Build Production-Grade Data & AI Platforms That Actually Work

Designing Distributed Data & AI Systems - Book Cover

Stop guessing. Get the battle-tested blueprints, runbooks, and decision frameworks that turn distributed data systems from risky experiments into reliable revenue engines.

Sound familiar?

❌Your data pipeline breaks every Monday morning

❌ML models stuck in notebooks, never reaching production

❌Cloud costs spiraling out of control ($50k → $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'll 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

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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

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Production MLOps

Feature stores, model registries, Shadow→A/B→Prod workflows, one-click rollback

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Low-Latency Inference

Sub-200ms p99 patterns, caching strategies, graceful degradation, hedged requests

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Observability & Reliability

Complete incident playbooks, drift detection, SLO engineering, on-call setup

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Security & Compliance

PII handling, GDPR workflows, zero-trust IAM, DLP in CI/CD

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4 Production Blueprints

Anti-fraud detection, self-service platforms, feature serving, batch→streaming migration

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30-Day Implementation Plan

Week-by-week RACI, metrics gates, stakeholder templates, go/no-go criteria

Why This Book is Different

Not Another Theory Book

❌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 → 185ms p99 case study)

🔒

Compliance workflows (GDPR, HIPAA, CCPA)

🏗️

4 complete blueprints with architectures & configs

Who This Is For

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

What Readers Are Saying

Finally, a book that shows the *operational* reality of data platforms, not just the sunny-day scenarios.

Senior Data Engineer

Beta Reader

The incident playbooks alone are worth 10× the price. We've used 3 of them already.

Platform Team Lead

Beta Reader

Chapter 8 on low-latency helped us reduce p99 from 600ms to 180ms in 2 weeks.

ML Engineer

Beta Reader

What's Inside

Principles

The 5 system qualities, trade-off frameworks

Control Planes

Data contracts, schema evolution, metadata management

Workload Topologies

Batch vs streaming vs micro-batch patterns

Ingestion & CDC

Idempotency, backfills, bounded lag

Lakehouse

Delta/Iceberg/Hudi, medallion architecture

Orchestration

Airflow/Dagster/Prefect, MTTR optimization

Frequently Asked Questions

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