.jpg)
"Empower Your Data Science Journey with Big Data Mastery – Learn Hadoop with @Naresh IT’s Comprehensive Online Training!"
In today's data-driven world, businesses and organizations are collecting vast amounts of information at an unprecedented scale. Data science has emerged as a powerful field to help derive insights and value from this data, but traditional tools often fall short when it comes to managing such high volumes. This is where Hadoop, an open-source big data framework, becomes crucial. Hadoop enables data scientists to process and analyze large datasets efficiently, making it an essential skill for anyone looking to elevate their data science expertise.
If you’re aspiring to become a proficient data scientist, gaining knowledge in Hadoop can significantly boost your skill set. Here’s why Hadoop is so important in the realm of data science and how it empowers professionals to work with data at scale.
Take the Data Science Fundamentals skill track to gain the foundational skills you need to become a Data Scientist.
The Scope of Learning Hadoop for Data Science
As big data continues to grow in scope, the demand for data scientists with Hadoop expertise is increasing. Mastering Hadoop can open up career opportunities in diverse fields, including finance, healthcare, retail, and technology. As companies prioritize data-driven strategies, those skilled in Hadoop are highly sought after for roles in data engineering, data analysis, and machine learning, often commanding competitive salaries.
For data science enthusiasts, Hadoop knowledge provides a competitive edge, helping them unlock more effective ways to manage, analyze, and derive insights from data.
Are you ready to elevate your data science career? Join @Naresh IT’s Hadoop Online Training and master the skills to handle big data effectively. Our comprehensive course covers Hadoop fundamentals, advanced MapReduce programming, ecosystem tools, and real-time analytics applications. With hands-on projects, expert instructors, and flexible online learning options, you'll gain practical experience and in-depth understanding of Hadoop’s role in data science.
Final Thoughts
As data science and big data continue to converge, learning Hadoop is more essential than ever. Hadoop equips data scientists to tackle large datasets, leverage distributed processing, and uncover valuable insights that drive decision-making. By enrolling in Naresh IT’s Hadoop Online Training, you’ll not only gain essential Hadoop skills but also enhance your career potential, positioning yourself at the forefront of the data science field. Make the leap to data mastery and discover the power of Hadoop with Naresh IT!
---------------------------------------------------------------------------------------------------
For More Details About Full Stack Courses : Visit Here
Register For Free Demo on UpComing Batches : Click Here
Introduction:
Hadoop is an open source platform that is highly popular these days. Every corporation is hunting for a skilled Hadoop developer.
The Hadoop was first introduced by Mike Cafarella and Doug Cuttingwho who are the persons trying to create a search engine system that can index 1 billion pages. So they are going to start their research to build that search engine but they found that the cost for preparing such engine will be very high. So they stumbled upon a document released in 2003 that outlined the design of Google's distributed file system, known as GFS. Later, in 2004, Google published another article that introduced MapReduce to the public. Finally, these two investigations formed the basis for the "Hadoop" framework.
How It All Started?
As previously noted, the cornerstone of Hadoop and its relevance in terms of search data processing. The word GFS is something that is mostly utilized to solve all search strategies for keeping very huge files created as a part of the web. In any case, there is a crawling and indexing procedure.
It also uses the MapReduce technique which is mainly used to map the large data files and help for better search strategy. It is recognized as an important component of the "Hadoop" system.
So, by considering it the Hadoop is became so powerful.
What is Big Data?
As previously stated, Hadoop is open source. It is primarily a Java-based system designed for storing and analyzing large amounts of data. The data is stored on low-cost commodity servers that operate as clusters. Cafarella, Hadoop employs the MapReduce programming approach to store and retrieve data from its nodes more quickly.
Big data refers to a collection of enormous datasets that cannot be handled using conventional computer approaches. It is no more just a technique or a tool; rather, it has evolved into a comprehensive subject including a variety of tools, approaches and frameworks.
Here the Big data is mainly used to involves the data produced by different devices and applications. Some of the disciplines that fall within the category of Big Data are covered below.
Black Box Data − It is a component which is get used as a part of helicopter, airplanes, and jets, etc.is primarily utilized to record the flight crew's voices and information, as well as recordings from microphones and headphones in the aircraft.
