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Cut Alert Noise. Resolve Data Incidents Faster.

Acceldata's intelligent alerts and notifications system surfaces only what demands attention, delivering full context at the point of alert, and cutting the time from detection to resolution.

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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

One media streaming provider replaced 10 separate alert pings per failing pipeline with a single grouped alert.
Engineers opened one notification and understood the full scope immediately.
They started fixing immediately — instead of triaging ten near-identical tickets.

Fewer alerts. More signal. Faster decisions.

Stop Treating Every Alert the Same

Most monitoring tools fire an alert for every anomaly, every policy breach, every threshold crossed. The result: alert fatigue so severe that engineers start ignoring the inbox entirely.

Acceldata groups related alerts by asset, pipeline, or schema — collapsing redundant noise into single, actionable signals.

Understand Any Alert in Seconds — Without Tab-Switching

Once an alert fires, the real time sink begins: opening five tools, hunting for lineage documentation, reconstructing context manually.

Real outcome: A retail chain identified the exact table and transform causing a data freshness delay — inside the alert view, without opening a single additional tool. Time from alert to root cause: minutes, not hours.

Route the Right Alerts to the Right People

Acceldata's configurable subscription reports let you build routing logic that matches how your team actually operates

Alert Type

Delivery

Recipient

HIGH

Pipeline failure

Immediate

PagerDuty + Slack

On-call engineer

Team lead CC'd

MEDIUM

Pipeline failure

Within 1 Hour

Email + Slack

Owning team

Domain DL

LOW

Pipeline failure

Daily Digest

Email summary

All subscribers

Batched 6am

See the Blast Radius Before You Start Remediating

One broken pipeline rarely breaks just one thing. Acceldata's impact analysis maps the direct blast radius of every incident the moment it triggers.

Reduce MTTR — Not by Working Faster, But by Guessing Less

Engineers spend the majority of incident time reconstructing context — what failed, why it failed, what else it touched, and what to do next. Acceldata compresses that phase to near-zero.

Correlated Alerts

Correlated alerts show related failures across assets so you see patterns, not isolated events

Root-Cause Context

Root-cause context surfaces the originating failure in the pipeline chain

Recommended actions

Recommended actions give engineers a starting point, not a blank page

The outcome is measurable: reduction in alerts summarized per tenant and decrease in MTTR are the primary success metrics tracked across Acceldata's subscription reports rollout.

Built for Teams Running Complex Data Pipelines at Scale

If your team is:

Managing pipelines across multiple clouds, warehouses, or orchestration tools

Struggling to prioritize incidents without knowing their downstream impact

Spending more time reconstructing context than actually fixing problems

Getting escalations from business users before internal monitoring catches the issue

Ready to get started

Explore all the ways to experience Acceldata for yourself.

Expert-led Demos

Get a technical demo with live Q&A from a skilled professional.

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30-Day Free Trial

Experience the power of Data Observability firsthand.

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Let our experts help you achieve your data observability goals.

Detect Data Anomalies Before They Breach Your SLOs

Most systems alert you after something breaks. Acceldata surfaces deviations as they emerge — across metrics, pipelines, and raw data — so your team can act before impact.

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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Rule-based tools were built for infrastructure. Data breaks differently.

Unknown-Unknowns Hit Business Decisions

Subtle multi-column anomalies — a changed logging format, a misreporting inventory system, a stale data feed — slip past every univariate check and silently corrupt the downstream decisions that depend on them.

Context-Blind Thresholds

A 12% volume drop Tuesday after a holiday is normal. The same drop Wednesday mid-quarter close is a P1. Rules don't know the difference.

Query Any Source. Govern at the Query Layer. Own Your Compute.

XDP's federated query engine runs on Trino — extended with native governance, unified catalog integration, and customer-controlled Kubernetes execution.

No proprietary formats.

No forced cloud dependency.

No bolt-on governance tools.

One SQL statement.

Multiple heterogeneous catalogs.

Zero data movement required.

Zero Configuration

Three Steps to Detection. No Manual Threshold Tuning.

Most ML-based monitoring tools shift the burden from writing alert rules to writing training pipelines. Acceldata eliminates both.

Select Tables or Pipeline Metrics

Point Acceldata at the tables or pipeline metrics you want to monitor. No agent installation, no SDK changes.

Choose Your Detection Model

Time-series, multivariate, or both. Select the detection mode that matches your pipeline's risk profile.

Set Sensitivity Level

One slider. Low, medium, or high sensitivity. Acceldata handles the rest — including recalibration after schema changes.

Two Detection Modes. Full-Spectrum Coverage

Two complementary detection modes — covering the shape of your pipeline metrics over time and the multivariate relationships inside your data.

Metric-based
Anomaly Detection

Monitor how your data behaves over time

Detect changes in freshness, volume, and data distribution
Identify shifts in data "shape" across columns and datasets
Surface anomalies in metrics like distinct values, averages, and variance

EXAMPLE

Missing regions, delayed pipelines, or unexpected spikes — flagged instantly.

Multivariate
Anomaly Detection

Find hidden issues inside your data

Analyze full datasets across multiple correlated fields
Detect anomalous rows and segments — even when rules pass
Reveal patterns humans didn't anticipate

Customer Example

Global Retailer

Uses multivariate anomaly detection on shipping data. When the system flags an anomalous pattern, a notification reaches the business user directly — who decides in context whether it's normal variation or something that needs a new policy. No engineer in the loop for triage.

What This Means For Your Team

For Data Engineers

Stop chasing false positives — focus on real anomalies with context.

For SREs

Detect early signals before incidents escalate.

For Data Leaders

Reduce MTTD and MTTR while improving data reliability across the organization.