AI scans every Fabric Data Pipeline for 8 reliability anti-patterns — missing error paths, zero retry policies, no timeouts, hardcoded values. A Dual Microsoft MVP delivers the scored report with prioritized fixes.
These issues don't crash your pipeline. They quietly corrupt your data, waste capacity, and serve stale dashboards for hours before anyone notices.
Pipeline stops silently on error. No alert fires. Downstream dashboards show stale data for hours before anyone notices.
Pipeline fails at 2 AM. The team finds out at 10 AM when the CEO asks why the dashboard is wrong. Eight hours of silence.
Bronze loads fine but writes zero rows. Silver transforms nothing. Gold serves empty dashboards. Everyone blames the data source.
A single transient 429 or timeout kills the entire pipeline. Adding 2 retries with 30s backoff catches 90% of these failures.
A Spark session hangs, the activity runs forever, consuming capacity units. Nobody knows until the bill arrives.
Works in dev, breaks in prod. Pipeline parameters and expressions prevent environment-specific failures and make testing possible.
Ten activities chained sequentially when five could run in parallel. Total pipeline duration doubles for no reason.
ForEach set to 50 concurrent items when the downstream source can only handle 5. Throttling cascades into timeout failures.
Every finding comes with a specific fix and priority level — not just a red flag.
Failure paths, try-catch patterns, error propagation across activity chains
Success rates, failure patterns, duration trends, SLA compliance from job history
Retry policies, timeouts, ForEach batch counts, parameterization vs hardcoding
Overlapping schedules, disabled triggers, gaps between pipeline runs, SLA windows
Quality gates between layers, dependency chain depth, notification coverage
15-minute call. We get read-only access to your workspace and understand which pipelines are business-critical. No write access needed.
AI reads every pipeline definition, analyzes activity configurations, pulls full job history via the Fabric REST API, and maps all schedules.
Scored report with prioritized fixes, monitoring query templates, and a 2-hour walkthrough call where we implement the highest-impact fixes together.
Everything needed to go from "we hope it works" to "we know it works."
Every pipeline scored 0–100 across 5 dimensions. Color-coded severity. Executive summary and per-pipeline breakdown.
Each finding ranked by impact and effort. Specific implementation steps — not vague recommendations. Copy-paste ready configurations.
Success rates, failure patterns, duration trends, and schedule adherence across the last 30 days of pipeline execution history.
KQL and REST API queries to track pipeline health going forward. Drop them into your existing monitoring stack.
Live session with a Dual Microsoft MVP. We review findings, implement the top fixes together, and answer architecture questions.
One follow-up call within 30 days to check progress, answer questions, and validate implemented fixes.
Secure checkout via Stripe. You'll receive an intake questionnaire after payment.
Spark Optimization ($3,500) + Pipeline Health Check ($4,000)
Audit your notebooks and the pipelines that run them.
View Data Engineering Pack →Every Pipeline Health Check engagement includes three professional deliverables — see a sample below.
Interactive scored report with findings, severity ratings, metrics, and recommendations. Dark-themed, print-ready.
PowerPoint summary for leadership — score, key findings, recommendations, and next steps. Ready to present.
Detailed written report with findings table, remediation steps, and priority recommendations. Shareable with stakeholders.
Sample uses anonymized data for demonstration purposes
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.
No. Read-only access is enough. We read pipeline definitions and job history via the Fabric REST API. We never modify your pipelines or any other artifacts in your workspace.
This audit is built for Fabric Data Pipelines. ADF shares many patterns, but the APIs and configurations differ. Reach out and we'll scope a custom ADF engagement.
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 to surface reliability degradation.
The report includes specific, copy-paste-ready fixes for every finding. During the 2-hour walkthrough, we implement the highest-impact fixes together — adding retry policies, failure paths, and notification activities live in your workspace.
Same day. The 15-minute connect call, AI scan, and report generation happen in the morning. The 2-hour walkthrough and fix session happens that afternoon. You walk away with everything by end of day.
Get a scored reliability audit from a Dual Microsoft MVP. Every anti-pattern found, every fix documented.
CLIENT RESULTS
Insurance
Zero undetected failures
12 pipelines had no error handling — failures went unnoticed for days. Added retry policies, timeout guards, and Teams alerts.
12 → 0 silent failures
Logistics
4-hour SLA consistently met
Nightly refresh pipelines frequently timed out due to hardcoded connection strings and missing parallelism. Restructured for concurrent execution.
92% → 99.8% on-time
Energy
Audit-ready documentation
No documentation on 23 production pipelines. Generated dependency maps, data lineage diagrams, and runbook for each.
23 pipelines documented
IS THIS RIGHT FOR YOU?
Data shows up late or not at all, and you only find out when someone complains about a report.
Enough orchestration complexity that you need structured error handling and monitoring.
Business users depend on fresh data at specific times — and pipeline timing is unpredictable.
You've wired it up, but want an expert review on retry logic, parameterization, and idempotency.
Each engagement is standalone — or bundle them for deeper savings.
$12,500
Full tenant health audit
$15,000
Medallion lakehouse architecture
$18,000
AI agent integration via MCP
$2,500
Power BI model review
$4,500
Spark anti-pattern detection
$8,000
Migration plan to Fabric
$12,000
Eventstreams & KQL setup