Social media data is a key aspect in the growth of Big Data since it gives information about people's behavior.
Stock Exchange Data − This data contains information regarding customers' 'buy' and'sell' choices on shares of various firms.
Power Grid Data refers to the information used by a specific node in relation to a base station.
Transport data refers to the model, capacity, distance, and availability of vehicles.
Search Engine Data − Search engines retrieve data from several databases.
Nowadays, we use so many social media programs. Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe which is a real time example of Big Data.
Big Data & Hadoop – Restaurant Analogy:
Let us now use a restaurant example to better appreciate the challenges related with Big Data. Here we will try to learn that how Hadoop solved that problem.
Let us consider that the Bob is a businessman who has opened a small restaurant. Initially, he received two orders each hour in his restaurant, and he had one chef and one food shelf to manage all of them.
Let us now compare the restaurant example to the typical case, in which data is created at a consistent pace and our existing systems, such as RDBMS, are capable of handling it, much like Bob's chef.
Similarly, numerous processing units were deployed to analyze large data sets in concurrently (much as Bob employed four cooks). Even in this situation, bringing in several processing units was ineffective since the centralized storage unit became the bottleneck.
In other words, the central storage unit's performance determines the whole system performance. As a result, if our central store fails, the entire system becomes vulnerable. As a result, this single point of failure had to be addressed once more.
Hadoop File System was created utilizing a distributed file system design. It runs on commodity hardware. Compared to other distributed systems, HDFS is more fault-tolerant and constructed utilizing low-cost hardware.
To address the storage and processing issues, two essential Hadoop components were created: HDFS and YARN. HDFS addresses the storage issue since it stores data in a distributed manner and is easily expandable. YARN solves the processing problem by significantly lowering processing time. Moving on, what is Hadoop?
What is Hadoop?
As previously said, Hadoop is an open-source software framework similar to Java script that is mostly used for distributed Big Data storage and processing over large clusters of commodity hardware. Hadoop is released under the Apache 2 license.
Hadoop was created based on Google's MapReduce article and incorporates functional programming techniques. Hadoop is one of Apache's most advanced projects, written in Java. Hadoop was founded by Doug Cutting and Michael J. Cafarella.
Hadoop-as-a-Solution:
Let's look at how Hadoop solves the Big Data concerns we've explored so far.
The main issues with big data are as follows:
Capturing data
Curation
Storage
Searching
Sharing
Transfer
Analysis
Presentation
How to store huge amount of data:
Hadoop's major component is the HDFS system, which provides a distributed approach to store Big Data
Here the data is stored in blocks in DataNodes and you specify the size of each block.
For example, suppose you have 512 MB of data and have set HDFS to produce 128 MB of data blocks.
The HDFS will partition data into four blocks (512/128=4) and store them across many DataNodes.
To ensure fault tolerance, data blocks are duplicated on separate DataNodes while being stored in them.
How to store a variety of data:
HDFS in Hadoop is capable of storing any types of data, whether structured, semi-structured, or unstructured.
HDFS, there is no pre-dumping schema validation.
It also adheres to the principle of "write once, read many."
Because of this, you can simply write any type of data once and read it several times to gain insights.
How to process the data faster:
In this situation, Hadoop may relocate the processing unit to the data rather of the other way around.
So, what does it mean to move the compute unit to data?
It implies that instead of transporting data from multiple nodes to a single master node for processing, the processing logic is given to the nodes where the data is stored, allowing each node to handle a portion of the data simultaneously. Finally, all of the intermediary output generated by each node is combined, and the final answer is returned to the client.
Features of Hadoop:
The following properties distinguish Hadoop from others. Such as
It is ideal for distributed storage and processing.
Hadoop provides a command-line interface for interacting with HDFS.
The built-in servers for namenode and datanode allow users to simply verify the status of the cluster.
Streaming access to file system data.
HDFS provides file permissions and authentication.
Reliability
Hadoop architecture was developed in such a way that it includes built-in fault tolerance characteristics. So that even if a failure occurs, there is a backup method in place to deal with the problem. hence, Hadoop is highly reliable.
