Hadoop Framework Overview and History Hadoop is a distributed computing framework for building and running large data analytics solutions on distributed...
Hadoop is a distributed computing framework for building and running large data analytics solutions on distributed and parallel computing systems. It is an open-source project with extensive documentation and a vibrant community of contributors and users.
History:
2007: Hadoop project started as an academic project at the Apache Research Lab.
2010: Google announced their acquisition of the project and renamed it Apache Hadoop.
2012: Hadoop 1.0 was released as the first official release under the Apache 2.0 license.
2016: Hadoop 2.0 was released, introducing significant improvements like YARN (Yet Another Resource Negotiator) for resource management and data locality.
2019: Hadoop 3.0 was released, introducing support for distributed file systems like HDFS (Hadoop Distributed File System).
Key Features:
MapReduce: A core component responsible for parallel processing of large datasets.
HDFS: A distributed file system that provides high performance and fault tolerance for storing and accessing big data.
YARN: A resource manager that allocates resources to running applications efficiently.
Apache Hive: A data warehouse system that allows SQL-based data analysis on top of Hadoop data.
Benefits:
Scalability: Can be used to analyze petabytes of data on commodity hardware.
Performance: Provides high performance and fault tolerance for data processing.
Flexibility: Can be extended with various tools and technologies.
Open-source: Provides freedom and flexibility in its usage.
Examples:
Data processing: Processing and analyzing large datasets in various industries like finance, healthcare, and research.
Real-time analytics: Building real-time dashboards and alerts for critical business events.
Hadoop on the cloud: Running Hadoop on cloud platforms for scalability and flexibility