When Yahoo! and Google launched their groundbreaking search engines, they were confronted with a stagnant data issue. Their engines were collecting enormous volumes of data, and they needed a way to make sense of it all. So, these businesses had to figure out what information they were collecting and how to monetize it.
Every day, 2.5 quintillion bytes of new data are created in the universe. By 2020, data will have grown at a compound annual rate of 40%, resulting in a total of roughly 45 zettabytes. Hadoop was developed due to its simplicity and ability to handle large volumes of data. Using Hadoop, significant problems might be split into smaller components, allowing faster and more cost-effective research.
It is possible to process the information and recombine the small pieces to present findings by splitting the big data problem into small parts that you can process in parallel. A Yahoo engineer, Doug Cutting, created Hadoop, now administered by the Apache Software Foundation as an open-source project. You can utilize it as long as you comply with the terms of the Apache License Version 2.0. You will understand the Big Data framework using Hadoop and Spark as part of the Big Data Hadoop certification training.
What is Hadoop?
Apache Software Foundation’s Hadoop is a free and open-source project. It offers a software framework for distributing and running programs across a cluster of servers based on Google’s Map-Reduce programming model and file system (GFS).
For the Nutch search engine project, Hadoop was created initially. There are no restrictions on using Hadoop because it’s free and built-in Java. When working with massive datasets, it makes efficient use of commodity hardware in a cluster setting.
It can do it with a single machine, but the power of Hadoop comes from clusters, and it can expand from one machine to many thousands of nodes. It’s important to understand that Hadoop is made up of two parts:
1. Hadoop Distributed File System (HDFS)
- HDFS is a highly fault-tolerant, distributed, dependable, and scalable system in terms of data storage.
- Multi-node HDFS holds many copies of the same data; files are saved in 64-MB blocks and are spread across multiple workstations.
- In a typical Hadoop cluster, there is one namenode and many datanodes.
- The Map-Reduce programming approach can process large amounts of data in parallel, which divides the work into several tasks.
- It’s also a paradigm for distributing massive data sets among several nodes, which is why it’s so popular.
What is Hadoop Architecture?
A single master node, as well as several worker nodes, make up a tiny Hadoop cluster. A JobTracker, TaskTracker, NameNode, and DataNode make up the master node. While it is possible to create data-only worker and computer-only worker nodes, a slave or worker node serves as a DataNode and TaskTracker. The Hadoop Distributed File System (HDFS) is handled in a larger cluster by a NameNode server that includes the file system index and a backup NameNode that can take snapshots of the NameNode’s memory structures, minimizing file-system corruption and decreasing data loss.
Apache Hadoop Architecture
- Hadoop Common — Consists of libraries and utilities used by other Hadoop modules
- A distributed file system that stores commodity machines’ data, providing very high aggregate bandwidth across the cluster, is HDFS.
- Hadoop YARN is a resource management platform that computes resources in clusters and uses them to schedule user applications.
- Hadoop MapReduce is a programming model for large-scale data processing.
Why is Hadoop important?
- Possibility of storing and processing large volumes of data quickly, regardless of a kind: As data volumes and types, particularly from social media and the Internet of Things (IoT), continue to rise, this is an increasingly vital aspect to consider.
- Data processing speed: Hadoop’s distributed computing technology is capable of quickly processing large amounts of data. There is more processing power when you use more computing nodes.
- Fault tolerance: Hardware failure will not affect data or application processing. Jobs are immediately routed to other nodes whenever a node goes down, ensuring that distributed computing fails. Automatically, several copies of all data are kept on hand just in case something goes wrong.
- Flexibility: Unlike typical relational databases, no preprocessing of data is required before storage in NoSQL systems. With unlimited storage, you can decide afterward how to utilize it. Videos, text, and images are examples of unstructured data.
- Low price: The open-source framework may store large amounts of data on commodity hardware with no additional cost.
- Scalability: Adding nodes is a simple way to expand your system’s capacity to handle more data. There isn’t much of an administrative burden.
- Improve sentiment analysis of the 12 Terabytes of tweets created every day by turning them into valuable data.
- Incorporate the analysis of billions of customer complaints to find out why customers are leaving.
- In real-time, show them ads for tempting deals based on an analysis of their search/buying patterns.
- Discover possible fraud among the millions of credit card transactions made every day.
Hadoop opens up more career options because of the increased demand for its services. According to the Big Data market projection, there will be an increased demand for Big Data engineers. Since the amount of generated data is expanding exponentially, Big Data will be around for a long time.
According to Avendus Capital’s analysis, India’s big data IT market was worth $1.15 billion in 2015. About one-fifth of the country’s KPO business, worth about $5.6 billion, was accounted for by big data analytics. According to The Hindu, India alone would have a shortfall of nearly a quarter-million Big Data scientists by the end of 2018. Consequently, a profession in Big Data Analysis utilizing Hadoop offers considerable potential for advancement.
Many multinational corporations (MNCs) use Hadoop and see it as essential to their operations, proving the significance of the technology. Many people believe that only social media firms make use of this technology. In reality, Hadoop is now being used in a wide range of different businesses to deal with BIG DATA.