Vanilla Hadoop

“Vanilla Hadoop” refers to the standard and original distribution of the Apache Hadoop framework without any additional components, customizations, or third-party extensions. It includes the core Hadoop components that are essential for distributed storage and batch processing of large datasets. These core components are:

1. Hadoop Distributed File System (HDFS): Vanilla Hadoop includes HDFS, which is a distributed file system designed for storing and managing large volumes of data across a cluster of commodity hardware. HDFS divides data into fixed-size blocks and replicates them across multiple DataNodes for fault tolerance.

2. MapReduce: Vanilla Hadoop uses the MapReduce programming model and processing framework for distributed data processing. MapReduce is designed for batch processing of data and is based on the concepts of map and reduce tasks.

In a Vanilla Hadoop setup, you typically interact with HDFS and run MapReduce jobs to process data. Here’s a basic overview of how it works:

• Data is ingested into HDFS, where it is divided into blocks and distributed across the cluster.

• MapReduce jobs are written in Java or other compatible languages and consist of two main phases:

  • Map Phase: Data is processed in parallel by mapper tasks, generating intermediate key-value pairs.
  • Reduce Phase: The intermediate key-value pairs are shuffled and sorted, and reducer tasks process and aggregate them to produce the final output.

• MapReduce jobs can be used for various data processing tasks, including data transformation, filtering, sorting, and aggregation.

While Vanilla Hadoop provides the foundational components for distributed data storage and batch processing, it is often considered the building block for more extensive Hadoop-based ecosystems. Organizations typically enhance Vanilla Hadoop by integrating additional components and tools for specific use cases, such as real-time processing, interactive querying, machine learning, and data warehousing.