AI Oracle Performance Monitor
Budget: ₹12,500 – ₹37,500 INR
I need an AI-driven solution that plugs into my Oracle instance and pinpoints query-level problems in real time while still giving me the option to run scheduled, deeper analyses. The focus is squarely on query performance: I want to see why certain SQL statements are slow, which ones are consuming the most CPU or I/O, and where locking or blocking is stalling throughput.
Although memory usage or broader database-wide operations could be future add-ons, right now the win for me is a tool that can learn from AWR/ASH data, parse execution plans, correlate wait events, and surface clear, actionable recommendations—think automatic index advice, rewritten SQL hints, or resource limit alerts. Live dashboards or push notifications are essential for the real-time side, while PDF or HTML reports work fine for the scheduled runs.
Deliverables
• Installable or containerised tool that connects securely to Oracle and begins collecting telemetry.
• Machine-learning logic that detects slow query execution, high resource consumption, and locking scenarios with minimal false positives.
• Real-time visual dashboard plus a scheduler for periodic reports.
• Setup guide and usage documentation clear enough for a DBA to maintain going forward.
The stack is flexible—Python, Java, or even a low-code AI platform is fine as long as it interacts cleanly with Oracle and can be deployed on-prem. Let me know which libraries (e.g., Scikit-learn, TensorFlow, or Oracle Machine Learning) you plan to use and how you will keep overhead on the database to a minimum.
Although memory usage or broader database-wide operations could be future add-ons, right now the win for me is a tool that can learn from AWR/ASH data, parse execution plans, correlate wait events, and surface clear, actionable recommendations—think automatic index advice, rewritten SQL hints, or resource limit alerts. Live dashboards or push notifications are essential for the real-time side, while PDF or HTML reports work fine for the scheduled runs.
Deliverables
• Installable or containerised tool that connects securely to Oracle and begins collecting telemetry.
• Machine-learning logic that detects slow query execution, high resource consumption, and locking scenarios with minimal false positives.
• Real-time visual dashboard plus a scheduler for periodic reports.
• Setup guide and usage documentation clear enough for a DBA to maintain going forward.
The stack is flexible—Python, Java, or even a low-code AI platform is fine as long as it interacts cleanly with Oracle and can be deployed on-prem. Let me know which libraries (e.g., Scikit-learn, TensorFlow, or Oracle Machine Learning) you plan to use and how you will keep overhead on the database to a minimum.