Dbt For Analytics Engineering On Bigquery

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Dbt For Analytics Engineering On Bigquery

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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-10-25

Dbt For Analytics Engineering On Bigquery written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-25 with Computers categories.

"DBT for Analytics Engineering on BigQuery" Unlock the full potential of your data warehouse by mastering the intersection of analytics engineering, DBT, and Google BigQuery. "DBT for Analytics Engineering on BigQuery" offers a comprehensive, technical deep dive into building robust, scalable, and cost-efficient analytics pipelines in the cloud. The book begins by laying a strong foundation in BigQuery architecture and analytics engineering paradigms, exploring key concepts such as storage and compute separation, distributed query execution, data governance, and the transformational role of DBT within the modern data lifecycle. Progressing beyond fundamentals, the text delivers practical guidance on advanced DBT features tailored for BigQuery, including incremental models, materializations, macros, and templating for reusable and flexible model design. Readers will learn sophisticated data modeling techniques—ranging from star and snowflake schemas to Data Vault and temporal modeling—while gaining hands-on strategies for ingesting semi-structured data, implementing change data capture, enforcing granular security, and automating documentation and data lineage. Integrated workflows for testing, validation, documentation, and data quality assurance ensure that every transformation meets enterprise-grade standards for reliability and compliance. Culminating with real-world solutions for orchestration, DevOps, and emerging analytics applications, the book details best practices for scheduling, deployment, and monitoring of DBT projects in production, including CI/CD integration, cost optimization, and performance engineering. The final chapters look to the future, covering the rapid evolution of semantic layers, AI-driven observability, data contracts, and mesh architectures. Whether you are a data engineer, analytics professional, or technical leader, this guide equips you to harness DBT and BigQuery for next-generation analytics engineering at scale.

Analytics Engineering With Sql And Dbt

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Author : Rui Pedro Machado
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-12-08

Analytics Engineering With Sql And Dbt written by Rui Pedro Machado and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-08 with Computers categories.

With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. dbt (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL. Authors Rui Machado from Monstarlab and Hélder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence. With this book, you'll learn: What dbt is and how a dbt project is structured How dbt fits into the data engineering and analytics worlds How to collaborate on building data models The main tools and architectures for building useful, functional data models How to fit dbt into data warehousing and laking architecture How to build tests for data transformations

Fundamentals Of Analytics Engineering

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Author : Dumky De Wilde
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-29

Fundamentals Of Analytics Engineering written by Dumky De Wilde and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Computers categories.

Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering Key Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Scale data processing with cloud based analytics platforms and tools Understand the principles of data quality management and data governance Streamline code base with best practices like collaborative coding, version control, reviews and standards Automate and orchestrate data pipelines Drive business adoption with effective scoping and prioritization of analytics use cases Who this book is for This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.

Dbt Analytics Engineering Ithome

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Author : 謝秉芳（Karen Hsieh）、黃郁豪（Bruce Huang）、韓衣錦（Michael Han）、羅可涵（Stacy Lo）
language : zh-CN
Publisher: 博碩文化
Release Date : 2024-11-15

Dbt Analytics Engineering Ithome written by 謝秉芳（Karen Hsieh）、黃郁豪（Bruce Huang）、韓衣錦（Michael Han）、羅可涵（Stacy Lo） and has been published by 博碩文化 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-15 with Computers categories.

