What is Hadoop?
Hadoop is an open-source framework for storing and processing large datasets across clusters of computers using simple programming models.
It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware that is big and expensive, Hadoop breaks up large datasets and distributes them across clusters of commodity hardware.
This makes Hadoop a very cost-effective way to store and process large amounts of data.
Benefits of using Hadoop:
Scalability: Hadoop can be scaled up to thousands of nodes, making it a good choice for storing and processing very large datasets.
Reliability: Hadoop is designed to be reliable, and it can tolerate node failures.
Cost-effectiveness: Hadoop is a cost-effective way to store and process large amounts of data, as it can be run on commodity hardware.
Flexibility: Hadoop can be used to store and process a variety of data types, including structured, unstructured, and semi-structured data.
Main components of Hadoop:
Hadoop Common: This is a collection of utilities that are used by all of the other Hadoop components.
Hadoop Distributed File System (HDFS): This is a distributed file system that stores data across multiple nodes in a cluster. HDFS is designed to be reliable and scalable, and it can store petabytes of data.
Hadoop YARN: This is a framework for job scheduling and resource management. YARN allows Hadoop to run multiple jobs simultaneously, and it ensures that jobs are allocated the resources they need.
Hadoop MapReduce: This is a programming model for processing large datasets in parallel. MapReduce breaks down a large dataset into smaller chunks, and then it processes each chunk in parallel on multiple nodes in a cluster.
Big data applications where Hadoop is used:
Data warehousing: Hadoop can be used to store and process large amounts of structured data, such as data from customer transactions or sales records.
Data analytics: Hadoop can be used to analyze large amounts of data to find patterns and trends. For example, Hadoop can be used to analyze customer data to identify which products are most popular or to identify customer churn.
Machine learning: Hadoop can be used to train and run machine learning models on large datasets. For example, Hadoop can be used to train a machine learning model to predict customer behavior or to detect fraud.