Do you think IIT Guwahati certified course can help you in your career?
Introduction
Hadoop is a Java-based Apache open-source framework that allows large datasets to be processed across clusters of computers using simple programming models. The Hadoop framework application runs in a clustered computing environment that allows for distributed storage and computation. Hadoop is built to scale from a single server to tens of thousands of computers, each with its processing and storage capabilities.
History of Hadoop
According to its co-founders, Doug Cutting and Mike Cafarella, Hadoop was born out of the Google File System paper published in October 2003. "MapReduce: Simplified Data Processing on Large Clusters," another Google paper, was born from this one. The Apache Nutch project began development, but in January 2006, it was renamed Hadoop and moved to the new Hadoop subproject. It was named after a toy elephant owned by Doug Cutting, who worked at Yahoo!. Around 5,000 lines of code for HDFS and 6,000 lines of code for MapReduce were factored out of Nutch's initial code.
Hadoop Modules
Hadoop Distributed File System (HDFS): It is a file system used by Hadoop to store data. GFS, a Google paper, was published, and HDFS was built on top of it. According to the document, the files will be broken down into blocks and stored in nodes across the distributed architecture.
Map Reduce: This framework allows Java programs to perform parallel data processing using key-value pairs. The Map task converts input data into a data set that can be computed in Key value pair. The reduce task consumes the output of the Map task, and the output of the reducer produces the desired result.
Hadoop Common: These Java libraries are used by other Hadoop modules and start Hadoop.
Yarn: A third Resource Negotiator is used to manage the cluster and schedule jobs.
The Hadoop Distributed File System: An Overview (HDFS)
The Hadoop Distributed File System is a flexible, resilient, clustered approach to file management in a big data environment. Files do not end up in HDFS. It's a data service that provides a unique set of required capabilities when data volumes and velocity are high. Because data is written once and read many times, HDFS is an excellent choice for supporting big data analysis, rather than the constant read-writes of other file systems. The service, which consists of a "NameNode" and multiple "data nodes" running on commodity hardware, performs best when the entire cluster is in the same physical rack in the data center.
Only one master server exists in the HDFS cluster.
Because it is a single node, single-point failure may occur.
It keeps the file system namespace by performing operations like opening, renaming, and closing files.
It streamlines the architecture of the system.
DataNode
The HDFS cluster consists of many DataNodes.
Each DataNode contains a number of data blocks.
These data blocks hold information.
DataNode is in charge of the file system's clients' reading and writing requests.
It creates, deletes, and replicates blocks after receiving instructions from the NameNode.
Job Finder
The Job Tracker's job is to accept MapReduce jobs from clients and use NameNode to process the data.
NameNode responds by sending Job Tracker metadata.
Task Manager
For Job Tracker, it functions as a slave node.
It applies the task and the code from Job Tracker to the document.
A Mapper is another name for this procedure.
Layer of MapReduce
MapReduce is a parallel programming model for writing distributed applications developed at Google for efficient processing of large amounts of data (multi-terabyte data sets) on large clusters (thousands of nodes) of commodity hardware in a fault-tolerant and reliable manner. The MapReduce program is powered by Hadoop, an Apache open-source framework.
Advantages of Hadoop
Performance
The distributed processing and storage architecture of Hadoop enables it to process large amounts of data quickly. Hadoop beat the fastest supercomputer in the world in 2008. It divides the data from the input file into blocks and stores it in these blocks across multiple nodes. It also divides the user's task into sub-tasks assigned to these worker nodes and runs in parallel, improving performance.
Various Sources of Information
Hadoop is capable of handling a wide variety of data types. Data can be structured or unstructured and come from various sources, including email and social media. Hadoop is capable of extracting value from a diverse set of data. Hadoop accepts data in various formats, including text files, images, CSV files, XML files, and others.
Fault-Tolerant
Hadoop 3.0 uses erasure coding to provide fault tolerance. For example, using the erasure coding technique, 6 data blocks produce three parity blocks, resulting in 9 blocks in HDFS. The data block affected by a node failure can be recovered using these parity blocks and the remaining data blocks.
Cost-effective
Hadoop is a cost-effective solution because it stores data in a cluster of commodity hardware. Because commodity hardware is inexpensive, the cost of adding nodes to the framework is not prohibitively high. We only have 50% storage overhead in Hadoop 3.0, compared to 200 percent in Hadoop2.x. Because redundant data has decreased, fewer machines are required to store data.