Economical
Hadoop uses commodity hardware (like your PC, laptop). We can easily execute it in any environment.
For example, in a modest Hadoop cluster, all DataNodes can have standard specifications such as 8-16 GB RAM, 5-10 TB hard drive, and Xeon CPUs.
It is also easier to manage a Hadoop system and more cost-effective. Furthermore, Hadoop is open-source software, thus there are no license fees.
Scalability
Hadoop offers the inherent capacity to integrate smoothly with cloud-based services. So, if you deploy Hadoop in the cloud, you won't have to worry about scalability.
Flexibility
Hadoop is extremely adaptable in terms of its capacity to handle various types of data. Hadoop can store and analyze many types of data, including structured, semi-structured, and unstructured data.
Hadoop Core Components:
While configuring a Hadoop cluster, you have the choice of selecting a number of services as part of your Hadoop platform, however there are two services that are always mandatory for setting up Hadoop.
HDFS
Let us go ahead with HDFS first. HDFS's major components are the NameNode and the DataNode. Let's go over the duties of these two components in depth.
HDFS uses the master-slave architecture and includes the following components.
Namenode
The namenode is the commodity hardware that houses the GNU/Linux operating system and namenode software.
It is software that can be executed on standard hardware.
The machine with the namenode operates as the master server and performs the following tasks: Manages the file system namespace and controls client access to files.
It can also perform file system operations including renaming, shutting, and opening files and directories.
If a file is removed in HDFS, the NameNode instantly records it in the EditLog.
It routinely gets a Heartbeat and a block report from all the DataNodes in the cluster to check that they are alive.
It maintains track of all the blocks in the HDFS and DataNode where they are stored
Datanode:
The datanode is a piece of commodity hardware that runs the GNU/Linux operating system and datanode software.
Every node (commodity hardware/system) in a cluster will have a datanode. These nodes control the system's data storage.
Datanodes execute read-write operations on file systems based on client requests.
They also conduct block formation, deletion, and replication in accordance with the namenode's commands.
It is also responsible for producing, removing, and replicating blocks based on the NameNode's choices.
It transmits heartbeats to the NameNode on a regular basis to indicate the overall health of HDFS; the default frequency is 3 seconds.
Block
In general, HDFS files are used to store user data.
A file in a file system will be partitioned into one or more segments and/or stored in separate data nodes.
These file segments are referred to as blocks. A Block is the minimal quantity of data that HDFS may read or write.
The default block size is 64MB, although it can be adjusted depending on the HDFS settings.
YARN
Hadoop's YARN consists of two primary components, which include:
ResourceManager and
NodeManager.
ResourceManager
It is a cluster-level component (one per cluster) that operates on the master computer.
It manages resources and schedules apps that operate on top of YARN.
It consists of two components: the Scheduler and the ApplicationManager.
The Scheduler allocates resources to the many operating apps.
The ApplicationManager is in charge of receiving job submissions and negotiating the first container for running the application.
It monitors the heartbeats from the Node Manager.
NodeManager
It is a node-level component (one on per node) that runs on all slave machines.
It is in charge of maintaining containers and monitoring resource use in each one.
It also monitors node health and log management.
It connects with ResourceManager on a constant basis in order to stay current.
Hadoop Ecosystem
Hadoop is a platform or framework for solving Big Data challenges. It serves as a package of services for consuming, storing, and analyzing large data collections, as well as configuration management tools.
Fault detection and recovery − HDFS's use of commodity hardware leads to frequent component failures. As a result, HDFS should have systems for detecting and recovering faults quickly and automatically.
HDFS should contain hundreds of nodes per cluster to manage applications with large datasets.
When computation occurs near the data, it is more efficient to complete the required job. It decreases network traffic while increasing throughput, particularly for large datasets.
Last.FM Case Study
Last.FM, an online radio and community-driven music discovery service, was created in 2002.
Here, the user sends information to the Last.FM servers identifying the songs they are listening to.
The supplied data is processed and saved so that the user may view it as charts.
Scrobble: When a user plays a track of his or her own choosing and provides the information to Last.FM via a client application.
Radio listen: When a user listens to a Last.FM radio station and streams a song.
