Hadoop

These are the main components and features of Hadoop:

Hadoop Distributed File System (HDFS):

• Distributed Storage: HDFS allows the storage of large files by dividing them into blocks and distributing them across multiple nodes in a cluster.
• Replication: Data blocks are replicated across several nodes to ensure fault tolerance and high availability.
• Data Access: Provides fast access to data through a distributed file system.

MapReduce:

• Programming Model: MapReduce is a programming model for the distributed processing of large data sets. It is based on two main functions: Map, which filters and sorts the data, and Reduce, which aggregates and summarizes the results.
• Parallel Processing: Enables parallel processing of data across multiple nodes, improving processing efficiency and speed.

YARN (Yet Another Resource Negotiator):

• Resource Management: YARN manages cluster resources and schedules applications, allowing multiple applications to run simultaneously on the cluster.
• Scalability: Facilitates Hadoop’s scalability by allowing better utilization of cluster resources.

Hadoop Ecosystem: Hadoop has an ecosystem of tools and projects that complement its functionality, such as:

• Hive: A data warehouse system that provides an SQL interface for querying data stored in HDFS.
• Pig: A high-level language for processing data in Hadoop.
• HBase: A distributed NoSQL database that runs on top of HDFS.
• Spark: A fast data processing engine that can run on Hadoop and improves in-memory processing performance.
• Sqoop: A tool for transferring data between Hadoop and relational databases.
• Flume: A service for ingesting large amounts of streaming data into HDFS.

In summary, Hadoop is a powerful and flexible platform for storing and processing big data, enabling organizations to manage and analyze large volumes of data efficiently.