Hadoop – Big Data Overview, Big Data Solutions And Introduction

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Hadoop – Big Data Overview

The amount of information created by human beings increases exponentially every year as a result of the proliferation of new technologies, devices, and communication means like social networking sites. We generated 5 billion gigabytes of data between the beginning of time and 2003. Assuming the information is stored on disks, they could fill an entire football field if stacked.. The same amount was created every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Although all this data being generated has value and can be put to good use, it is being ignored..

Define “Big Data.”

Big data refers to a set of data sets so large that they defy conventional data-processing methods. In its current form, it is neither a technique nor a tool; rather, it has evolved into a comprehensive field that necessitates a wide range of resources, including both specific methods and broader theoretical frameworks.

Just What Does “Big Data” Entail?

The term “big data” refers to the massive quantities of information generated by a variety of sources. The following are examples of disciplines that can be classified as part of Big Data.

  • Information recorded by a flight recorder, found in aircraft such as helicopters, planes, and jets. It records the aircraft’s performance data as well as the pilots’ and copilots’ voices, as well as any recordings made with onboard audio equipment.
  • e with respect to a base station.
  • Details about transportation systems, such as vehicle makes and models, passenger capacities, average travel times, and parking options, are all part of this data set.
  • Data Retrieval by Search Engines Search engines retrieve a large amount of information from a variety of databases.


As a result, Big Data consists of data with enormous volumes, high rates of change, and a broad range of possible applications. There will be three distinct types of information contained within.

  • We prefer structured data over relational data.
  • Unstructured data is inferior to XML data.
  • Word documents, PDFs, text documents, and media logs are examples of unstructured data.

the advantages of big data

  • Marketing firms are gaining insight into the effectiveness of their campaigns, promotions, and other advertising mediums by mining the data stored in social networks like Facebook.
  • Companies and retailers are using social media data, such as customers’ likes and dislikes and opinions of products, to inform their manufacturing and stocking decisions.
  • In order to better serve their patients, hospitals are now able to quickly access information about their patients’ past medical records.

Systems for Analyzing Massive Amounts of Data

Accurate analysis made possible by big data technologies is key to improving operational efficiencies, decreasing costs, and lowering risks for businesses.

If you want to tap into the potential of big data, you’ll need an infrastructure that can handle massive amounts of data, both structured and unstructured, in real time without compromising privacy or security.

Amazon, IBM, Microsoft, and many others have all released products designed to process massive amounts of data. When investigating tools for dealing with massive amounts of data, we focus on two broad categories of software:

Big Data in Operations

In this category are tools like MongoDB, which offer functionalities primarily geared toward data capture and storage in real-time, interactive workloads.

New cloud computing architectures have emerged over the past decade, and NoSQL Big Data systems are built to take advantage of these developments so that massive computations can be run cheaply and efficiently. As a result, operational big data workloads are less complicated to oversee, less expensive to implement, and more quickly available.

Using real-time data, some NoSQL systems can provide insights with little to no coding, data scientists, or extra infrastructure needed.

Using Big Data Analysis

Massively parallel processing (MPP) database systems and MapReduce are two examples of such systems, as they offer analytical capabilities for looking backward and conducting complex analysis that could potentially involve all of the data.

A MapReduce-based system can scale from a handful of servers to tens of thousands of high- and low-end machines, providing a new way to analyze data that complements the capabilities provided by SQL.

It’s common practice to use both types of technology in a given application because they complement one another.


Analytical systems that don’t do anything else, versus operational systems that don’t do anything else

Operational Analytical
Latency 1 ms – 100 ms 1 min – 100 min
Concurrency 1000 – 100,000 1 – 10
Access Pattern Understands How to Use a Computer and Reads Books Reads
Queries Selective Unselective
Data Scope Operational Retrospective
End User Customer Data Scientist
Technology NoSQL MapReduce, MPP Database

The Problems of Big Data

Here are some of the biggest problems with big data:

  • Capturing data
  • Curation
  • Storage
  • Searching
  • Sharing
  • Transfer
  • Analysis
  • Presentation

Enterprise servers are commonly used to help businesses meet the aforementioned obstacles.


