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My learningData Engineering With AILesson 09
Vector Indexes for Data Engineers — HNSW vs IVF vs Flat Without the Math Degree

Lesson 09

Vector Indexes for Data Engineers — HNSW vs IVF vs Flat Without the Math Degree

Vector indexes power fast similarity search in AI and RAG systems, but choosing the right one can be confusing. HNSW delivers high-speed, accurate retrieval, IVF balances scale and performance, and Flat search offers perfect accuracy with higher compute cost. Understanding these trade-offs helps data engineers build faster and smarter AI pipelines.

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Curriculum

25 lessons · 2h 0m

0/25 lessons done2h 0m left
  1. 04:58

    AI Is Transforming Data Engineer Roles: What’s Changing

    04:58

  2. 04:19

    The Data Engineer Role Just Split Into 4 Jobs — Which One Are You?

    04:19

  3. 04:34

    Writing SQL Is No Longer the Hardest Part of Being a Senior Data Engineer — Here’s What Is

    04:34

  4. 04:40

    Chunking Is the New Partitioning — The Data Engineering Decision That Makes or Breaks RAG

    04:40

  5. 04:26

    Fixed vs Recursive vs Semantic Chunking — Choosing the Right Strategy for Your AI Pipeline

    04:26

  6. 06:03

    Embedding Pipelines Explained — How Data Engineers Choose & Version Embedding Models

    06:03

  7. 07:01

    Your embedding model just got upgraded — how to re-embed billions of rows without downtime

    07:01

  8. 04:27

    CDC for Unstructured Data — The Ingestion Pattern Most Data Pipelines Miss

    04:27

  9. 08:17

    Vector Indexes for Data Engineers — HNSW vs IVF vs Flat Without the Math Degree

    08:17

  10. 09:51

    Pure Vector Search Is Dead — Why Hybrid Retrieval Is Now the Production Standard

    09:51

  11. 05:22

    Rerankers — The Low-Cost Pipeline Upgrade That Beats Bigger Embedding Models

    05:22

  12. 03:56

    Query Transformation as a Pipeline Stage — Rewriting Vague Questions Before Retrieval

    03:56

  13. 02:48

    Your RAG Gave a Wrong Answer — The Data Engineer’s Failure Tree for Debugging It

    02:48

  14. 05:18

    PDFs Are the New CSVs — Building Parsing Pipelines That Scale to Millions

    05:18

  15. 03:47

    The Duplicate Documents Secretly Killing Your Data Quality — MinHash, SimHash & Embedding Dedup Explained

    03:47

  16. 04:33

    The 5 AI Agents Every Self-Healing Data Pipeline Needs

    04:33

  17. 04:04

    Schema Drift That Fixes Itself — Letting AI Patch Your Pipeline Without a Ticket

    04:04

  18. 04:30

    Stop Measuring Uptime — The New SLA Every Senior Data Engineer Is Moving To

    04:30

  19. 05:11

    LangGraph + Airflow — The Production AI Agent Pattern Data Teams Are Shipping

    05:11

  20. 02:22

    How I Built a Complete Data Engineering Pipeline from a Teams Message Using Claude Code

    02:22

  21. 04:01

    12-Month Legacy Migration Done in 6 Weeks — The AI-Driven Playbook for Data Teams

    04:01

  22. 04:07

    Let Claude Write the Data Engineering Tests You Forgot — Prompt Patterns That Actually Work

    04:07

  23. 03:48

    Natural Language to SQL in Production — 3 Wins and 3 Disasters

    03:48

  24. 03:30

    Your Data Passed Every Test and Is Still Wrong — Semantic Data Validation Explained

    03:30

  25. 04:29

    Your LLM Bill Is About to Explode — Token Budgets as a First-Class Pipeline SLI

    04:29