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Acceldata Data Observability Cloud

From Blind Spots to Full Stack Visibility

Monitor every pipeline, enforce data quality at scale, and catch data issues before they reach production.

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE
Use Cases

What your data team can do now

Six use cases by enterprise teams running ADM in production today.

End-to-End Data Pipeline Observability

Monitoring 1,000+ pipelines across hybrid and multi-cloud environments — Databricks, SnapLogic, on-prem — is fragmented by design. Failures go undetected until a downstream dashboard breaks. ADOC stitches cross-pipeline health into a single view with execution status, schema drift, dependency mapping, and business-process-level observability.

MTTR drops from hours to minutes. 40–60% faster root cause resolution across platforms.

Data Quality Monitoring at Scale

Applying 25 DQ policies across 500–600 pipelines — one asset at a time — creates unsustainable manual overhead. ADOC enables bulk policy deployment across entire domains via UI or upload template, with rule-level execution detail, SLA tracking, and continuous trust scoring — no scripting, no IT dependency.

Policy setup 5× faster. 15–25% increase in governed data product adoption. 20–40% fewer data quality escalations.

Data Freshness and SLA Reliability

SLA breaches reach your users before your team knows they exist. ADOC alerts at configurable thresholds — before the breach — routing to PagerDuty or ServiceNow with lineage, score, and severity attached. One grouped incident per asset. No alert storms.

20–35% fewer SLA breaches. 30–50% faster MTTR. On-call gets root cause, not just a ping.

Cloud Migration Data Reliability

Migrations from Hadoop, Teradata, or Oracle to Snowflake and Databricks are blind lifts — schema mismatches, row-count drops, and KPI drift go undetected until they hit production reporting. ADOC helps certify data at source, reconciles at destination, and validates transformation integrity end-to-end, with audit-ready discrepancy reports at every stage.

50–70% faster reconciliation cycles. 20–40% reduction in mismatch-related revenue leakage post-migration.

Data Cost and Compute Optimization

Snowflake credits and Databricks DBUs spiral without workload-level visibility — inefficient joins, over-provisioned warehouses, and runaway queries drain budgets silently. ADOC tracks cost lineage at warehouse, query, user, and role level — with AI circuit breakers that stop cost spikes before they hit the bill.

10–25% reduction in cloud data spend. 2–3× query performance improvement.

AI and ML Data Pipeline Observability

AI models degrade silently when upstream pipeline changes shift inputs — stale features, missing records, schema drift — with no alerting until model performance drops. ADOC monitors freshness, completeness, and schema stability of all model inputs and gates pipelines on DQ thresholds before data reaches training or inference.

30–50% faster resolution of model data issues. Model validation cycles shortened by 30%.

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