♛ 第一本 dbt 繁體中文書 ♛ 資料分析師與工程師必讀的技術及職涯實戰指南 本書改編自第 15 屆 iThome 鐵人賽 AI & Data 組優選系列文章《被 dbt 帶入門的數據工作體驗 30 想》及其團隊夥伴作品。四位作者由不同身份和視角出發，分享如何透過 dbt 實踐 Analytics Engineering（分析工程）。 dbt 是一個以 SQL 為基底的開源資料轉換工具，採用軟體工程原則，如版本控制、測試、模組化，讓資料轉換更可靠且高效。本書將帶你動手建立 dbt 專案，親自體驗其優勢。 Analytics Engineering 則是隨著資料產業演化而發展出的新興領域，介於資料分析和資料工程之間，且和兩者的部分任務重疊。 除技術外，書中也會討論資料文化、如何打造資料團隊，以及資料專業的職涯規劃和發展。無論新手或老手，本書都是能為你提供獨到見解的實用指南。 【重點摘要】 ✦ dbt 由淺入深 dbt Cloud 及 dbt Core 實作應用 ✦ 動手操作 附範例、語法、操作截圖 ✦ 資料分析必備 資料品質及建模最佳實踐 ✦ 打造資料文化 資料團隊現代化經典案例 【目標讀者】 任何工作中使用資料的人。包含： ◆ 參與資料專案的成員，不論你在資料部門，或是支援資料專案的軟體部門。你負責資料轉換成資訊的過程，想嘗試新工具，解決原本資料流程遇到的痛點。 ◆ 在工作上經常使用資料的角色，例如：行銷、Sales、PM、財務、營運人員等。你對資料、報表有好奇心、想知道資料轉換成資訊的過程，並且喜歡動手操作。 【專業推薦】 透過真實案例與深入見解，引導你有效導入 dbt，營造資料驅動環境。無論你是資料處理老手或新手，本書皆提供所需知識與工具，幫助組織進入資料引導決策的未來。 ──── 高嘉良（CL Kao）｜Recce, CEO 這本書涵蓋了打造優秀數據團隊所需的全方位知識，不僅適合技術人員閱讀，也非常適合產品經理、商業分析師等角色參考。 ──── Richard Lee｜TNL Mediagene 技術長 因緣際會被我推坑的 Taipei dbt Meetup 熱血志工群，融合真實經驗，以案例故事呈現 Data 如何貫穿企業，讓你認識 dbt 並一窺 Data Team 的重要定位。 ──── 陳正瑋（艦長）｜DevOps Taiwan Community 志工／前 Organizer

Cloud Native Financial Data Engineering Principles Pipelines And Scalable Architectures 2025

