Efficient data storage and management are the backbone of any Big Data and AI-driven system. Traditional relational databases like MySQL and PostgreSQL are designed for structured data with predefined schemas, making them less effective for handling large-scale, unstructured data that is common in Big Data environments. To address this limitation, organizations increasingly rely on NoSQL databases such as MongoDB, Cassandra, and HBase. These databases are optimized for horizontal scaling, allowing data to be distributed across multiple servers to ensure high availability and fault tolerance.

For distributed file storage, the Hadoop Distributed File System (HDFS) is a widely adopted solution. HDFS breaks large datasets into smaller blocks, which are stored across different machines in a cluster. This architecture provides redundancy through data replication, ensuring data durability even in the event of hardware failures.

Cloud-based storage solutions like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage offer scalable, cost-effective options for storing massive datasets. These platforms provide integrated security features, such as encryption and access control, and are designed to handle the dynamic storage needs of AI applications, where data can grow rapidly.

Data management also involves ensuring data quality, integrity, and accessibility. Tools like Apache Hive and Apache HBase provide data warehousing capabilities on top of distributed storage, enabling efficient querying and analytics. Metadata management tools, such as Apache Atlas, help track data lineage and governance, which are critical for compliance with data protection regulations.