Real-time Data Monitoring
Automated monitoring for data in motion (jobs, pipelines, stored procedures, workflows, DAGs, etc.) and data at rest (datasets) components across the data stack that sequentially make up a cross-platform data workflow to power data products and data reports.
Jobs that move, stitch, cleanse, transform, orchestrate, or refresh data can fail due to several reasons including schema drift, code issues, access issues, etc.
Jobs running longer than expected due to resource contention or sudden data volume spikes, introducing downstream delays across the data workflow.
Jobs may not start at their designated time due to runtime issues or orchestration failures.
Issues here indicate incomplete data, often presenting as missing rows, unexpected zero-record files, or significant deviations from historical volume baselines.
Data becomes stale due to refresh issues or delayed arrival times.
Data may be inaccurate at the field level due to manual input errors, corrupted records or transformation logic issues resulting in incorrect data values.
Unified, Cross-Platform Traceability
Auto-discovered unified view of the end-to-end data flow, spanning from data producers to data consumers. This view provides a live blueprint of upstream and downstream dependencies for both data in motion and data at rest.
to the source is essential for quickly identifying the root cause of a data incident.
is vital for performing impact analysis and holistically ensuring proper remediation across all consuming applications and reports.
To achieve this, our DOC integrates traditional Data Lineage (tracking data transformations) with Job or Process Lineage (tracking execution flow). This combination offers a unique graphical and actionable representation of the entire data workflow.
Automated Incident Management
Pantomath seamlessly integrates with existing IT service management workflows and provides bi-directional integrations with platforms like Service Now and Jira to ensure that data incidents are managed with the same rigor as IT/Security incidents.
Event Correlation & Automated RCA
Our Incident Intelligence features leverage proprietary AI Agents to correlate a multitude of events into a single, cohesive incident and immediately provide the diagnosis with dynamic decisioning through feedback loops and customized runbooks.
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Automated Root Cause Analysis (RCA)
Actionable Resolution Plans
AI-Powered Remediation & Self-Healing
Dynamic orchestration to execute automated, end-to-end self-healing, ensuring that the entire data workflow is restored correctly, not just the single failed component.
Leveraging the impact radius identified during RCA, the AI Agent dynamically implements circuit breakers.
This automatically pauses downstream jobs dependent on the faulty data, preventing the consumption of stale or corrupted data and protecting data trustworthiness across the organization.
Once the root cause is resolved (either automatically or via the AI-generated plan), the platform initiates dynamic orchestration.
It automatically sequences and re-runs only the impacted downstream data assets, ensuring an end-to-end refresh without wasted compute cycles, delivering fully synchronized and trustworthy data to consumers.
