What is Hadoop? What is Its Definition and Uses?
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Hadoop is a powerful open-source framework that has revolutionized the world of big data analytics. It was created by Doug Cutting and Mike Cafarella in 2006 and is now maintained by the Apache Software Foundation. Hadoop allows organizations to store, process, and analyze vast amounts of structured data and unstructured data in a cost-effective manner. It is based on the distributed computing model, which enables users to perform complex operations on large datasets that would be impossible to process using traditional computing methods. In this blog, we will take you into what Hadoop is, how it works, and its significance in the field of big data analytics.
What is Hadoop?
Hadoop is an Apache open-source platform that stores, processes, and analyses extraordinarily large volumes of data. Hadoop is developed in Java, not OLAP (online analytical processing). It undergoes offline processing or batch processing. Facebook, Google, Yahoo, Twitter, LinkedIn, and many other sites use it. In addition, expanding the cluster only needs adding nodes.
When to Utilize Hadoop and Why?
Here are several fundamental truths that people should be aware of before looking at the evidence. Many businesses are utilizing Hadoop for various purposes. It's crucial to comprehend how you employ Hadoop, what else the software can provide, and how it can impact your company. The following details will aid in understanding that:
- Hadoop is the best option for processing extremely large volumes of data.
- Since it can store and prove any type of data in various formats and across various time periods. At any time, you are always free to modify it.
- Compared to alternative methods, data analysis and processing using parallelism are quick.
What are Hadoop's Components?
The HDFS, MapReduce, and YARN are the three main parts of Hadoop.
1. Hadoop HDFS:
The Hadoop Distributed File System is where the data in Hadoop is kept. NameNode and DataNode are the two daemons that are now active in Hadoop HDFS.
• NameNode: The HDFS master daemon is named NameNode. On the master nodes, it functions. It keeps the filesystem namespace up to date. The real data is not stored by NameNode. It saves metadata, including details about file blocks, file permissions, block locations, etc. The NameNode oversees the DataNode and guides them. Every three seconds, NameNode receives a heartbeat from a DataNode, indicating that the latter is still alive.
• DataNode: The HDFS slave daemon is called DataNode. The slave nodes that store the actual business data are called DataNodes. Based on the directives from NameNode, they are in charge of fulfilling the read/write requests from the client. To confirm their existence, DataNodes send messages to the NameNode that contain their heartbeats.
• Secondary NameNode: It is an additional Hadoop HDFS daemon. It serves as the primary NameNode's helper node. The image file and edit logs are downloaded by the secondary NameNode from the primary NameNode, and the edit logs are periodically applied to the Fsimage. The corrected image file is then returned to the NameNode. In this case, file system metadata is recovered using the last save Fsimage on the secondary NameNode if the original NameNode fails. It acts as the storage layer for Hadoop. On several cluster nodes, data is stored using the Hadoop Distributed File System. Blocks of the data are divided up and stored on multiple nodes. The block size, by default, is 128 MB. We can configure the block size to suit our requirements.
2. Hadoop MapReduce:
Every time a client wishes to process data in a Hadoop cluster, it first stores the data in Hadoop HDFS before writing a MapReduce program to complete the processing. The following is how Hadoop MapReduce operates:
• Hadoop separates the task into two categories of tasks, namely, map tasks and reduces tasks. These tasks were scheduled by YARN (which we will see later in this article). These processes take place on several data nodes.
• Input splits, or fixed-size components, are used to divide the input into the MapReduce process.
• For each input split, a single map task is generated, which executes a user-defined map function for each record. These map tasks are executed on the DataNodes that house the input data.
• The map task's output is written to the local disc as intermediate output.
• The map tasks' intermediate outputs are jumbled and sorted before being given to the reducer.
• The sorted intermediate output of the mapper is delivered to the node where the reducer task is running for a single reduce task. After merging these outputs, the user-defined reduce function receives them and processes them.
• The mapper's output is compiled and produced by the reduce function. The reducer's output is kept on HDFS.
• The user sets the number of reducers for various reduced functions. The map tasks divide their output when there are several reduced tasks, making one partition for each reduced task.
It is the Hadoop processing layer. Data from Hadoop HDFS is processed in parallel by Hadoop MapReduce on multiple cluster nodes. It separates the user-submitted task into several tasks and executes each one as a separate task on a piece of commodity hardware.
3. Hadoop YARN:
• ResourceManager: is YARN's chief daemon. To manage the resources across the cluster, it runs on the master node for each cluster. Scheduler and ApplicationManager are the ResourceManager's two main parts. The scheduler distributes resources across numerous clustered applications. ApplicationManager accepts the task provided by the client, arranges for the container to run the application-specific ApplicationMaster, and, in the event of a failure, restarts the ApplicationMaster container.
• NodeManager: NodeManager is one of YARN's slave daemons. It functions on each of the cluster's slave nodes. It is in charge of starting and overseeing the containers on nodes. Containers use a limited amount of resources, such as memory, CPU, and other resources, to run application-specific processes. NodeManager introduces itself to ResourceManager when it first launches. It periodically transmits a heartbeat to the Resource Manager. It provides the cluster with resources.
