NEW — Find the Silent Failures

Your pipelines are failing and you don't know it

AI scans every pipeline for missing error handling, broken retry policies, scheduling gaps, and reliability blind spots. An MVP walks you through the fixes.

Get Started — $4,000
8
Anti-Patterns Detected
5
Score Dimensions
100%
Job History Analyzed
½ day
Delivery Time

What we audit

Five dimensions scored 0-100. Every finding comes with a specific fix — not just a red flag.

🚨

Error Handling

Does every critical activity have a failure path? Are there notification activities for pipeline failures? We check every dependency chain.

📈

Reliability

Job history analysis: success rates, failure patterns, duration trends, SLA compliance. We spot degradation before it becomes an outage.

⚙️

Configuration

Retry policies, timeouts, ForEach batch counts, parameterization. Every activity checked against Microsoft best practices.

📅

Scheduling

Overlapping schedules, disabled schedules, gaps between pipeline runs. We map your entire orchestration timeline.

🏗️

Architecture

Quality gates between layers, pipeline parameterization, dependency chain depth, notification coverage.

The silent killers in your pipelines

These are the issues that don't crash your pipeline — they just quietly corrupt your data or waste your capacity.

No failure path on critical activities

Pipeline stops silently on error. No alert fires. Downstream dashboards show stale data for hours before anyone notices.

Zero retry policy

A single transient 429 or timeout kills the entire pipeline. Adding 2 retries with 30s backoff catches 90% of these.

No timeout on notebook activities

A Spark session hangs, the activity runs forever, consuming capacity. Nobody knows until the bill arrives.

No quality gate between layers

Bronze loads fine but writes zero rows. Silver transforms nothing. Gold serves empty dashboards. Everyone blames the data source.

No notification on failure

Pipeline fails at 2am. The team finds out at 10am when the CEO asks why the dashboard is wrong.

Hardcoded paths and dates

Works in dev, breaks in prod. Pipeline parameters and expressions prevent environment-specific failures.

How it works

Three steps. Half a day. Clear action plan.

Connect

15-min call. We get read access to your workspace and understand which pipelines are business-critical.

Scan

AI reads every pipeline definition, analyzes activity configurations, pulls full job history, and maps schedules.

Deliver

Scored report with prioritized fixes, monitoring query templates, and a 2-hour walkthrough call with an MVP.

Simple pricing

Every pipeline in your workspace, audited. One flat fee.

Data Pipeline Health Check

$4,000 one-time
  • All pipelines in workspace scanned
  • Activity-level configuration audit
  • Error handling & failure path analysis
  • Full job history reliability analysis
  • Schedule conflict detection
  • Quality gate assessment
  • Scored report (0-100 across 5 dimensions)
  • Monitoring query templates
  • Prioritized fix list with implementation steps
  • 2-hour walkthrough with MVP
  • 30-day support (1 follow-up call)

Bundle & Save

Spark Optimization ($3,500) + Pipeline Health Check ($4,000)

$6,500 save $1,000

Audit your notebooks and the pipelines that run them.

Common questions

How is this different from the Spark Optimization Audit?
The Spark audit focuses on notebook code and Spark configs — what runs inside the compute engine. This pipeline audit focuses on orchestration — how activities are chained, how errors propagate, how schedules align. Different layers, both critical.
Do you need write access?
No — read-only access is enough. We read pipeline definitions and job history via the Fabric REST API. We never modify your pipelines.
What if my pipelines use ADF instead of Fabric?
This audit is for Fabric Data Pipelines. If you're running Azure Data Factory, the patterns overlap significantly — reach out and we'll scope a custom engagement.
What job history do you analyze?
We pull all available job execution history from the Fabric API — typically the last 30 days. We analyze success rates, duration trends, failure patterns, and schedule adherence.
Will you fix the issues or just report them?
The report includes specific fixes for every finding. During the 2-hour walkthrough, we implement the highest-impact fixes together — adding retry policies, failure paths, and notification activities.