Which database is best for data analytics of large datasets, requiring structured and semi-structured storage?

HBase is designed specifically for handling large datasets, especially in scenarios where data is structured and semi-structured. It is a NoSQL database that operates on top of the Hadoop Distributed File System (HDFS) and provides high scalability and the ability to store and manage very large amounts of data across clusters of computers.

The column-family-oriented storage model of HBase allows for efficient storage and retrieval of data for analytics, accommodating the needs of both structured and semi-structured data types. Additionally, it excels in environments where real-time read/write access is crucial, making it suitable for analytics on large-scale applications.

While MySQL and PostgreSQL are both powerful relational databases suitable for structured data, they may not perform as well with vast amounts of data typical in analytics scenarios that require horizontal scaling. Cassandra, another NoSQL database, is also designed for scalability and can handle large volumes of data, but it is mainly focused on high availability and quick write operations rather than the complex querying and analytics that HBase seamlessly supports when integrated with data processing frameworks like Hadoop. Thus, HBase stands out as the optimal choice for data analytics involving large datasets needing both structured and semi-structured storage.