• ApplicationMaster: The individual application's ApplicationMaster tracks container status and progress while negotiating containers with schedulers. The ResourceManager schedules the MapReduce task and the ApplicationMaster, while the NodeManagers control the containers in which they are running. It is the Hadoop layer for managing resources and processes. Job scheduling in the cluster and resource sharing among the apps running in the cluster are the responsibilities of YARN.
These are Hadoop's three main building blocks.
How exactly does Hadoop operate?
Huge data sets can be distributed across a cluster of commodity computers using the Hadoop framework. Hadoop processing is carried out concurrently across numerous servers in parallel.
Hadoop receives data and applications from clients. HDFS (a vital part of Hadoop) manages the distributed file system and metadata, to put it simply. The input and outgoing data are then processed and converted by Hadoop MapReduce. Finally, YARN distributes the duties among the cluster members.
Clients can anticipate much more effective use of commodity resources with Hadoop, along with high availability and an integrated point of the failure monitoring system. Customers can also anticipate fast response times when submitting inquiries to connected business systems.
Overall, Hadoop offers a comparatively simple solution for businesses seeking to maximize.
What Is the Function of Hadoop?What Is the Function of Hadoop?
The Hadoop ecosystem's flexibility has enabled rapid expansion over time. Many tools and applications are now part of the Hadoop ecosystem and can be used to gather, store, process, analyze, and manage large amounts of data. The most well-liked programs include:
• Big data workloads frequently use the open-source, distributed processing system Spark. Apache Spark offers generic batch processing, streaming analytics, machine learning, graph databases, and ad hoc searches. It leverages in-memory caching and optimized execution for quick performance.
• Presto - A distributed, open-source SQL query engine designed for quick, on-the-fly data analysis. The ANSI SQL standard is supported, and this includes advanced queries, joins, aggregations, and window functions. Many data sources, such as the Hadoop Distributed File System (HDFS) and Amazon S3, can be processed using Presto.
• Hive - Provides access to Hadoop MapReduce through a SQL interface, enabling distributed and fault-tolerant data warehousing in addition to large-scale analytics.
• HBase - An open source, non-relational, versioned database that makes use of either the Hadoop Distributed File System or Amazon S3 (using EMRFS) (HDFS). Designed for random, strictly consistent, real-time access to tables with billions of entries and millions of columns, HBase is a massively scalable, distributed big data store.
• Zeppelin is an interactive notebook that allows for real-time data exploration.
Benefits or Advantages of Hadoop:
Big data issues are resolved using Hadoop. The following are some of Hadoop's advantages:
• Adding extra nodes to the Hadoop cluster will increase storage and computational power. As a result, it is not needed to purchase extra hardware. It is a less expensive solution as a result.
• It can manage both semi-structured and unstructured data.
• Hadoop clusters combine distributed computing and storage.
• The Hadoop framework offers the power and flexibility to perform previously impossible things.
• The HDFS layer in Hadoop offers fault tolerance, self-healing, and replication features. If a server or disc crashes, data replication occurs automatically.
• Hadoop assists in distributing data across numerous servers minimizes network overloading, and provides scalability, dependability, and a wealth of libraries for varied applications at a reduced cost.
Cons or Disadvantages of Hadoop
• Hadoop is a complicated application that is challenging to manage. The biggest issue is Hadoop's security, which is by default disabled owing to its extreme complexity. Your data may be at great risk if the person in charge of the platform doesn't know how to enable it.
• Speaking of security, managing Hadoop is a risky business because of its very nature. The framework is largely built in Java, a language that has often been abused by hackers.
• Hadoop lacks network- or storage-level encryption.
• Hadoop will have trouble scaling whenever it is managed by a single master.
• Because of its high capacity design, the Hadoop distributed file system is unable to effectively support the random reading of small files. Thus, it is not advised for enterprises with little data.
• Hadoop, like other open-source software, has experienced some stability concerns. It is highly advised that businesses make sure they are using the most recent stable version to prevent these problems.
• Google's cloud dataflow and Apache Flume are two prospective solutions that could improve the efficiency and dependability of data gathering, processing, and integration. By relying solely on Hadoop, many firms are also losing out on significant advantages.
• Hadoop's programming model is extremely constrained.
• Why does the built-in redundancy of Hadoop duplicate data necessitate greater storage space?
• The Hadoop map-reduce programming model is essentially a foundation for group management. It does not, however, facilitate gathering gushing information.
In conclusion, Hadoop is a powerful open-source framework that has revolutionized big data processing and storage. Its ability to handle large datasets in parallel on commodity hardware has made it the go-to solution for many organizations. It has become an essential technology for data engineers, data scientists, and developers who want to work with big data.
As we have seen in this blog, Hadoop has several advantages over traditional relational database systems, including scalability, fault tolerance, and cost-effectiveness. However, it also has some limitations, such as complexity and the need for specialized skills.
Despite its challenges, Hadoop's future looks promising, with the rise of new technologies like Apache Spark and the continued growth of big data. As the demand for big data processing and analytics increases, Hadoop is likely to remain a vital tool for managing and processing large datasets.
Overall, Hadoop's impact on the world of big data cannot be overstated, and its contributions will continue to shape the industry for years to come.