Hadoop In 5 Minutes | What Is Hadoop? | Introduction To Hadoop | Hadoop Explained |Simplilearn

Summary

This script delves into the evolution of data management with the advent of the digital age. It highlights the shift from handling simple data to grappling with 'big data', necessitating robust solutions like Hadoop. The script explains Hadoop's three core components: HDFS for distributed storage with a 3x replication schema ensuring fault tolerance, MapReduce for efficient parallel data processing, and YARN for resource management. It underscores Hadoop's pivotal role in big data applications across various industries.

Takeaways

• 📚 In the pre-digital era, data was minimal and primarily document-based, easily managed with a single storage and processing unit.
• 🌐 The advent of the internet led to the explosion of data known as 'big data', which came in various forms such as emails, images, audio, and video.
• 💡 Hadoop was introduced as a solution to handle big data efficiently, utilizing a cluster of commodity hardware to store and process vast amounts of data.
• 🗂️ Hadoop's first component, the Hadoop Distributed File System (HDFS), distributes data across multiple computers in blocks, with a default block size of 128 megabytes.
• 🔄 HDFS ensures data reliability through a replication method, creating copies of data blocks and storing them across different nodes to prevent data loss.
• 🔄 The MapReduce component of Hadoop processes data by splitting it into parts, processing them in parallel on different nodes, and then aggregating the results.
• 📊 MapReduce improves efficiency by parallel processing, which is particularly beneficial for handling large volumes of diverse data types.
• 📈 Yet Another Resource Negotiator (YARN) is Hadoop's third component, managing resources like RAM, network bandwidth, and CPU for multiple simultaneous jobs.
• 🔧 YARN consists of a Resource Manager, Node Managers, and Containers, which work together to assign and monitor resources for job processing.
• 🌟 The 3x replication schema in HDFS ensures fault tolerance, which is crucial for maintaining data integrity even if a data node fails.
• 🌐 Hadoop and its ecosystem, including tools like Hive, Pig, Apache Spark, Flume, and Scoop, are game-changers for businesses, enabling applications like data warehousing, recommendation systems, and fraud detection.

Q & A

What was the main challenge with data storage and processing before the rise of big data?

Before the rise of big data, the main challenge was that storage and processing could be done with a single storage unit and processor, as data was mostly structured and generated slowly.

What types of data are included in the term 'big data'?

Big data includes semi-structured and unstructured data, such as emails, images, audio, video, and other formats generated rapidly.

Why did traditional storage and processing methods become inadequate for big data?

Traditional storage and processing methods became inadequate because the vast and varied forms of big data were too large and complex to be handled by a single storage unit and processor.

How does Hadoop's Distributed File System (HDFS) store big data?

HDFS splits data into blocks and distributes them across multiple computers in a cluster. For example, 600 MB of data would be split into blocks of 128 MB each, and these blocks would be stored on different data nodes.

What happens if a data node crashes in HDFS?

If a data node crashes in HDFS, the data is not lost because HDFS uses a replication method, creating multiple copies of each block across different data nodes. This ensures fault tolerance.

How does the MapReduce framework process big data?

MapReduce splits data into parts, processes each part separately on different data nodes, and then aggregates the individual results to give a final output, improving load balancing and processing speed.

What is the role of YARN in Hadoop?

YARN, or Yet Another Resource Negotiator, efficiently manages resources such as RAM, network bandwidth, and CPU across the Hadoop cluster. It coordinates resource allocation and job processing through resource managers, node managers, and containers.

Why is the replication factor set to 3 in HDFS?

The replication factor is set to 3 in HDFS to ensure that each block of data is stored on three different data nodes, making the system fault-tolerant and preventing data loss in case of node failure.

What are some applications of Hadoop in businesses?

Hadoop is used in businesses for various purposes, including data warehousing, recommendation systems, and fraud detection. Companies like Facebook, IBM, eBay, and Amazon use Hadoop for managing and analyzing large datasets.

What are some components of the Hadoop ecosystem besides HDFS, MapReduce, and YARN?

In addition to HDFS, MapReduce, and YARN, the Hadoop ecosystem includes tools and frameworks like Hive, Pig, Apache Spark, Flume, and Sqoop, which help with big data management, processing, and analysis.

Outlines

00:00

💾 Introduction to Hadoop and Big Data

The script begins by contrasting the data landscape before the digital era, where data was minimal and easily managed, with the present reality of vast amounts of data generated in a multitude of forms and formats. The advent of the internet has led to the rise of big data, which is challenging to handle with traditional storage and processing methods. Hadoop was introduced as a solution to this problem, using a cluster of commodity hardware to store and process big data efficiently. Hadoop consists of three main components: HDFS for storage, MapReduce for processing, and YARN for resource management. HDFS distributes data across multiple nodes in blocks to prevent data loss, even if a node fails. MapReduce processes data in parallel across different nodes, improving efficiency and load balancing. YARN manages resources to ensure that multiple jobs can run simultaneously without resource conflicts. The script also mentions other tools in the Hadoop ecosystem like Hive, Pig, Spark, Flume, and Scoop.

