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Reliability & monitoring

Pipeline reliability and schema drift monitoring.

The worst data failure is not the pipeline that stops. It is the one that keeps running while quietly reporting the wrong number. We instrument your pipelines so problems surface as an alert to your team rather than a question from your client.

  • Freshness alerting
  • Schema drift detection
  • Reconciliation
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What you get

Hear it from an alert, not a client.

Stalled feeds seen at once

Freshness tracked per source and per account.

Light days held back

Volume checks flag a day arriving 60% light before it's reported.

Schema drift detected

New columns, type changes and unexpected enums, on every source.

Totals reconciled

Warehouse figures compared with platform UIs within tolerance.

The problem

Silent failure is the expensive kind.

A hard crash is easy: something pages, someone fixes it. The costly failures are quiet. A platform adds an enum value and your CASE statement buckets it as null. A rate limit turns a partial response into a real-looking but incomplete day. A currency field changes type and spend numbers land off by a factor of a hundred. Each of these produces a dashboard that renders perfectly and is completely wrong, and you find out when a client does.

What we instrument

What's included.

  • Freshness monitoring per source and per account, so a single stalled client feed is visible immediately
  • Volume and distribution anomaly checks that flag a day arriving 60% light before it is reported
  • Schema drift detection on every source: new columns, dropped fields, type changes and unexpected enums
  • Reconciliation jobs that compare warehouse totals back to platform UI figures within tolerance
  • dbt test coverage on the assumptions your models depend on, wired into the run
  • Alert routing into Slack or PagerDuty with severity, plus an on-call runbook per failure mode

How we work

How we run it.

  1. Step 1

    Map every existing pipeline and rank sources by how much reporting depends on them

  2. Step 2

    Instrument freshness and volume first, since those catch the majority of real incidents

  3. Step 3

    Add schema contracts and reconciliation for the highest-stakes sources

  4. Step 4

    Tune thresholds against real history so the alerts stay credible, then hand over runbooks

What we use

Tools, platforms and methods.

  • dbt tests
  • Great Expectations
  • Airflow sensors
  • Elementary
  • Slack / PagerDuty
  • Snowflake
  • BigQuery

Related work

Projects we've delivered.

Tinuiti case study cover
120+ automations live

Tinuiti

We rebuilt the ETL, warehouse and automation layer across Amazon, Walmart, TikTok, Instacart and Criteo.

Read the case study
BTR Media case study cover
50+ campaigns, one dashboard

BTR Media

We built ETL pipelines into one data model and one dashboard for Amazon, Walmart and Instacart campaigns.

Read the case study
SellerFuse case study cover
One pipeline, every client account

SellerFuse

We built the SP-API and Ads API pipelines, live dashboards and automation behind SellerFuse.

Read the case study

FAQ

Common questions.

Can you add monitoring to pipelines someone else built?

Yes, and it is a common first engagement. Observability sits alongside your existing pipelines rather than replacing them, so it does not require a rewrite. It also tends to be the fastest way to find out what is actually wrong before deciding what to rebuild.

How do you keep alerts from becoming noise people ignore?

Thresholds are set from your real historical variance rather than round numbers, seasonality is accounted for, and alerts carry severity so a late non-critical source does not page anyone at 3am. An alert channel people mute is worse than no alerting, so we tune it against replayed history before going live.

What is schema drift, and why does it matter so much here?

Schema drift is an upstream source changing shape without warning: a renamed field, a new type, an added enum value. Ad platforms ship these changes continuously. Because the pipeline keeps running, the damage is silent and can span weeks of reporting before anyone notices.

Do you offer ongoing monitoring as a managed service?

Yes. Some clients take the tooling and runbooks and run it themselves; others keep us on for on-call coverage and fixes. Both are available, and the tooling is identical either way, so you are never locked in.

Go deeper

Guides from our engineers.

Guide

How to Scale an Amazon Agency: Data Infrastructure Lessons from 35 to 3,500 Clients

Scaling an Amazon agency from dozens to thousands of clients breaks manual reporting completely. Here's the infrastructure that makes that scale possible.

Read the guide

Guide

ETL Pipelines for Amazon Sellers: How to Unify SP-API, Ads API, and Inventory Data

Amazon sellers juggle data across SP-API, the Ads API, and spreadsheets. Here's how a proper ETL pipeline unifies it into one reliable source of truth.

Read the guide

Related

Related services.

ETL & ELT pipelines

Incremental, orchestrated, tested pipelines that re-run without a babysitter.

Learn more

Pacing & anomaly detection

Pacing projected to month end and anomaly alerts routed to the account owner.

Learn more

Data maturity audit

A written assessment of your data stack and a prioritized roadmap, in 2 to 3 weeks.

Learn more

Find out what is failing quietly.

We will review your current pipelines and report where a silent failure could reach a client dashboard today.

Talk to an engineer