Data Engineering for Analytics and AI: From Raw Data to Production: Build Reliable Data Pipelines and Products with SQL, dbt, Quality, and Hands-On Pr [Portuguese]

By Deivison Viana Andrade

Stop building dashboards and AI applications on data you cannot trust.

Modern organizations have more data than ever-but unreliable pipelines, inconsistent metrics, broken transformations, poor data quality, and unclear ownership continue to undermine analytics and artificial intelligence projects.

Data Engineering for Analytics and AI is a practical, end-to-end guide to building reliable data systems that turn raw information into production-ready analytics products.

Designed for data analysts, BI professionals, developers, aspiring data engineers, analytics engineers, and technical leaders, this book connects the foundations of Data Engineering with the modern practices of Analytics Engineering.

You will learn how to move beyond isolated SQL queries and build complete, maintainable data workflows using SQL, dbt, data modeling, automated testing, observability, governance, and semantic layers.

What You Will LearnBuild reliable data pipelines from ingestion to productionTransform and model data with advanced SQL techniquesDesign dimensional models, fact tables, dimensions, and analytical martsStructure professional Analytics Engineering projects with dbtWork with sources, staging models, intermediate layers, and martsUse Jinja, macros, incremental models, snapshots, and reusable transformationsImplement data quality tests and business-rule validationApply data contracts and reconciliation techniquesMonitor freshness, volume, schema changes, lineage, and anomaliesIntroduce Data Observability into production environmentsDefine SLAs, SLOs, ownership, and reliability practicesBuild practical Data Governance without unnecessary bureaucracyCreate catalogs, metadata, business glossaries, and trusted metricsDesign a Semantic Layer for consistent analyticsPrepare enterprise data for AI, RAG, Text-to-SQL, copilots, and intelligent agentsReduce the risk of AI hallucinations caused by unreliable business dataFrom Analytics to Engineering

If you already work with SQL, Power BI, dashboards, analytics, software development, or Data Science and want to understand what happens before the dashboard, this book provides a practical path into professional Data Engineering.

Rather than teaching:

Raw Data → Ingestion → Transformation → Modeling → Testing → Observability → Governance → Semantic Layer → Analytics → AI

Throughout the book, concepts are reinforced with commented scripts, real-world scenarios, diagrams, professional tips, review questions, exercises, troubleshooting guidance, and hands-on projects.

Build Real Projects

You will work through practical architectures that include:

An end-to-end analytics platform
Transform transactional data into clean, tested, documented analytical models.

A production-ready data quality and observability environment
Detect failures, schema changes, freshness problems, anomalies, and unreliable metrics before they reach users.

An AI assistant powered by trusted enterprise data
Explore how semantic layers, controlled queries, RAG, and governed metrics can help AI systems provide safer and more reliable answers.

Who This Book Is For

This book is ideal for:

Data Analysts ready to move into Data EngineeringBI and Power BI professionalsAnalytics EngineersJunior and Intermediate Data EngineersSoftware Developers transitioning into dataData Scientists who need more reliable data foundationsArchitects working with modern data platformsProfessionals preparing enterprise data for AI

Build pipelines. Build trusted metrics. Build data products. Build the foundation that modern AI actually needs.

• Format: Paperback
• Language: Portuguese
• ISBN: B0HG8XNT5T
• ISBN13: 9798194522620
• Release Date: August 2026
• Publisher: Independently Published
• Length: 372 Pages
• Weight: 1.42 lbs.
• Dimensions: 0.8" x 7.0" x 10.0"