“Finally, a book that shows the operational reality of data platforms, not just the sunny-day scenarios.”
S
Senior Data Engineer/Beta Reader
Early Access
Build Production-Grade Data & AI Platforms That Actually Work
Stop guessing. Get the battle-tested blueprints, runbooks, and decision frameworks that turn distributed data systems from risky experiments into reliable revenue engines.
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The Problem
Sound familiar?
Your data pipeline breaks every Monday morning
ML models stuck in notebooks, never reaching production
Cloud costs spiraling out of control ($50k to $200k/month)
3-day 'simple' schema changes that break everything
p99 latency at 800ms when you need <200ms
No one knows how to fix things when they break at 2 AM
You're not alone.
Most data/AI platforms fail not because of missing technology—but missing guardrails, runbooks, and proven patterns.
What You Get
12 Comprehensive Chapters
Covering every layer of modern data/AI platforms:
Foundational Principles
The 5 system qualities that matter (reliability, scalability, evolvability, cost-efficiency, compliance)
Real-Time Ingestion & CDC
Zero-loss pipelines with bounded lag, idempotency patterns, safe backfill strategies
Lakehouse Architecture
Delta/Iceberg/Hudi decision frameworks, Bronze/Silver/Gold patterns, compaction strategies
Orchestration That Doesn't Suck
Airflow vs Dagster vs Prefect comparison, MTTR optimization, dbt integration
Production MLOps
Feature stores, model registries, Shadow/A-B/Prod workflows, one-click rollback
Low-Latency Inference
Sub-200ms p99 patterns, caching strategies, graceful degradation, hedged requests
Observability & Reliability
Complete incident playbooks, drift detection, SLO engineering, on-call setup
Security & Compliance
PII handling, GDPR workflows, zero-trust IAM, DLP in CI/CD
4 Production Blueprints
Anti-fraud detection, self-service platforms, feature serving, batch-to-streaming migration
30-Day Implementation Plan
Week-by-week RACI, metrics gates, stakeholder templates, go/no-go criteria
Why This Book
Not another theory book
What you won't find
No vague 'best practices' without context
No toy examples that don't scale
No missing operational details
What you actually get
Real architectures from production systems processing billions of events
Actual code snippets and configuration examples
Complete runbooks for common failure scenarios
Decision frameworks for every major tech choice
Cost models showing real monthly spend breakdowns
95,000+ words of production-tested knowledge
50+ runbooks & checklists you can use immediately
Cost optimization frameworks (one team saved $48k/month)
Performance patterns (800ms to 185ms p99 case study)
Compliance workflows (GDPR, HIPAA, CCPA)
4 complete blueprints with architectures & configs
Who This Is For
Built for practitioners
Perfect if you are:
Data Engineer
building or scaling platforms
ML Engineer
trying to get models to production
Platform Engineer
responsible for reliability
Engineering Manager
making architectural decisions
Tech Lead
evaluating technology stacks
You'll learn to:
Design systems that balance speed, cost, and reliability
Choose the right tech stack (with actual decision criteria)
Build pipelines that don't lose data or create duplicates
Deploy ML models safely with instant rollback
Achieve <200ms p99 latency at scale
Detect issues before users notice them
Meet compliance requirements without killing velocity
Reduce costs by 30-60% through smart architecture
Beta Readers
What readers are saying
“The incident playbooks alone are worth 10x the price. We've used 3 of them already.”
P
Platform Team Lead/Beta Reader
“Chapter 8 on low-latency helped us reduce p99 from 600ms to 180ms in 2 weeks.”
M
ML Engineer/Beta Reader
Table of Contents
What's inside
01
Principles
The 5 system qualities, trade-off frameworks
02
Control Planes
Data contracts, schema evolution, metadata management
03
Workload Topologies
Batch vs streaming vs micro-batch patterns
04
Ingestion & CDC
Idempotency, backfills, bounded lag
05
Lakehouse
Delta/Iceberg/Hudi, medallion architecture
06
Orchestration
Airflow/Dagster/Prefect, MTTR optimization
FAQ
Frequently asked questions
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