05:00

🛡️ Advantages of HDFS 3x Replication

The second paragraph focuses on the benefits of the 3x replication schema in HDFS. It poses a question to the audience about the advantage of this replication method, suggesting options like supporting parallel processing, faster data analysis, ensuring fault tolerance, or managing cluster resources. The correct answer is fault tolerance, as HDFS replicates data across multiple systems to prevent data loss even if a node fails. The paragraph also highlights the impact of Hadoop on businesses, mentioning its use by major companies like Facebook, IBM, eBay, and Amazon for various applications such as data warehousing, recommendation systems, and fraud detection. The script concludes with a call to action for viewers to like, subscribe, and stay tuned for more content on technology trends.

Keywords

💡Digital

Digital refers to the representation of information in binary form, typically using only two digits, 0 and 1. In the context of the video, the term is used to contrast the past when data was minimal and mostly analog with the present where data is abundant and predominantly digital. The shift to digital has led to the generation of vast amounts of data that require advanced systems for storage and processing.

💡Big Data

Big Data is a term used to describe the massive volume of structured, semi-structured, and unstructured data that is so large it's difficult to process using traditional database and software techniques. The video explains that the advent of the internet has led to an explosion of data types including emails, images, audio, and video, all of which fall under the umbrella of big data.

💡Hadoop

Hadoop is an open-source software framework used for distributed storage and processing of big data. The video highlights Hadoop as a solution to the problem of handling big data by utilizing a cluster of commodity hardware. It emphasizes Hadoop's ability to store and process vast amounts of data efficiently, which is crucial for businesses dealing with large-scale data.

💡Hadoop Distributed File System (HDFS)

HDFS is the primary storage system used by Hadoop. It is designed to store large data sets reliably across multiple machines. The video explains that HDFS splits data into blocks and distributes these blocks across different data nodes in a cluster. This not only optimizes storage but also ensures data availability and fault tolerance, which is a key feature of HDFS.

💡Replication Factor

The replication factor in HDFS refers to the number of copies of a data block stored in the cluster. The video uses the example of block 'a' being replicated three times across different data nodes, illustrating how this method ensures that data is not lost even if a data node fails. This replication strategy is central to HDFS's fault-tolerance mechanism.

💡MapReduce

MapReduce is a programming model and an associated implementation for processing and generating large datasets. The video describes how MapReduce works by splitting data into parts and processing each part separately on different data nodes. It contrasts this with traditional data processing methods, which are less efficient when dealing with large volumes of data. MapReduce is a key component of Hadoop that enables efficient data processing.

💡Fault Tolerance

Fault tolerance is the ability of a system to continue operating normally in the event of the failure of some of its components. The video emphasizes the importance of fault tolerance in HDFS, where data is replicated across multiple systems to ensure that even if a data node crashes, the data remains accessible and the system continues to function.

💡Yet Another Resource Negotiator (YARN)

YARN is a resource management platform responsible for managing compute resources in clusters and using them for scheduling users' applications. The video explains that YARN plays a crucial role in managing resources for multiple jobs running on Hadoop, ensuring efficient utilization of resources such as RAM, network bandwidth, and CPU.

💡Data Nodes

In the context of Hadoop, data nodes are the individual machines in a cluster that store actual data. The video mentions that data is distributed amongst many computers and stored in blocks on these data nodes. The concept is integral to understanding how Hadoop distributes data storage across a network of computers.

💡Resource Manager

The Resource Manager is a component of YARN that is responsible for allocating resources to different applications running on the Hadoop cluster. The video describes how the Resource Manager assigns resources, ensuring that each job gets the necessary resources to complete its task, which is vital for the efficient operation of the cluster.

💡Containers

In the context of YARN, containers are the abstraction of physical resources. The video explains that containers hold a collection of physical resources and are used to process job requests. They are allocated by the Resource Manager and Node Manager to run various tasks, which is a key part of how YARN manages resources in a Hadoop cluster.

Highlights

In the pre-digital era, data was minimal and mostly structured in documents and rows and columns.

The advent of the internet led to a surge in data generation in various forms and formats.

Big data encompasses semi-structured and unstructured data types like emails, images, audio, and video.

Hadoop was developed to efficiently store and process vast amounts of data using commodity hardware.

Hadoop Distributed File System (HDFS) is designed for storing massive data by distributing it across multiple computers.

Data in HDFS is stored in blocks, with a default block size of 128 megabytes.

HDFS employs a replication method to ensure data is not lost even if a data node fails.

The replication factor in HDFS is set to 3 by default, enhancing fault tolerance.

MapReduce is Hadoop's processing component that splits data for parallel processing on different nodes.

