Hadoop System

A Hadoop system refers to a distributed computing framework and ecosystem designed for storing, processing, and analyzing large volumes of data across a cluster of commodity hardware. The core components of a Hadoop system include the Hadoop Distributed File System (HDFS) and the Hadoop MapReduce programming model. Additionally, the Hadoop ecosystem consists of various related projects and tools that extend the capabilities of Hadoop.

Here are some key components and aspects of a Hadoop system:

1. Hadoop Distributed File System (HDFS):

   • HDFS is the primary storage system of a Hadoop system. It is designed to store vast amounts of data in a distributed and fault-tolerant manner.
   • Data is divided into blocks, typically 128 MB or 256 MB in size, and distributed across multiple nodes in the cluster.
   • HDFS provides high availability and fault tolerance by replicating data across nodes, ensuring that data remains accessible even if some nodes fail.

2. Hadoop MapReduce:

   • MapReduce is a programming model and processing framework for distributed data processing in Hadoop.
   • It divides data processing tasks into two stages: the “map” phase for data transformation and the “reduce” phase for aggregation and summarization.
   • MapReduce allows developers to write custom code to process data in parallel across the cluster.

3. Hadoop Ecosystem:

   • The Hadoop ecosystem consists of numerous projects and tools that work alongside HDFS and MapReduce to provide a wide range of data processing, analytics, and management capabilities.
   • Key projects in the Hadoop ecosystem include Apache Hive (SQL-like querying), Apache Pig (data scripting), Apache HBase (NoSQL database), Apache Spark (in-memory processing), Apache Flink (stream processing), and many more.

4. Resource Management:

   • Hadoop clusters require resource management to allocate CPU and memory resources efficiently among various tasks and jobs.
   • Common resource management systems used in Hadoop clusters include Hadoop YARN (Yet Another Resource Negotiator) and Apache Mesos.

5. Cluster Management:

   • Tools like Apache Ambari and Cloudera Manager help in the provisioning, configuration, and monitoring of Hadoop clusters.
   • These tools provide user-friendly interfaces for managing and maintaining Hadoop clusters.

6. Data Ingestion:

   • Data is ingested into the Hadoop system from various sources, including logs, sensors, databases, and external data stores.
   • Apache Flume and Apache Sqoop are tools commonly used for data ingestion.

7. Data Processing:

   • Data processing is the core function of a Hadoop system, and it can involve batch processing, real-time processing, and machine learning tasks.
   • Apache Spark and Apache Flink are popular for real-time and batch processing, while Apache Mahout and Apache MLlib offer machine learning capabilities.

8. Data Storage Formats:

   • Data in Hadoop is often stored in various formats, such as Avro, Parquet, and ORC, to optimize storage and processing efficiency.

9. Data Security and Governance:

   • Data security and governance are essential aspects of a Hadoop system, and projects like Apache Ranger and Apache Atlas help manage access control and metadata.

10. Data Visualization and Reporting:

    • Tools like Apache Zeppelin and Apache Superset enable data visualization and reporting on top of Hadoop data.