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How a Real Estate Platform Stabilized 200+ Data Pipelines.

A pipeline reliability playbook for Data Engineering Leads drowning in 3am alerts.

In depth

You have 200+ pipelines.

01

Pipeline sprawl is what success looks like in data engineering.

02

The first symptom is the on-call calendar.

03

The second symptom is data trust.

The detail

The 16-week program that gets you there.

Zone · 01

Weeks 1–3 - Standardized framework

Pick one orchestrator. Pick one templating system.

Zone · 02

Weeks 4–7 - Observability that engineers actually use

End-to-end traces, not point monitoring. Every pipeline has a trace from source to sink.

Zone · 03

Weeks 8–10 - Data contracts at the boundaries

Not every join needs a data contract. The boundaries between teams do.

By the numbers

The figures that make it a board-level conversation.

85%
Pages per month - reduction
14 min
On-call MTTR
5 weeks
Avg time-to-onboard new engineer
Inside the report

What you'll take away.

01

Standardized framework

Pick one orchestrator.

02

Observability that engineers actually use

End-to-end traces, not point monitoring.

03

Data contracts at the boundaries

Not every join needs a data contract.

Questions

Frequently asked.

Do we have to migrate every pipeline?
Can we do this with our existing team?
What about pipelines that pre-date the team?
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Next step

On-call becomes manageable and the team gets its weekends back.

Talk through how this applies to your roadmap with our engineering leads - a working session, not a sales pitch.

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