MapReduce improves efficiency by processing large volumes of data in parts and aggregating the results.

The Map phase of MapReduce counts occurrences, while the Reduce phase aggregates the counts.

YARN (Yet Another Resource Negotiator) manages resources and job requests in the Hadoop cluster.

YARN consists of a Resource Manager, Node Manager, Application Master, and Containers.

The Hadoop ecosystem includes tools like Hive, Pig, Apache Spark, Flume, and Scoop for data management.

The 3x replication schema in HDFS ensures fault tolerance by making copies of data stored across multiple systems.

Hadoop has been a game-changer for businesses, enabling applications like data warehousing, recommendation systems, and fraud detection.

The video concludes with a question about the advantage of the 3x replication schema in HDFS.

The video invites viewers to subscribe and engage with the content for more on technologies and trends.

Transcripts

let's rewind to the days before the

world turned digital

back then miniscule amounts of data were

generated at a relatively sluggish pace

all the data was mostly documents and in

the form of rows and columns

storing or processing this data wasn't

much trouble as a single storage unit

and processor combination would do the

job but as years passed by the internet

took the world by storm giving rise to

tons of data generated in a multitude of

forms and formats every microsecond

semi-structured and unstructured data

was available now in the form of emails

images audio and video to name a few

all this data became collectively known

as big data

although fascinating it became nearly

impossible to handle this big data and a

storage unit processor combination was

obviously not enough

so what was the solution

multiple storage units and processors

were undoubtedly the need of the hour

this concept was incorporated in the

framework of hadoop that could store and

process vast amounts of any data

efficiently using a cluster of commodity

hardware hadoop consisted of three

components that were specifically

designed to work on big data in order to

capitalize on data the first step is

storing it the first component of hadoop

is its storage unit the hadoop

distributed file system or hdfs

storing massive data on one computer is

unfeasible hence data is distributed

amongst many computers and stored in

blocks

so if you have 600 megabytes of data to

be stored hdfs splits the data into

multiple blocks of data that are then

stored on several data nodes in the

cluster

128 megabytes is the default size of

each block

hence 600 megabytes will be split into

four blocks a b c and d of 128 megabytes

each

and the remaining 88 megabytes in the

last block e

so now you might be wondering what if

one data node crashes

do we lose that specific piece of data

well no that's the beauty of hdfs

hdfs makes copies of the data and stores

it across multiple systems

for example when block a is created it

is replicated with a replication factor

of 3 and stored on different data nodes

this is termed the replication method

by doing so data is not lost at any cost

even if one data node crashes making

hdfs fault tolerant after storing the

data successfully it needs to be

processed

this is where the second component of

hadoop mapreduce comes into play

in the traditional data processing

method entire data would be processed on

a single machine having a single

processor this consumed time and was

inefficient especially when processing

large volumes of a variety of data to

overcome this mapreduce splits data into

parts and processes each of them

separately on different data nodes

the individual results are then

aggregated to give the final output

let's try to count the number of

occurrences of words taking this example

first the input is split into five

separate parts based on full stops

the next step is the mapper phase where

the occurrence of each word is counted

and allocated a number

after that depending on the words

similar words are shuffled sorted and

grouped following which in the reducer

phase all the grouped words are given a

count

finally the output is displayed by

aggregating the results all this is done

by writing a simple program

similarly mapreduce processes each part

of big data individually and then sums

the result at the end

this improves load balancing and saves a

considerable amount of time

now that we have our mapreduce job ready

it is time for us to run it on the

hadoop cluster

this is done with the help of a set of

resources such as ram network bandwidth

and cpu

multiple jobs are run on hadoop

simultaneously and each of them needs

some resources to complete the task

successfully

to efficiently manage these resources we

have the third component of hadoop which

is yarn

yet another resource negotiator or yarn

consists of a resource manager node

manager application master and

containers the resource manager assigns

resources node managers handle the nodes

and monitor the resource usage in the

node the containers hold a collection of

physical resources

suppose we want to process the mapreduce

job we had created

first the application master requests

the container from the node manager

once the node manager gets the resources

it sends them to the resource manager

this way yarn processes job requests and

manages cluster resources in hadoop

in addition to these components hadoop

also has various big data tools and

frameworks dedicated to managing

processing and analyzing data

the hadoop ecosystem comprises several

other components like hive pig apache

spark flume and scoop to name a few

the hadoop ecosystem works together on

big data management

so here's a question for you

what is the advantage of the 3x

replication schema in hdfs

a supports parallel processing b faster

data analysis

c ensures fault tolerance

d

manages cluster resources

give it a thought and leave your answers

in the comment section below three lucky

winners will receive amazon gift

vouchers hadoop has proved to be a game

changer for businesses from startups and

big giants like facebook ibm ebay and

amazon there are several applications of

hadoop like data warehousing

recommendation systems fraud detection

and so on

we hope you enjoyed this video if you

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