Scope @ NareshIT:
At Naresh IT, you will find an experienced faculty that will lead, advise, and nourish you as you work toward your desired objective.
Here, you will gain valuable hands-on experience in a realistic industry-oriented setting, which will undoubtedly help you design your future.
During the application design process, we will inform you about other aspects of the application as well.
Our expert trainer will explain the ins and outs of the problem scenario.
Our slogan is "achieve your dream goal." Our amazing team is working tirelessly to ensure that our pupils click on their targets. So, believe in us and our advise, and we promise you of your success.
.jpg)
Introduction:
As we already know that the Hadoop is an Apache open-source framework written in java environment, so it is the open source and being widely considered.
It allows distributed processing of large datasets across clusters of computers using simple programming models.
The Hadoop architecture is basically used to designed in such a manner so that it can scale up from single server to thousands of machines, each offering local computation and storage.
Now if we need to understand what exactly the Hadoop is, then we need to have first understand the issues related to the Big Data and the traditional processing system as it is being considered as a major component and area of Hadoop.
As the technology is going to be Advanced day by day ahead, so we need to understand the importance of Hadoop, and its application strategy using which it can be able to provide the solution to the problems associated with Big Data. Here I am also going to discuss about the CERN case study to highlight the benefits of using Hadoop.
Problems with Traditional Approach:
In traditional approach, the main issue was handling the heterogeneity of data i.e., structured, semi-structured and unstructured.
In this approach, the user interacts with the application, which in turn handles the part of data storage and analysis.
It is mainly suffering with the problem for storing the colossal amount of data.
It also has the problem to store heterogeneous data.
In traditional processing the accessing and processing speed is also having the major problem specially when the data size is very large.
Limitation/ problems lies with Traditional processing:
This approach works fine with those applications that process less voluminous data that can be accommodated by standard database servers.
It is limited up to the limit of the processor that is processing the data.
But when it comes to dealing with huge amounts of scalable data, it is a hectic task to process such data through a single database bottleneck.
Now if we are going to consider the Big Data then it is being considered as a best solution over the traditional approach. Few major parts are discussed as below.
The Big Data is emerging technology which is being used by most of the organization.
It is basically the collection of large datasets that cannot be processed using traditional computing techniques.
It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, techniques and frameworks.
Now the Organizations are examining large data sets to uncover all hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
.jpg)
Evolution of Hadoop:
The evolution of Hadoop was get started from 1999 when it was first identified by Apache Software Foundation who launch it as a non-profit platform.
Later on, In 2003, Doug Cutting launches project Nutch to handle billions of searches and indexing millions of web pages.
In Oct 2003 – Google releases its first research papers with GFS (Google File System) where it describe about the Hadoop and its relevant functionality feature.
In Dec 2004, Google releases papers with MapReduce which is being considered as a major component of Hadoop system.
In 2005, Nutch used GFS and MapReduce to perform operations in the HDFS system which is a major component for Hadoop environment.
In 2006, Yahoo created Hadoop based on GFS and MapReduce with Doug Cutting and team. You would be surprised if I would tell you that, in 2007 Yahoo started using Hadoop on a 1000 node cluster.
Later in Jan 2008, Yahoo released Hadoop as an open source project to Apache Software Foundation. In Jul 2008, Apache tested a 4000 node cluster with Hadoop successfully. In 2009, Hadoop successfully sorted a petabyte of data in less than 17 hours to handle billions of searches and indexing millions of web pages. Moving ahead in Dec 2011, Apache Hadoop released version 1.0. Later in Aug 2013, Version 2.0.6 was available.
What is Hadoop?
Hadoop is a framework that allows you to first store Big Data in a distributed environment, so that, you can process it parallelly. There are basically two components in Hadoop:
The first one is HDFS for storage (Hadoop distributed File System), that allows you to store data of various formats across a cluster. The second one is YARN, for resource management in Hadoop. It allows parallel processing over the data, i.e. stored across HDFS.
As we have already discussed earlier that the Hadoop is an open-source software framework like Java script and is mainly used for storing and processing Big Data in a
distributed manner on large clusters of commodity hardware. Hadoop is licensed under the Apache v2 license.
