Key Differences Between Hadoop 1 and Hadoop 2 for Big Data Processing

When comparing Hadoop 1 and Hadoop 2 in terms of Big Data processing and scalability, there are several key differences that have significant impacts:

• YARN Architecture: One of the major changes introduced in Hadoop 2 is the introduction of YARN (Yet Another Resource Negotiator) architecture. This architecture separates the resource management and processing components in Hadoop, allowing for more flexible and efficient resource allocation.
• Improved Scalability: Hadoop 2 provides improved scalability compared to Hadoop 1. With YARN, multiple applications can now run simultaneously on a Hadoop cluster, enabling better resource utilization and overall scalability.
• Support for Various Workloads: Hadoop 2 is designed to support a wider range of workloads including batch processing, interactive queries, real-time processing, and more. This enhances the versatility of Hadoop clusters for handling different types of Big Data processing requirements.
• Enhanced High Availability: Hadoop 2 offers enhanced high availability features compared to Hadoop 1, ensuring better fault tolerance and reliability for Big Data processing tasks.
• Compatibility and Integration: Hadoop 2 is compatible with existing Hadoop 1 applications and frameworks, making it easier for organizations to migrate to the newer version without significant disruptions.

Overall, the key differences between Hadoop 1 and Hadoop 2, particularly the introduction of YARN architecture, improved scalability, support for various workloads, enhanced high availability, and compatibility, have a significant impact on Big Data processing and scalability capabilities.