Discover the power of Hadoop with our guide to Hadoop features and advantages. From its ability to handle large amounts of data to its cost-effective scalability, Hadoop is an essential tool for modern businesses. Whether you’re new to Hadoop or looking to expand your knowledge, our guide covers everything you need to know about this powerful big data processing platform. Join us and unlock the full potential of Hadoop today!
Apache Hadoop is among the most popular open-source tools used to make sense of big data. Every company in today’s data-driven society must constantly analyse information. Hadoop is a collection of technologies and tools for handling large amounts of data, and it is becoming increasingly popular for this purpose.
In this article, we will learn about Hadoop features and advantages, components of Hadoop and properties and limitations of Hadoop.
What is Hadoop: Definition
Hadoop is a well-known name in the tech industry and has become very famous. If you’re here because you’re interested in how quickly Hadoop became popular, you’re in the right place. Here, you’ll find a detailed look at the many valuable things Hadoop can do. Hadoop is a free and open-source framework made by the Apache Software Foundation for processing large, different datasets in a distributed way using a cluster of cheap computers and other equipment that is easy to find. Hadoop is a safe and reliable way to store and look at data together.
Hadoop applications run on groups of cheap servers that can handle vast amounts of data. A commodity computer is affordable for most people and easy to get. The main reason to use them is to boost processing power cheaply.
Data in Hadoop is kept on a distributed system, which works much like a computer’s local file system. Based on the “Data Locality” principle, its processing model is set up so that servers in a cluster that contains data also handle the logic needed to analyse the data. This is a shortened version of a program written in a higher-level language, like Java, that does the same thing. This kind of software can work with the data in Hadoop HDFS.
What are the essential features of Hadoop?
The features of Hadoop are as follows:
Hadoop is a project that anyone can work on
Anyone can use the Hadoop software stack for free. “Open source” software is software whose source code can be changed, looked at, and analysed by anyone who wants to. The code can be altered to fit each business’s needs.
The ability of a Hadoop cluster to grow is unmatched.
Hadoop is a very scalable storage platform because it can store and share large sets of data across many cheap computers working together at the same time. Hadoop lets businesses run applications that use hundreds of terabytes of data across several nodes, which is impossible with traditional relational database management systems (RDBMS).
With Hadoop, it is easy to deal with failures.
The most important thing about Hadoop is that it can handle mistakes. Hadoop Distributed File System (HDFS) is built with a way to copy files in case something goes wrong. Hadoop makes multiple copies of each block on all of the devices, depending on the replication factor (by default, it is 3). So, if one system in the cluster fails, data can be accessed from other machines in the set with a copy of the same data. Erasure coding has taken the place of this way of copying data in Hadoop 3. Erasure coding is becoming increasingly popular because it can handle mistakes and save space. With Erasure Coding, you need less than 50% more storage space.
Hadoop is quite economical.
Hadoop also gives companies exploring large data sets a low-cost way to store their information. Relational database management systems that have been used for years can’t handle this much data because it would be too expensive to add more space. In the past, to keep costs low, many businesses had to sort data and do analyses based on guesses about the most valuable information. It would cost too much to keep the raw data so that it would be erased. This method might have worked in the short term, but when the organisation’s goals changed, the entire collection of raw data was no longer available. On the other hand, Hadoop is a scale-out system that can store all of a business’s data cheaply for later use.
Hadoop is very fast when it comes to processing data.
Hadoop puts a lot of emphasis on data distribution, which could lead to faster processing times. Hadoop HDFS is in charge of storing data in a distributed way, and MapReduce makes the parallel processing of the data possible.
Let us now discuss the components of Hadoop.
Components of Hadoop
Hadoop has four core components, which are the following:
- HDFS
- YARN
- MapReduce
- Hadoop Common
HDFS: The Hadoop Distributed File System (HDFS) is the backbone of the Hadoop ecosystem. It distributes massive datasets over several organised or unstructured nodes and keeps track of their information through log files.
