Hadoop

Hadoop is an open-source framework developed by the Apache Software Foundation used for storing and processing large datasets in a distributed computing environment. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage. Hadoop is built to handle massive amounts of data in a fault-tolerant and efficient manner.

Core Components

• Hadoop Distributed File System (HDFS):
  • Purpose: HDFS is designed to store large datasets reliably and to stream those datasets to user applications at high bandwidth.
  • Architecture: It follows a master-slave architecture. The HDFS cluster consists of a single NameNode (master) that manages the file system namespace and regulates access to files by clients. There are multiple DataNodes (slaves) that manage storage attached to the nodes they run on.
  • Features:
    • Fault Tolerance: Data is replicated across multiple nodes to ensure availability in case of hardware failure.
    • Scalability: Can scale up to thousands of nodes.
    • High Throughput: Optimized for high throughput of data access rather than low latency.
• MapReduce:
  • Purpose: A programming model and execution engine that is used for processing and generating large datasets.
  • Components:
    • JobTracker: The master node that manages the jobs and resources in the cluster.
    • TaskTracker: The slave nodes that execute the tasks as directed by the JobTracker.
  • Phases:
    • Map Phase: Processes input data and converts it into a set of key-value pairs.
    • Reduce Phase: Processes the intermediate key-value pairs to generate the final output.
• 1 YARN (Yet Another Resource Negotiator):
  • Purpose: Manages resources in the Hadoop cluster and schedules jobs.
  • Components:
    • ResourceManager: The master that controls and allocates resources.
    • NodeManager: Manages resources and monitoring on the individual nodes.
    • ApplicationMaster: Manages the lifecycle of applications running on YARN.
• Hadoop Common:
  • Purpose: Provides common utilities and libraries that support the other Hadoop components. It includes the necessary Java libraries and files needed to start Hadoop.

Ecosystem and Tools

Hadoop has a rich ecosystem of tools and frameworks that enhance its functionality:

• Hive: A data warehouse infrastructure that provides data summarization, query, and analysis. It uses a SQL-like language called HiveQL.
• Pig: A high-level platform for creating MapReduce programs using a language called Pig Latin.
• HBase: A distributed, scalable, big data store modeled after Google’s Bigtable and written in Java.
• Sqoop: A tool designed for efficiently transferring bulk data between Hadoop and structured data stores such as relational databases.
• Flume: A service for efficiently collecting, aggregating, and moving large amounts of log data.
• Oozie: A workflow scheduling system to manage Hadoop jobs.
• Zookeeper: A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
• Spark: A fast and general engine for large-scale data processing that can run on Hadoop clusters.

Key Features

• Scalability: Can scale horizontally to handle more data by adding more nodes to the cluster.
• Cost-Effective: Uses commodity hardware, making it a cost-effective solution for big data processing.
• Flexibility: Can process structured, semi-structured, and unstructured data.
• Fault Tolerance: Automatically handles hardware failures by replicating data across multiple nodes.
• Distributed Computing: Processes data in parallel across multiple nodes, increasing processing speed and efficiency.

⠀Use Cases

• Data Warehousing: Companies use Hadoop for storing vast amounts of structured and unstructured data.
• Log Processing: Hadoop is used to analyze and process server logs for insights and troubleshooting.
• Recommendation Systems: Used by companies like Netflix and Amazon for building recommendation engines.
• Data Archival: Storing large volumes of historical data for compliance and analysis.

Spark

Apache Spark is an open-source, distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Originally developed at UC Berkeley’s AMPLab, Spark offers a fast and general-purpose cluster-computing framework. It extends the MapReduce model to support more types of computations and optimizes performance by keeping data in memory whenever possible.

Key Features

• Speed:
  • Spark processes data in memory, which significantly reduces the time to read and write to disk. This makes Spark up to 100 times faster than Hadoop MapReduce for certain applications.
  • The DAG (Directed Acyclic Graph) execution engine optimizes task execution by breaking down jobs into stages and tasks, which can be executed in parallel.
• Ease of Use:
  • Spark provides high-level APIs in Java, Scala, Python, and R. This versatility allows developers to use the language they are most comfortable with.
  • The interactive shell provided by Spark allows for real-time data analysis and debugging.
• Compatibility:
  • Spark is compatible with Hadoop’s HDFS and YARN, allowing it to be deployed on existing Hadoop clusters.
  • It also supports a wide range of data sources, including HBase, Cassandra, Kafka, and more.