Google Hadoop

Google’s involvement with Hadoop is closely related to the origins of Hadoop itself. Hadoop, as an open-source framework for distributed computing and storage, was inspired by Google’s proprietary technologies for processing and analyzing large-scale data. Here’s how Google is connected to Hadoop:

1. Google’s MapReduce:

   • Google’s MapReduce is a programming model and processing framework used internally at Google for distributed data processing.
   • MapReduce was introduced in a seminal paper by Google researchers Jeffrey Dean and Sanjay Ghemawat in 2004. This paper served as a foundational inspiration for Hadoop’s MapReduce framework.

2. Hadoop’s Origin:

   • Hadoop, as an open-source project, was created by Doug Cutting and Mike Cafarella, with inspiration from Google’s MapReduce and Google File System (GFS).
   • Doug Cutting, one of the creators of Hadoop, named it after his son’s stuffed elephant, which is a reference to the yellow elephant in the Hadoop logo. The name was meant to imply that Hadoop could handle large volumes of data.

3. Hadoop’s MapReduce Implementation:

   • Hadoop’s MapReduce framework was developed as an open-source implementation of Google’s MapReduce concept.
   • It allows developers to write distributed data processing applications using the same model and principles as Google’s MapReduce.

4. Hadoop’s Success in the Industry:

   • Hadoop quickly gained popularity in the industry as a scalable and cost-effective solution for big data processing.
   • The Apache Hadoop project, which includes the Hadoop Distributed File System (HDFS) and Hadoop MapReduce, became a widely adopted framework for processing and analyzing large datasets.

5. Google’s Big Data Services:

   • While Google’s MapReduce is proprietary and not available for external use, Google offers various cloud-based big data services, including Google Cloud Dataflow, which is based on the same principles of data processing as MapReduce.