Hadoop: Tackle Huge Data Sets

The Common Sense Way

In this setup, a company will have access to a computer specifically designed to store and analyze massive amounts of data. When it comes to data storage, developers will turn to companies like Oracle, IBM, etc. In this method, the user collaborates with the application, which is responsible for storing and analyzing the data.


This method is adequate for applications that handle data volumes within the capabilities of common database servers, or at most, the limits of the processor. But processing such massive amounts of scalable data through a single database bottleneck is a time-consuming and inefficient task.

Resolution by Google

MapReduce is a Google algorithm that was used to resolve this issue. This algorithm splits the work up into manageable chunks, distributes them across a network of computers, and then gathers the completed tasks into a unified dataset.


Doug Cutting and his group at Google created an Open Source Project (HADOOP) based on the solution provided by Google.

Through the MapReduce algorithm, which Hadoop employs, data can be processed in parallel with other users’ data. In a nutshell, Hadoop is employed in the programming of programs that can conduct exhaustive statistical analysis on massive datasets.

Hadoop – Introduction

Hadoop is a Java-based, open-source framework developed by the Apache software foundation that facilitates distributed processing of large datasets across clusters of computers with minimal need for complex programming models. Hadoop is a framework application that functions in a system where data storage and processing are carried out in a decentralized fashion across a network of computers. Hadoop was created to easily expand from a single server to thousands of machines, each of which can provide its own local computation and storage.

The Hadoop Framework’s Architecture Framework

There are essentially two tiers to Hadoop:

  • the MapReduce processing/computing layer, and
  • A Layer For Storing Things (Hadoop Distributed File System).


MapReduce was developed at Google as a parallel programming model for writing distributed applications to process massive datasets (multi-terabyte data-sets) on large clusters (thousands of nodes) of commodity hardware in a dependable, fault-tolerant manner. Hadoop, an Apache open-source framework, is the host for the MapReduce program.

File Sharing with Hadoop

Hadoop’s HDFS is a distributed file system built on top of the GFS used by Google and is optimized for use with inexpensive servers. There are many parallels to be drawn with pre-existing distributed file systems. In contrast to other forms of distributed file storage, however, there are important distinctions to be made. It can handle failure gracefully and can be used with modest hardware. It’s great for applications that need access to large datasets and has a high throughput.

Hadoop also features the following two modules in addition to the aforementioned core components:

  • Common Hadoop – A collection of Java libraries and tools used by other parts of Hadoop.
  • Hadoop YARN is a system for managing resources in a cluster and scheduling jobs.

What is the function of Hadoop?

Larger servers with more powerful configurations are costly to develop, but a distributed system made up of many inexpensive commodity computers with a single central processing unit (CPU) can read the dataset in parallel and provide much higher throughput. It’s also a lot less expensive than buying a single high-end server. Hadoop’s first selling point is that it can be deployed on a large number of inexpensive nodes in a cluster.

Hadoop is used to distribute and execute programs across a network of computers. The following are some of the fundamental operations carried out by Hadoop during this procedure:


  • At first, information is organized into different folders and files. Each file is split into 64M and 128M chunks (preferably 128M).
  • Each node in the cluster receives a copy of the files and processes them individually.
  • HDFS monitors the operation because it is atop the local file system.
  • The redundancy of the blocks allows for the smooth operation of the system in the event of a hardware failure.
  • Verifying the correct execution of the program.
  • Conducting the sort that follows the map and before the reduce phase.
  • dispatching the categorized information to a specified machine.
  • Keeping track of job-specific debugging logs.

Several Benefits of Hadoop

  • The Hadoop framework speeds up the process of developing and validating distributed applications. Effectively utilizing the inherent parallelism of the CPU cores, it automatically partitions data and tasks across machines.
  • Instead of depending on hardware for FTHA, the Hadoop library was built to automatically detect and recover from application-level failures.
  • The number of servers in the cluster can be changed on the fly without affecting Hadoop’s ability to function normally.
  • Hadoop’s compatibility across platforms thanks to being Java-based is another major benefit.