eCommerce Data Pipeline Observability

Pipeline reliability

98.8%

▲ 249 of 252 runs completed

On-time delivery (SLA)

97.2%

▲ runs delivered within freshness SLA

Rows processed (14d)

5.4M

▲ across 6 pipelines

Mean time to recovery

108 min

▼ 3 failed runs auto-flagged & fixed

Pipelines monitored

■ Shopify · Amazon · Stripe · GA4 · Klaviyo · Meta

How the data flows

Ingest

Python + Airbyte

Pull each source via REST / GraphQL / webhooks into S3 raw — incremental & idempotent.

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Orchestrate

Airflow (MWAA) / Dagster

Schedules, dependencies, retries and backfills with per-pipeline SLAs.

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Transform

dbt

Version-controlled models + data tests; raw → staged → marts.

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Monitor & Alert

Great Expectations

Schema-drift, null-spike, volume and freshness checks → Slack / PagerDuty.

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Deliver

Tableau / Looker / Power BI

Reliable, on-time, performance-tuned datasets the whole team can trust.

Daily Pipeline Runs by Outcome

Every pipeline run over the last 14 days, classified by outcome — the at-a-glance health view.

Data Freshness — Avg Minutes Behind SLA

Lower is better. The spikes line up exactly with the incidents in the log below.

Rows Ingested by Source (14d)

Volume by connector — GA4 event data dominates, as it should for eCommerce.

Incidents by Root Cause

What actually broke — grouped so the fix targets the root cause, not the symptom.

Incident Log — auto-detected, triaged & resolved

Date	Pipeline	Source	Root cause	Status	Recovery
6/12	ga4_events	GA4	Source API timeout (GA4 quota)	LATE	auto-recovered
6/14	shopify_orders	Shopify	Schema drift (new variant field)	FAILED	95 min
6/16	klaviyo_events	Klaviyo	Late source (Klaviyo export delay)	LATE	auto-recovered
6/18	amazon_settlements	Amazon	Duplicate keys (settlement re-post)	FAILED	160 min
6/19	stripe_payments	Stripe	Source API timeout (Stripe rate limit)	LATE	auto-recovered
6/21	ga4_events	GA4	Null spike (consent-mode nulls)	FAILED	70 min
6/22	shopify_orders	Shopify	Late source (webhook backlog)	LATE	auto-recovered

Each incident is caught by monitoring, root-caused, and fixed durably — not patched over.

Raw source data

View CSV Download CSV

pipeline_runs.csv — showing 12 of 252 rows. The dashboard above is computed from this file, untouched.

date	pipeline	source	rows_ingested	status	duration_min	freshness_min	sla_min
2026-06-10	shopify_orders	Shopify	3333	success	2.7	7	45
2026-06-10	shopify_orders	Shopify	2934	success	7.7	17	45
2026-06-10	shopify_orders	Shopify	3576	success	3.1	10	45
2026-06-10	shopify_orders	Shopify	2748	success	4.0	13	45
2026-06-10	stripe_payments	Stripe	3260	success	3.9	16	45
2026-06-10	stripe_payments	Stripe	3851	success	4.0	15	45
2026-06-10	stripe_payments	Stripe	4152	success	2.5	20	45
2026-06-10	stripe_payments	Stripe	4025	success	4.9	4	45
2026-06-10	ga4_events	GA4	59132	success	4.9	3	60
2026-06-10	ga4_events	GA4	45708	success	8.4	20	60
2026-06-10	ga4_events	GA4	56791	success	7.6	18	60
2026-06-10	ga4_events	GA4	59380	success	5.1	18	60

How this works in production

How the production version works — built on your preferred AWS stack:

Ingestion

Python + Airbyte on ECS/Fargate

REST / GraphQL / webhooks + light scraping for sources without an API.

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Orchestration

Airflow (MWAA) / Dagster

Schedules, retries, SLAs and alerting on miss.

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Lakehouse

S3 + Amazon Redshift (Databricks/Spark for heavy jobs)

Schema design & storage patterns tuned per workflow for scale and cost.

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Transform

dbt

Modular, tested, documented models with full lineage.

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Observability

Great Expectations + freshness/volume monitors

Root-cause fast; durable fixes, not temporary patches.

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Delivery & IaC

Tableau/Looker + Terraform + Docker

Performance-tuned BI datasets; reproducible, reviewable infrastructure.

This demo runs the real aggregation logic in-process on synthetic data — not a live production system.

Want this on your real Shopify / Amazon / Stripe data?

I build reliable, monitored eCommerce pipelines exactly like this on AWS — with alerting and root-cause analysis baked in from day one, so the data is accurate and on time without you having to chase it.

bing@bkydataconsulting.com