H2 Database vs Hadoop: What are the differences?

## Key Differences Between H2 Database and Hadoop

1. **Storage Model**: H2 Database is an embeddable database engine that usually operates in memory or stored as a file, while Hadoop is a distributed storage and processing framework that stores large volumes of data across a cluster of machines. 
2. **Use Cases**: H2 Database is commonly used for small to medium-sized applications that require a lightweight and fast database solution, whereas Hadoop is designed for handling large-scale data processing tasks such as big data analytics, data warehousing, and machine learning.
3. **Data Processing Paradigm**: H2 Database follows a traditional row-based data storage and processing model, while Hadoop utilizes a distributed processing model called MapReduce to process and analyze data in parallel across a cluster of nodes.
4. **Scalability**: H2 Database is more suitable for single-node or small-scale deployments due to its limitations in scalability and handling large datasets, whereas Hadoop is highly scalable and can efficiently manage petabytes of data across thousands of machines in a cluster.
5. **Fault Tolerance**: H2 Database does not inherently provide fault tolerance mechanisms for data storage and processing, while Hadoop ensures fault tolerance by replicating data across multiple nodes in the cluster to prevent data loss in case of node failures.
6. **Ecosystem**: H2 Database has a smaller ecosystem and toolset compared to Hadoop, which has a rich ecosystem with various modules like HDFS, MapReduce, YARN, and Hive for different data processing and analysis tasks.

In Summary, the key differences between H2 Database and Hadoop lie in their storage models, use cases, data processing paradigms, scalability, fault tolerance mechanisms, and ecosystem.```