Everything you need to build, govern, and scale data and AI workloads—one unified platform.
Monitor, detect, and resolve data and AI issues with end-to-end observability across pipelines.
Build, orchestrate, and run data pipelines with intelligent agents that automate the entire engineering workflow.
Query your lakehouse in-place with Velox-accelerated performance. 10x faster than traditional warehouses.
Distributed training, high-throughput inference, and GPU notebooks—everything you need for production AI.
Build, deploy, and manage intelligent agents to automate and optimize data operations.
An open-source data platform for Hadoop modernization, flexibility, and long-term control.
Browse solutions to help you solve the complex business challenges unique to your industry.
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Browse materials to help you access the tools, guides, and insights essential to your workflows.
Learn about our mission, leadership, and vision driving modern data operations forward.
Browse solutions to help you solve the complex business challenges unique to your industry.
Browse materials to help you access the tools, guides, and insights essential to your workflows.
Learn about our mission, leadership, and vision driving modern data operations forward.
Everything you need to build, govern, and scale data and AI workloads—one unified platform.
Monitor, detect, and resolve data and AI issues with end-to-end observability across pipelines.
Build, deploy, and manage intelligent agents to automate and optimize data operations.
Build, orchestrate, and run data pipelines with intelligent agents that automate the entire engineering workflow.
Query your lakehouse in-place with Velox-accelerated performance. 10x faster than traditional warehouses.
Distributed training, high-throughput inference, and GPU notebooks—everything you need for production AI.
An open-source data platform for Hadoop modernization, flexibility, and long-term control.
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Monitor, detect, and resolve data issues with end-to-end observability across pipelines.
Monitor, detect, and resolve data issues with end-to-end observability across pipelines.
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AI-powered observability and optimization for Hadoop and big data environments.
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An open-source data platform for Hadoop modernization, flexibility, and long-term control.
Incidents resolved in seconds. Not sprints.
Data quality incidents average 53 days to resolve. ADM's Incidents Agent detects, traces root cause, and routes a fix — before the next pipeline run.
Root cause analysis
Mean time to resolve
Faster cross-system resolution
A pipeline breaks.
Your team opens six tabs to find out why.
You have monitors, dashboards, and runbooks. But when something breaks, root cause still lives across three tools — and resolution depends on whoever's on-call.
Alert Fatigue
Every downstream asset fires its own alert. Engineers triage noise instead of fixing the root issue.
No Cross-System View
DQ scores, pipeline logs, and lineage live in separate dashboards. Correlation is manual, every time.
Longer Resolution Cycles
Invalid records affecting 700+ customers sit unresolved for weeks — slow triage, no structured ownership.
Incidents Outpace Engineers
Policy failures post-deployment have no alerting. Issues compounds before anyone knows they exist.
Anomaly to resolution. Guided every step.
Structured agent-driven response — with human approval before any fix is applied.
Ask the Incident Management Agent Anything
Enterprise Incident Management on Autopilot
Autonomous incident detection, cross-system correlation, and guided resolution in one intelligent loop.
Incident Management Agent in Action
Powered by the xLake Reasoning Engine, the Incidents Management Agent operates as part of a collaborative, agentic framework
Got Questions? Get Clarity
Traditional tools generate alerts. ADM clusters alerts into incidents, identifies root cause, and routes resolution with full context across systems.
Yes. All remediation actions are human-in-the-loop (HITL). The agent proposes fixes, but execution requires approval with a complete audit trail.
It detects data quality issues, pipeline failures, schema drift, freshness delays, volume anomalies, and cross-system inconsistencies.
The agent correlates pipeline logs, data quality signals, lineage, and deployment events to pinpoint root cause in seconds.
Yes. Incidents are automatically routed to tools like PagerDuty, ServiceNow, Jira, and Slack with full context pre-filled.
Yes. Through MCP-DC, the agent correlates signals across data pipelines, observability tools, and CI/CD systems to identify true root cause.
It recommends targeted remediation steps and can trigger workflows, but all fixes require human approval before execution.
The agent groups related alerts into a single incident, eliminating redundant notifications and focusing teams on the root issue.
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