Hadoop was developed, based on the paper written by Google on the MapReduce system and it applies concepts of functional programming. Hadoop is written in the Java programming language and ranks among the highest-level Apache projects. Hadoop was developed by Doug Cutting and Michael J. Cafarella.
Hadoop-as-a-Solution:
Let’s understand how Hadoop provides a solution to the Big Data problems that we have discussed so far.
The major challenges associated with big data are as follows −
Capturing data
Curation
Storage
Searching
Sharing
Transfer
Analysis
Presentation
How to store huge amount of data:
The Hadoop is mainly have a HDFS system which is provides a distributed way to store Big Data.
Here the data is stored in blocks in DataNodes and you specify the size of each block.
For example if you have 512 MB of data and you have configured HDFS such that it will create 128 MB of data blocks.
The HDFS will divide data into 4 blocks as 512/128=4 and stores it across different DataNodes.
While storing these data blocks into DataNodes, data blocks are replicated on different DataNodes to provide fault tolerance.
How to store a variety of data:
The HDFS in Hadoop can capable enough to store all kinds of data whether it is structured, semi-structured or unstructured.
HDFS, there is no pre-dumping schema validation.
It also follows write once and read many models.
Due to this, you can just write any kind of data once and you can read it multiple times for finding insights.
How to process the data faster:
In this case the Hadoop is allowed to move the processing unit to data instead of moving data to the processing unit.
So, what does it mean by moving the computation unit to data?
It means that instead of moving data from different nodes to a single master node for processing, the processing logic is sent to the nodes where data is stored so as that each node can process a part of data in parallel. Finally, all of the intermediary output produced by each node is merged together and the final response is sent back to the client.
Where is Hadoop used:
As we have already discussed earlier that the Hadoop is framework hence it is mostly used for:
Designing the Search engine as it has the ability to process a huge data. For designing the search in Yahoo, Amazon, Zvents it is mostly used.
It is also used for designing the Log processing environment like Facebook, Yahoo does have.
For making the Data Warehouse based application layer like the Facebook, AOL have.
For Video and Image Analysis based application. As it requires the high processing.
When not to use Hadoop:
Following are some cases where it is being not recommended by the expert to use the Hadoop:
Low Latency data access : Quick access to small parts of data
Multiple data modification : Hadoop is a better fit only if we are primarily concerned about reading data and not modifying data.
Lots of small files : Hadoop is suitable for scenarios, where we have few but large files.
After knowing the best suitable use-cases, let us move on and look at a case study where Hadoop has done wonders.
Hadoop-CERN Case Study
Now with respect to the discuss scenario the CERN is basically used when we need the data for scaling up in terms of amount and complexity.
One of the important task is to serve these scalable requirements.
Hadoop is also used for cluster setup. By using Hadoop, they limited their cost in hardware and complexity in maintenance.
They integrated Oracle & Hadoop and they got advantages of integrating. Oracle, optimized their Online Transactional System & Hadoop provided them scalable distributed data processing platform. They designed a hybrid system, and first they moved data from Oracle to Hadoop.
The main Hadoop components they are using at the CERN-IT Hadoop service:
You can learn about each of these tool in Hadoop ecosystem blog.
Techniques for integrating Oracle and Hadoop:
Export data from Oracle to HDFS
Sqoop was good enough for most cases and they also adopted some of the other possible options like custom ingestion, Oracle DataPump, streaming etc.
Query Hadoop from Oracle
They accessed tables in Hadoop engines using DB links in Oracle. That also build hybrid views by transparently combining data in Oracle and Hadoop.
Use Hadoop frameworks to process data in Oracle DBs.
Scope @ NareshIT:
At Naresh IT you will get a good Experienced faculty who will guide you, mentor you and nurture you to achieve your dream goal.
Here you will get a good hand on practice in terms of practical industry-oriented environment which will definitely help you a lot to shape your future.
During the designing process of application, we will let you know about the other aspect of the application too.
Our Expert trainer will let you know about every in’s and out’s about the problem scenario.
Achieving your dream goal is our motto. Our excellent team is working restlessly for our students to click their target. So, believe on us and our advice, and we assured you about your sure success.
+91 8179191999 
040-2374 6666 / +91
8179191999
