Hadoop Infrastructure

Hadoop infrastructure refers to the hardware and software components that make up a Hadoop cluster, a distributed computing environment designed to store, process, and analyze large volumes of data. Hadoop is commonly used for big data processing and analytics. Here are the key components of Hadoop infrastructure:

1. Hardware:

   • Cluster Servers: Hadoop typically runs on a cluster of commodity hardware servers. These servers can be categorized into master nodes and worker nodes.
   • Master Nodes: These nodes are responsible for coordinating and managing the cluster. They typically include the NameNode (for HDFS) and ResourceManager (for YARN).
   • Worker Nodes: Worker nodes are where data storage (DataNodes for HDFS) and data processing (NodeManagers for YARN) take place. They are the workhorses of the cluster.

2. Storage:

   • HDFS (Hadoop Distributed File System): HDFS is the primary storage system for Hadoop. It stores data across the cluster in a distributed and fault-tolerant manner. HDFS divides large files into blocks and replicates them across DataNodes for redundancy.
   • Secondary NameNode: Although not a primary storage component, the Secondary NameNode assists the NameNode in checkpointing metadata to improve HDFS reliability.

3. Resource Management:

   • YARN (Yet Another Resource Negotiator): YARN is the resource management layer in Hadoop. It manages cluster resources, allocates CPU and memory to running applications, and oversees job scheduling. YARN consists of a ResourceManager (for global resource management) and NodeManagers (for local resource management on worker nodes).

4. Data Processing Engines:

   • MapReduce: MapReduce is a batch processing model for parallel and distributed data processing. It is one of the earliest data processing engines in Hadoop and is still widely used.
   • Apache Spark: Spark is a versatile data processing framework that supports batch processing, real-time streaming, machine learning, and graph processing. It has gained popularity for its speed and ease of use.
   • Apache Hive: Hive provides a SQL-like query language for querying and analyzing data stored in HDFS. It translates HiveQL queries into MapReduce or Tez jobs.
   • Apache Pig: Pig is a high-level platform for creating MapReduce programs using a scripting language called Pig Latin. It simplifies the creation of complex data transformations.

5. Data Ingestion:

   • Flume: Flume is a data ingestion tool that collects, aggregates, and moves large volumes of data from various sources to Hadoop.
   • Sqoop: Sqoop is a tool for transferring data between Hadoop and relational databases, allowing data import/export to and from Hadoop.
   • Kafka: Kafka is a distributed messaging system often used for real-time data streaming and event data ingestion into Hadoop.

6. Management and Monitoring:

   • Ambari: Apache Ambari is a management and monitoring tool that simplifies the installation, configuration, and management of Hadoop clusters.
   • Cloudera Manager: Cloudera Manager is a similar management and monitoring tool provided by Cloudera, a Hadoop distribution vendor.
   • Ganglia, Nagios, and other monitoring tools are often used to track cluster performance, health, and resource utilization.

7. Security:

   • Kerberos: Kerberos is commonly used for authentication and security in Hadoop clusters, ensuring that only authorized users and services can access the cluster.
   • Hadoop Security: Hadoop provides various security features, including Access Control Lists (ACLs), encryption, and auditing, to protect data and cluster resources.

8. Ecosystem Components:

   • Hadoop has a rich ecosystem of components and libraries, including HBase (NoSQL database), Spark MLlib (machine learning), Mahout (machine learning), and more, that extend its capabilities for various data processing and analysis tasks.