Data Engineering & Platform

Practical architecture, patterns, and operational practices for reliable data pipelines, catalogs, and platform services.

Playbook

Data Platform Patterns Playbook

Practical architecture patterns, decision criteria, and operational responsibilities for choosing, building, and running reliable data platforms (lakehouse, event-driven, hybrid). Includes pattern overviews, trade-offs, governance implications, ingestion and storage guidance, a streaming-vs-batch decision flow, recommended SLAs and monitoring scope, cost-control heuristics, and a starting checklist for platform launches.

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Runbook

Data Pipeline Operational Runbook Template

A practical, ready-to-adapt runbook template for ETL/ELT and streaming pipelines: owners, SLAs/SLOs, daily health checks, monitoring, alert classification, a step-by-step triage playbook (with command templates), rollback and remediation procedures, escalation matrix, communication templates, post-incident review prompts, and an onboarding checklist. Includes example entries for batch jobs and streaming connectors.

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Dashboard Template

Data Platform Health Dashboard Template

A practical, implementable dashboard layout and widget catalogue to monitor ingestion success, pipeline latency, data freshness, schema changes, contract violations, consumer SLAs, and suggested alerting and ownership so teams detect problems early and route ownership to reduce downstream analytic failures.

Dashboard Template
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Playbook

Data Contract & SLA Template (Editable)

A practical, editable playbook and template to define producer–consumer expectations for data integrations: schema guarantees, freshness and availability SLOs, ownership, change processes, validation tests, monitoring, onboarding steps, and a sample signed agreement. Includes clear examples, recommended metrics, common pitfalls, and ways to turn the template into an interactive, versioned contract registry.

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