- HDFS is made up of two main parts, i.e.
- Name node
- Data Node
- The Name Node is virtual since it holds metadata (information about information), which takes up far less space than the data nodes, which store the actual information. The data nodes are just another piece of hardware in a distributed system.
- Hadoop is unquestionably economical.
- HDFS is at the system’s core since it coordinates all the hardware and clusters.
YARN: “Yet Another Resource Negotiator,” or “YARN,” is a tool to coordinate how resources are shared across a cluster. It ensures that Hadoop’s resources are spread out evenly and schedules tasks.
- They have three parts, i.e.
- Resource Manager
- Nodes Manager
- Application Manager
- The node manager assigns resources like CPU, memory, and speed to each machine.
- After that, the node manager must acknowledge the resource manager. The only person who can give resources to applications in a system is the resource manager.
- The application manager acts as a middleman between the node manager and the resource management, helping the two agree when necessary.
MapReduce: By using parallel and distributed algorithms, MapReduce allows you to carry over the logic of processing and makes it easier to write applications that turn large sets of data into smaller ones that are easier to work with.
- MapReduce uses two functions, called Map() and Reduce(), which do the following:
- Map() sorts and filters data, putting them into groups.
- The result of the Map is a key-value pair, which is then used by the Reduce() method.
- Reduce(), as its name suggests, sums up the mapped data by adding them together. Simply put, Reduce() uses the output of Map () as its input and combines the tuples into a smaller set of tuples.
Hadoop Common: Hadoop Common is a group of programmes that help the other three parts of Hadoop work. Hadoop’s YARN, MapReduce, and HDFS rely on this collection of Java libraries and tools to function correctly.
Moving forward, it’s time to discuss a few advantages of Hadoop.
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Advantages of Hadoop
Here are the essential benefits of Hadoop:
Cost-cutting: Hadoop’s low cost comes from the fact that anyone can download and use it for free. Using standard hardware for Hadoop also helps keep costs down. To deal with big data, RDBMSs need complex and expensive infrastructure.
Improvements to scalability: Hadoop is a very scalable platform because it can spread vast amounts of data across many low-cost nodes. These parts are grouped so that they can be processed at the same time. Hadoop also lets businesses change the number of these nodes, or computers, based on what they need.
Flexible: Hadoop can be used in many different ways. It can work with structured, semi-structured, and unstructured datasets, making it worthwhile.
Minimal Traffic: Because each task is broken up into smaller tasks, Hadoop can keep the amount of network traffic low. More importantly, one of these subtasks is given to each data component in Hadoop. Less data is being sent across the network because of this.
Lastly, we will discuss the limitations of Hadoop.
Limitations of Hadoop
Hadoop has a lot of good points, but the framework also has some limitations. Here are a few examples:
Security Issues:
By default, the security function in Hadoop is turned off. To ensure the safety function is on, the user must be careful. Kerberos, which Hadoop uses, also provides that data is safe and secure. On the other hand, Kerberos is notoriously hard to manage and set up.
Problems in Processing:
Hadoop can only be used for batch processing, meaning the user can’t control what’s happening in the background. This makes it less likely that we’ll be able to get a result back quickly. Hadoop’s data processing also has problems because it deals with large amounts of data (terabytes or petabytes), which can be hard to keep track of.
Highly Vulnerable:
Hadoop is made with Java, which is a programming language. Because many people use Java, cybercriminals can now attack the whole Hadoop infrastructure.
Conclusion
Hadoop is a popular framework because it is free and has unique features like high availability and data integrity. It’s user-friendly and can handle large clusters with little effort. It gives us confidence that our data transfer rates are effectively being processed and distributed. The proximity of its data lowers the system’s need for bandwidth. The structure is written in Java. It uses C codes and shell scripts to work on a wide range of standard hardware and only handles large datasets. Hadoop is a data analyst expertise for massive data technologies, and firms are paying a lot of money for it because they know it will be in high demand in the future.
