Jeff Needham
Scaling
AI and ML algorithms and indexes often require massive datasets for training. RDBMs often struggle to scale horizontally to accommodate the storage and processing demand for large datasets, especially in real-time or near real-time scenarios.
Complex Data Joins
AI and ML applications require complex data transformations and feature engineering. Legacy RDBMS technologies will likely struggle with the performance of joins and aggregations on large datasets, impacting the speed and responsiveness of querying.
Latency
Rigid RDBMS schemas introduce latency in data access and retrieval due to disk-based storage and transactional processes. These limitations often hinder application performance, resulting in substandard user experiences.
Data Diversity
RDBMS technologies are poorly provisioned to handle the wide variety of data inputs needed for AI and ML algorithms, specifically streaming data from IoT devices, and unstructured or semi-structured data collections.
Real-Time Processing
The vast majority of AI applications require real-time processing of data streams. RDBMS technologies are incapable of providing the necessary real-time uptime, availability, and responsiveness to support these use cases.
Limited Algorithm Support
RDBMS technologies lack native support for many AI and ML technologies, introducing unnecessary complexity when managing application infrastructure to perform complex AI and ML computations.
MongoDB Excels at modernization, cloud migration, and forms the data foundation to scale AI.
