Autonomous DBA
Intelligence that learns from your environment
dbsnoop’s Autonomous DBA goes far beyond a simple alert engine or generic suggestions. It is an artificial intelligence specialized in databases, trained on hundreds of real environments, capable of understanding, learning, and acting based on the unique behavior of your operation.
How does it work?
Through comprehensive telemetry collection of the database’s operation, our AI continuously analyzes usage, health, load, relationships, and query indicators.
This includes:
Database metadata
Index structure and relationships between tables
Size, distribution, and fragmentation of data
Executed queries, response time, and impact
All of this feeds machine learning models, such as:
Unsupervised clustering (K-Means, DBSCAN) to identify usage patterns, hotspots, and anomalous behaviors;
Regression and time series models (ARIMA, Prophet, LSTM) to predict degradations and load impact;
Recommendation systems to suggest personalized improvements based on your environment’s own patterns and history.
CONTINUOUS AND LOCAL LEARNING
Our model combines global learning (based on hundreds of analyzed servers) with local learning (from your own databases). This ensures that every recommendation—whether for indexing, partitioning, configuration, or usage practices – has context, makes technical sense, and respects the particularities of your environment.
Real understanding of database usage
dbsnoop’s Autonomous DBA understands user behavior regarding the database:
It creates heatmaps of the load: when, how, and by whom the database is most used.
It detects seasonal and atypical loads, generating predictive alerts.
It identifies problematic queries before they become bottlenecks and recommends corrective actions based on their real impact.