uniqueName": "fare"
            },
            {
                "uniqueName": "[Measures]"
            }
        ],
        "measures": [{
            "uniqueName": "price",
            "aggregation": "average"
        }],
        "expands": {
            "columns": [{
                "tuple": [
                    "destination.[madrid]"
                ]
            }]
        }
    }
};

As an alternative, we can simply pass a string with the report’s URL:

report = "URL-to-report"

Set the report in the pivot table:

<FlexmonsterReact.Pivot ref={ref} toolbar={true} width="100%" report={report} reportcomplete={onReportComplete}/>

To add charts, we need to create a separate instance of Flexmonster and make it display charts with our data. We can do it by creating one more report and setting options.

const report_charts = {
    dataSource: {
        data: data
    },
    "slice": {
        "reportFilters": [{
                "uniqueName": "train_class"
            },
            {
                "uniqueName": "destination"
            },
            {
                "uniqueName": "fare"
            }
        ],
        "rows": [{
            "uniqueName": "train_type"
        }],
        "columns": [{
            "uniqueName": "[Measures]"
        }],
        "measures": [{
            "uniqueName": "price",
            "aggregation": "average",
            "format": "currency"
        }]
    },
    "options": {
        "viewType": "charts",
        "chart": {
            "type": "pie"
        }
    },
    "formats": [{
        "name": "currency",
        "thousandsSeparator": ",",
        "decimalPlaces": 1,
        "currencySymbol": "€"
    }]
};
<FlexmonsterReact.Pivot ref={ref_charts} toolbar={true} width="100%" report={report_charts}/>

After, run npm start and open [http://localhost:3000/hooks](http://localhost:3000/hooks) in your browser.

Now the pivot table component is rendered and filled with the predefined data.

Or change the slice with the drag-and-drop feature:

And here’s what we’ve got:

A React Hooks application empowered with a custom reporting solution that is ready to be used in any app for tackling all kinds of data analysis challenges.

I encourage you to take some time experimenting with this embedded analytics solution to explore its full potential and discover new insights into the data.

You can also see the React project on GitHub. Any of your feedback or questions would be so much appreciated!

Thank for reading !

What NOT to Do in the Data Science Domains Industry

What NOT to Do in the Data Science Domains Industry

Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following: * Statistics *...

Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following:

  • Statistics
  • Visualization
  • Data mining
  • Machine learning
  • Pattern recognition
  • Data platform operations
  • Artificial intelligence
  • Programming
    Math and statistics
    Statistics and other math skills are essential in several phases of the data science project. Even in the beginning of data exploration, you'll be dividing the features of your data observations into categories:
  • Categorical
  • Numeric:
  • Discrete
  • Continuous
    Continuous values have an infinite number of possible values and use real numbers for the representation. In a nutshell, discrete variables are like points plotted on a chart, and a continuous variable can be plotted as a line.
    Another classification of the data is the measurement-level point of view. We can split data into two primary categories:
    Qualitative:
  • Nominal
  • Ordinal
  • Quantitative:
  • Interval
  • Ratio
    Nominal variables can't be ordered and only describe an attribute. An example would be the color of a product; this describes how the product looks, but you can't put any ordering scheme on the color saying that red is bigger than green, and so on. Ordinal variables describe the feature with a categorical value and provide an ordering system; for example Education—elementary, high school, university degree, and so on.

Visualizing the types of data
Visualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical. Different types of data have different ways of representation. When we talk about the categorical values, the ideal representation visuals would be these:

  • Bar charts
  • Pie charts
  • Pareto diagrams

Frequency distribution tables
A bar chart would visually represent the values stored in the frequency distribution tables. Each bar would represent one categorical value. A bar chart is also a baseline for a Pareto diagram, which includes the relative and cumulative frequency for the categorical values:

Bar chart representing the relative and cumulative frequency for the categorical values
If we'll add the cumulative frequency to the bar chart, we will have a Pareto diagram of the same data:

Pareto diagram representing the relative and cumulative frequency for the categorical values
Another very useful type of visualization for categorical data is the pie chart. Pie charts display the percentage of the total for each categorical value. In statistics, this is called the relative frequency. The relative frequency is the percentage of the total frequency of each category. This type of visual is commonly used for market-share

*Statistics *
A good understanding of statistics is vital for a data scientist. You should be familiar with statistical tests, distributions, maximum likelihood estimators, and so on. This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren't) a valid approach. Statistics is important for all types of companies, especially data-driven companies where stakeholders depend on your help to make decisions and design and evaluate experiments.

Machine learning
A very important part of data science is machine learning. 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.
**
Choosing the right algorithm**
When choosing the algorithm for machine learning, you have to consider numerous factors to properly choose the right algorithm for the task. It should not only be based on the predicted output: category, value, cluster, and so on, but also on numerous other factors, such as these:

  1. Training time
  2. Size of data and number of features you're processing
  3. Accuracy
  4. Linearity
  5. Number of possible parameters
    Training time can range from minutes to hours, depending not only on the algorithm but also on the number of features entering the model and the total amount of data that is being processed. However, a proper choice of algorithm can make the training time much shorter compared to the other. In general, regression models will reach the fastest training times, whereas neural network models will be on the other side of the training time length spectrum. Remember that developing a machine-learning model is iterative work. You will usually try several models and compare possible metrics. Based on the metrics captured, you'll fine-tune the models and run comparisons again on selected candidates and choose one model for operations. Even with more experience, you might not choose the right algorithm for your model at first, and you might be surprised that other algorithms can outperform the first chosen candidate, as shown:

Big data
Big data is another modern buzzword that you can find around the data management and analytics platforms. The big does not have to mean that the data volume is extremely large, although it usually is. learn more Data science online course
SQL Server and big data
Let's face reality. SQL Server is not a big-data system. However, there's a feature on the SQL Server that allows us to interact with other big-data systems, which are deployed in the enterprise. This is huge!
This allows us to use the traditional relational data on the SQL Server and combine it with the results from the big-data systems directly or even run the queries towards the big-data systems from the SQL Server. The answer to this problem is a technology called PolyBase: