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Big Data Tutorial

Home Data Science Data Science Tutorials Big Data Tutorial

Big Data Basics

Introduction to Big Data

Big Data Architecture

Big Data Concepts

Careers in Big Data

Is Big Data a Database?

Trends Of Big Data

Big Data Technologies

Big Data Programming Languages

Challenges of Big Data Analytics

Descriptive and Inferential Statistics

Mahout vs Spark

Why Innovation is The Most Critical Aspect of Big Data?

What is Big data and Hadoop

What Is NOSQL Skills?

Big Data Techniques

Big Data in Banking

Big Data Interview Questions

Big data and analytics

What is Big Data Analytics?

What is Data Analysis?

What is Data Analyst?

What is Data Analytics

Careers in Data Analytics

Data Analysis Process

Who is a Data Scientist?

What is Data Visualization

Types of Data Visualization

Types of Qualitative Data

Examples of Data Visualizations

Secondary Data Analysis

Data Visualization Examples

Benefits of Data Visualization

Best Data Visualization Tools

What is a Data Scientist?

Data Scientist Work

Skills Required for Data Scientist

Data Scientist Skills

How to Become a Data Scientist in 2023

Data Analyst Associate

Big Data Analytics Examples

Big Data Analytics Jobs

Customer Data

Big Data Analytics Salary

Big Data Analytics Software

Big Data Analytics Techniques

Big Data Analytics Tools

Big Data Engineer

Data Analysis Techniques

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Data Quality Tools

Data Analysis Tools

Data Analysis Tools Research

Types of Data Analysis

Types of Quantitative Research

What is Qualitative Data Analysis

Free Data Analysis Tools

Data Analytics Trends

Types of Data Analysis Techniques

Data Analytics Interview Questions

Data Analyst Interview Questions

Statistical Analysis

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Statistical Analysis Types

Statistical Analysis Softwares

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Types of Data in Statistics

Statistical Analysis Tools

Statistical Data Analysis Techniques

Statistical Analysis Methods

Exploratory Data Analysis

Statistical Analysis Regression

Big Data Tutorial

In this blog, the category has been developed for those who are willing to master big data technology. It explains several tools and methodologies of performing operations on a large pool of data. The main focus of this section is to clarify the differences between the rivals to make it clear which technology has to be used to meet different requirements. Apache Storm vs Apache Spark, Apache Hadoop vs Apache Storm, MapReduce vs Apache Spark, Hadoop vs SQL Performance, and Business Intelligence vs BigData is some of the topics that have been covered here. While topics, like What, is Splunk, Uses Of Splunk, What is MapReduce, Hadoop Ecosystem, and so on are also has been described.

Necessity to learn Big Data

The five main reasons to study big data are:

1. The decisions that are driven by data are of competitive advantage

Organizations make use of big data to identify trends and detect patterns to predict the future. This way organizations know more than their competitors.

2. Big data is the foundation for Artificial Intelligence

The techniques and capacities required in big data organizations and artificial intelligence are similar. The organizations benefit greatly by building a sound big environment first and then set up artificial intelligence with big data as the base.

3. The demand for big data skills is high

With the current trends in big data, the requirement for big data professionals is rapidly growing. As a result, there are large increases in the salaries of the people working on big data.

4. There is growth in investments in big data everyday

Studies show that big data investments are growing year after year. The International Data Corporation (IDC) predicts that data-related hardware, software, and services are expected to grow at the rate of eleven percent by the year two thousand twenty.

5. Our horizons will broaden by studying big data

The fun investment of our time is studying big data. Our analytical and reasoning skills improve by studying big data because the domain of big data is full of puzzles to solve.

Applications of Big Data

The applications of big data are spread out in several areas and domains. Some of the domains and areas where big data is applicable are:

  • Healthcare domain
  • Manufacturing domain
  • Media and Entertainment domain
  • Internet of Things domain
  • Government sector
  • Cybersecurity and intelligence
  • Prediction and prevention of crime
  • Evaluation of pharmaceutical drug
  • Scientific Research
  • Forecasting of weather
  • Compliance of tax
  • Optimization of traffic

Example

The universities have an ocean of data, and analytics and data visualizations have been used to draw patterns of data related to students' information in the universities.

Prerequisites

The reader must have knowledge of the GNU or Linux operating system, and programming language proficiency like Java, Scala, or Python in order to learn Big Data.

Target Audience

Beginners can refer to this tutorial to understand Big Data basics. This tutorial is helpful for people who want to pursue a career in the field of Big Data. This tutorial is good learning for all other readers.

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