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(as of Oct 05, 2026 20:16:17 UTC – Details)
Great AI depends on great data. Do you know how to build the systems behind it?
A language model can be impressive and still give the wrong answer. A RAG assistant can retrieve an outdated policy. An AI agent can act on incomplete customer information. A data pipeline can finish successfully while quietly producing incorrect results.
The difference between an AI demo and a dependable AI system often begins long before the model receives a prompt.
It begins with data engineering.
AI Data Engineer in 300 Questions takes you from raw data to reliable AI through 300 focused questions and clear, practical answers. You’ll learn how data is collected, processed, stored, searched, protected, and delivered to the applications that depend on it.
Rather than overwhelming you with disconnected definitions, the book helps you understand how the pieces work together—and what can go wrong when they don’t.
Inside, you’ll explore:
SQL and Python: Query, join, clean, transform, and validate data without losing its meaning.
ETL, ELT, and data pipelines: Design incremental workflows, handle retries, prevent duplicates, and recover from failures.
Data lakes, warehouses, and lakehouses: Understand storage architectures and choose them according to real workloads.
LLMs and RAG data engineering: Ingest documents, preserve structure, manage chunks and metadata, and keep knowledge bases current.
Embeddings and vector databases: Build searchable data, understand semantic and hybrid search, and maintain reliable indexes.
Data quality, governance, and security: Detect unreliable information, preserve lineage, and enforce appropriate access.
Streaming, DataOps, and production systems: Coordinate workflows, monitor freshness, manage change, and troubleshoot failures.
The final chapter brings these ideas into AI Data Engineer technical interviews and system-design scenarios, helping you explain your decisions, investigate problems, and reason through engineering trade-offs.
Who is this book for?
Aspiring AI Data Engineers, data engineers moving into AI, software developers building LLM applications, and technical professionals preparing for data engineering interviews.
You don’t need to memorize an endless list of tools. You need to understand what the data represents, how it moves, when it can be trusted, and what to do when something breaks.
300 questions. One connected journey from raw data to reliable AI.
Start building the knowledge behind the intelligence.
ASIN : B0HKGF35T3
Accessibility : Learn more
Publication date : 20 September 2026
Language : English
File size : 493 KB
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 187 pages
Page Flip : Enabled
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