- Collect metrics:
- Collect usage metrics — e.g. business intelligence outputs (dashboards, reports), including peak access times.
- Collect runtime metrics from data processing engines, including when and for how long data processing occurs.
- Recommend when to scale up (or down) the data processing resources.
- Auto-scale: Automatically apply the recommended parameters to the data processing environment.
For example, imagine that 90% of users log in to the business intelligence tool during the last week of a financial quarter. With active metadata, the system can automatically scale up resources just before those weeks and automatically scale down the resources again afterwards.
Improve resource utilization and reduce processing delays.
Purge stale or unused assets
Maintain a clean data landscape.
Clean up data landscape by removing duplicate assets
Prevent duplicate assets to reduce costs.
Dynamic data pipeline optimization
Reduce unnecessary data processing and improve resource utilization.