What Is Hadoop In Big Data? Explained In Simple Terms

Hadoop is the open source framework more specifically it is an "Apache Framework" and it is used to deal with big data. The main purpose of the Hdoop is to collect data from multiple distributed sources and thus further processing it.

Hadoop In Big Data Analytics

The Hadoop ecosystem consists of open-source software solutions that enable you to store and process enormous volumes of data. Tools in this ecosystem include:

• HDFS (Hadoop Distributed File Systems)
• YARN (Yet Another Resource Negotiter)
• MapReduce

Apache Hadoop is also equipped with a vast ecosystem, which is used for big data analytics purposes, so basically, Hadoop is defined as:

1. Hadoop Distributed File System (HDFS): HDFS is a distributed file system that stores large datasets across a cluster of machines. It divides data into blocks and replicates them across multiple nodes for fault tolerance.

2. MapReduce: Hadoop includes a MapReduce framework, which allows you to write and run MapReduce jobs for processing data stored in HDFS

3. YARN (Yet Another Resource Negotiator): YARN is the resource management layer of Hadoop. It manages resources (CPU, memory) and schedules applications for execution. It decouples the resource management from the MapReduce programming model, allowing Hadoop to support various processing frameworks beyond MapReduce.

4. Hadoop Common: This component includes the libraries, utilities, and tools necessary for running Hadoop clusters.

5. Hadoop Ecosystem: Hadoop has a rich ecosystem of related projects, including Hive (SQL-like querying), Pig (data transformation), HBase (NoSQL database), Spark (in-memory processing), and many others. These projects leverage the Hadoop infrastructure for various data processing tasks.

Hadoop is commonly used in big data environments for processing and analyzing vast amounts of data. It provides scalability and fault tolerance, making it suitable for various applications, from batch processing to real-time analytics. The Hadoop ecosystem has grown to include various tools and libraries for specific data processing and analysis needs.

MUST READ: WHAT IS BIG DATA AND WHAT IT IS USED FOR

Why Hadoop?

• Cost-effectiveness
• High-level scalability
• Faster data processing
• Data locality
• Possibility of processing all types of data.

So, Apache Hadoop is the framework that is used to deal with huge amounts of data, thus providing multiple features so that it is easily understandable by big data analysts.

Conclusion:

The Hadoop ecosystem components provide a diverse set of capabilities, including structured data analysis and SQL querying (Hive), real-time NoSQL data storage (HBase), machine learning (Mahout), and flexible data storage and retrieval (NoSQL databases). The component selection is determined by the specific requirements of the data processing and analysis jobs.