Analytics Engineering with SQL and dbt: Building Meaningful Data Models at Scale by Rui Machado and Helder Russa

Analytics Engineering with SQL and dbt: Building Meaningful Data Models at Scale by Rui Machado and Helder Russa

The core thesis of Analytics Engineering with SQL and dbt is that data is a product, and it should be built, tested, and maintained with the exact same rigor as customer-facing software. In many modern organizations, data pipelines are notoriously unstable—often consisting of thousand-line, nested SQL queries that nobody understands, leading to conflicting metrics on executive dashboards. The authors introduce analytics engineering as the critical missing link that bridges the gap between raw data infrastructure and clean, business-ready analysis.

Rather than discussing abstract database theories, Machado and Russa walk readers through the end-to-end architecture of a scalable data platform. They demonstrate how to take raw, messy data from cloud warehouses like Snowflake, BigQuery, or Databricks and systematically transform it into highly optimized, documented, and fully tested data models. Using dbt as the orchestrator, the book details how to write modular SQL code, manage complex table dependencies (data lineage), implement automated quality guardrails, and safely deploy code changes using Git workflows.

As our regional technology and corporate sectors rapidly digitize, local companies are accumulating unprecedented amounts of transactional, user, and operational data. However, many data teams are struggling to manage this sudden influx. Analysts spend up to 80% of their time manually cleaning spreadsheets, fixing broken dashboards, and arguing over why different departments have completely different numbers for the exact same business metric. This operational friction paralyzes decision-making and wastes immense engineering resources.

Analytics Engineering with SQL and dbt provides an exceptionally clear, industry-proven roadmap to solve these exact systemic bottlenecks. Rui Machado and Hélder Russa strip away the confusion around data architecture, providing local engineering leads, startup founders, and business intelligence teams with a highly disciplined blueprint to build absolute reliability into their data platforms. It is an indispensable, must-read technical playbook for any local professional ready to master the modern data stack, eliminate data-team burnout, and transform raw enterprise databases into a powerful corporate asset.

Language: English.

Genre: Data Engineering.

Binding: সেলাই করা বাইন্ডিং

Quality: Premium Quality Books.

Printing: High Quality Printing.

Paper: Eye Friendly paper (Cream White)

Cover: Matt cover (Paperback).