Apache Hadoop Framework

The Hadoop framework is commonly used in big data analytics and is composed of several core components:

1. Hadoop Distributed File System (HDFS):

   • HDFS is the primary storage system in Hadoop. It is designed to store and manage large volumes of data across a cluster of commodity hardware.
   • HDFS breaks data into smaller blocks (typically 128 MB or 256 MB in size) and replicates them across multiple nodes in the cluster for fault tolerance.

2. MapReduce:

   • MapReduce is a programming model and processing framework for distributed data processing. It allows developers to write parallelizable programs to process and analyze large datasets.
   • A MapReduce job consists of two main phases: the Map phase, which processes data and generates intermediate key-value pairs, and the Reduce phase, which aggregates and processes the intermediate data.

3. Yet Another Resource Negotiator (YARN):

   • YARN is the resource management and job scheduling component of Hadoop. It enables multiple data processing frameworks, like MapReduce and Apache Spark, to run concurrently on the same Hadoop cluster.
   • YARN allocates resources (CPU, memory) to applications and manages their execution.

4. Hadoop Common:

   • Hadoop Common provides common utilities and libraries used by all Hadoop modules. It includes tools and APIs for distributed computing, security, and more.

5. Hadoop Ecosystem:

   • Hadoop has a rich ecosystem of projects and tools that extend its capabilities and address various aspects of big data processing. Some popular components of the Hadoop ecosystem include:
     • Hive: A data warehousing and SQL-like query language for Hadoop.
     • Pig: A high-level platform for creating MapReduce programs.
     • HBase: A NoSQL database for real-time, distributed storage.
     • Spark: A fast and general-purpose cluster computing framework.
     • Impala: A SQL query engine for Hadoop.
     • Kafka: A distributed streaming platform.
     • Many more for machine learning, data ingestion, data processing, and more.

6. Scalability and Fault Tolerance:

   • Hadoop is designed to scale horizontally, which means you can add more commodity hardware to the cluster as your data and processing needs grow.
   • It provides fault tolerance by replicating data and tasks across multiple nodes, ensuring that the system can continue working even if some nodes fail.

7. Data Locality:

   • Hadoop strives to move computation close to data. This concept, known as data locality, reduces network traffic and improves processing efficiency.

8. Security Features:

   • Hadoop offers various security features, including Kerberos authentication, Access Control Lists (ACLs), and encryption, to protect data and ensure secure access.

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