Compatibility
Most new Big Data technologies, such as Spark and Flink, are compatible with Hadoop. They have processing engines that use Hadoop as a backend, which means they use Hadoop as a data storage platform.
Scalable
Hadoop works on the horizontal scalability principle, which states that rather than changing the device's configuration, such as adding RAM, disk, and so on, which is known as vertical scalability, The entire machine must be added to the cluster of nodes. Because nodes can be added on the fly, Hadoop is a scalable framework.
Several Languages Supported
On Hadoop, developers can code in various languages, including C, C++, Perl, Python, Ruby, and Groovy.
User-friendliness
The Hadoop framework handles parallel processing, so MapReduce programmers don't worry about it. Distributed processing is handled automatically on the backend.
Free and Open Source Software
Hadoop is an open-source technology, which means that the source code is free. We can change the source code to meet your specific needs.
Network Traffic is Low
Each job submitted by the user is split into several independent sub-tasks, which are then assigned to the data nodes, resulting in a small amount of code being transferred to data rather than a large amount of data being transferred to code low network traffic.
Exceptional Throughput
Hadoop stores data in a distributed manner, allowing for easy distributed processing. The term "throughput" refers to the amount of work completed in a given amount of time. A job is divided into small jobs that work on data chunks in parallel, resulting in high throughput.
Hadoop Applications with Real-Time Big Data
Hadoop is used by businesses to understand customer needs better.
Healthcare industries
Law Enforcement and Security
Hadoop applications in government
Finance industries
Applications of Hadoop in the Retail Industry
Advertisements Using Hadoop Platforms for Targeting
Machine performance optimization
Financial Trading and Forecasting Hadoop Applications
What are the stumbling blocks to implementing Hadoop?
Data protection. Another issue is the fragmented data security issues, which are being addressed by new tools and technologies. Kerberos authentication is a significant step toward securing Hadoop environments.
MapReduce programming isn't suitable for every problem. It works well for simple information requests and problems that can be broken down into smaller chunks, but it is ineffective for iterative and interactive analytic tasks. MapReduce requires a lot of files. Iterative algorithms need multiple map-shuffle/sort-reduce phases to complete because the nodes only communicate through sorts and shuffles. This results in multiple files being created between MapReduce phases, which is inefficient for advanced analytics.
There is a well-known talent shortage. Entry-level programmers with sufficient Java skills to be productive with MapReduce can be challenging. This is because distribution companies are racing to add relational (SQL) technology to Hadoop. MapReduce programmers are much more difficult to come by than SQL programmers. Hadoop administration appears to be a blend of art and science, requiring a working knowledge of operating systems, hardware, and Hadoop kernel settings.
Data governance and management to the fullest extent possible. Hadoop lacks comprehensive data management, data cleansing, governance, and simple metadata tools. Data quality and standardization tools are notably lacking.
Frequently Asked Questions
What is Hadoop, and what is an example of it?
Hadoop examples include: Retailers use it to better understand and serve their customers by analyzing structured and unstructured data. Hadoop-powered analytics are used for predictive maintenance in the asset-intensive energy industry, with data from the Internet of Things (IoT) devices feeding data into big data programs.
What's the difference between Hadoop and big data?
Big data is used to describe large, complex data sets that are difficult to analyze using traditional data processing applications. Hadoop, or Apache Hadoop, is a software framework for storing and processing large, complex data sets.
Where is Hadoop used?
Hadoop is an ample data storage and processing system. Hadoop stores data on low-cost commodity servers that run in clusters. It's a fault-tolerant distributed file system that allows for concurrent processing. The Hadoop MapReduce programming model stores and retrieves data from its nodes more quickly.
Conclusion
This is only the start. The Hadoop ecosystem is a large and growing collection of tools and technologies to reduce the size of your big data problems. Every piece of software used in the industry has its disadvantages and advantages. If the software is critical to the organization, maximizing the benefits while minimizing the flaws is possible. We can see that Hadoop's advantages outweigh its drawbacks, making it a viable Big Data solution.
We hope that this blog has helped you enhance your knowledge regarding the subject of Hadoop and how it can be used to process large amounts of data in this article, and if you would like to learn more, To learn more, see Operating System, Unix File System, File System Routing, and File Input/Output.
84+ registered