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Author : Author1:- ANOOP PURUSHOTAMAN, Author2:- PROF. DR M K SHARMA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Cloud Native Financial Data Engineering Principles Pipelines And Scalable Architectures 2025 written by Author1:- ANOOP PURUSHOTAMAN, Author2:- PROF. DR M K SHARMA and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The financial services industry has undergone a profound transformation over the past decade. From high-frequency trading firms demanding millisecond-level insights to retail banks seeking richer, personalized customer analytics, the scale, velocity, and variety of financial data have exploded. Traditional on-premises data warehouses and batch-oriented ETL pipelines struggle to keep pace with today’s requirements for real-time risk monitoring, fraud detection, algorithmic trading signals, and regulatory reporting. In parallel, the rise of cloud computing has unlocked virtually unlimited storage and compute capacity, democratized access to sophisticated analytics tools, and fostered an ecosystem of serverless and managed services designed for elasticity and resilience. This book, Cloud-Native Financial Data Engineering: Principles, Pipelines, and Scalable Architectures, is born out of the need to bridge these trends. It is written for data engineers, architects, and technology leaders who are tasked with designing and operating the next generation of financial data platforms. Whether you are building a streaming pipeline to ingest market quotes, an event-driven system to detect anomalous trading patterns, or a unified data lake that brings together transaction, customer, and risk data, the cloud offers a paradigm shift: you can focus on business logic and analytical value, rather than on undifferentiated heavy lifting of infrastructure. In the chapters that follow, we first establish the foundational principles of cloud-native data engineering in a financial context. We examine how to decompose monolithic ETL workflows into micro-services and pipelines, how to embrace immutable, append-only event stores, and how to design for failure and recovery at every layer. We then explore the core building blocks of modern data architecture: data ingestion patterns (batch, stream, change-data capture), transformation frameworks (serverless functions, containerized jobs, SQL-on-data-lake), metadata management, and orchestration engines. Along the way, we emphasize best practices for security, governance, and cost optimization—imperatives in a regulated, risk-averse industry. Subsequent sections dive into specialized topics that address the unique demands of financial workloads. We cover real-time analytics use cases such as market data enrichment, fraud-signal propagation, and credit-scoring model deployment. We unpack architectural patterns for high-throughput, low-latency pipelines—leveraging managed streaming platforms, serverless compute, column-arithmetic engines, and cloud-native message buses. We also address data quality and lineage at scale, showing how to embed continuous validation tests and visibility into every pipeline stage, thereby ensuring that trading strategies and risk models rest on a bedrock of trusted data. A recurring theme throughout this book is scalability: both horizontal scalability of compute and storage, and organizational scalability via self-service data platforms. We explore how to enable “data as a product” within your enterprise—providing domain teams with curated, discoverable datasets, APIs, and developer tooling so they can build analytics and machine-learning solutions without reinventing ingestion pipelines or wrestling with infrastructure details. This shift not only accelerates time to insight but also frees centralized engineering teams to focus on platform reliability, cost governance, and feature innovation. By combining conceptual frameworks with concrete, provider-agnostic examples, this book aims to be both a roadmap and a practical guide. Wherever possible, we illustrate patterns with code snippets and architectural diagrams, while also pointing to managed services offered by leading cloud providers. We encourage you to adapt these patterns to your organization’s existing standards and to rigorously validate them within your security and compliance constraints. As the lines between “finance” and “technology” continue to blur, the ability to engineer data pipelines that are resilient, elastic, and observably sound becomes a strategic differentiator. Whether you are modernizing a legacy data warehouse, building a next-gen risk platform, or architecting a real-time trading analytics engine, the cloud-native principles and patterns in this volume will equip you to deliver robust, cost-effective solutions that meet the exact demands of financial markets and regulatory bodies alike. We extend our gratitude to the practitioners, open-source contributors, and early adopters whose insights and feedback have shaped this book. It is our hope that by sharing these learnings, we collectively raise the bar for financial data engineering and help usher in an era where data-driven decisions can be made with confidence, speed, and scale. Authors

Mastering Snowflake Dataops With Dataops Live

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Author : Ronald L. Steelman Jr.
language : en
Publisher: Springer Nature
Release Date : 2025-10-30

Mastering Snowflake Dataops With Dataops Live written by Ronald L. Steelman Jr. and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-30 with Computers categories.

This practical, in-depth guide shows you how to build modern, sophisticated data processes using the Snowflake platform and DataOps.live—the only platform that enables seamless DataOps integration with Snowflake. Designed for data engineers, architects, and technical leaders, it bridges the gap between DataOps theory and real-world implementation, helping you take control of your data pipelines to deliver more efficient, automated solutions.. You’ll explore the core principles of DataOps and how they differ from traditional DevOps, while gaining a solid foundation in the tools and technologies that power modern data management—including Git, DBT, and Snowflake. Through hands-on examples and detailed walkthroughs, you’ll learn how to implement your own DataOps strategy within Snowflake and maximize the power of DataOps.live to scale and refine your DataOps processes. Whether you're just starting with DataOps or looking to refine and scale your existing strategies, this book—complete with practical code examples and starter projects—provides the knowledge and tools you need to streamline data operations, integrate DataOps into your Snowflake infrastructure, and stay ahead of the curve in the rapidly evolving world of data management. What You Will Learn Explore the fundamentals of DataOps, its differences from DevOps, and its significance in modern data management Understand Git’s role in DataOps and how to use it effectively Know why DBT is preferred for DataOps and how to apply it Set up and manage DataOps.live within the Snowflake ecosystem Apply advanced techniques to scale and evolve your DataOps strategy Who This Book Is For Snowflake practitioners—including data engineers, platform architects, and technical managers—who are ready to implement DataOps principles and streamline complex data workflows using DataOps.live.

Data Engineering With Dbt

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Author : Roberto Zagni
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-06-30

Data Engineering With Dbt written by Roberto Zagni and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.

Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and run Purchase of the print or Kindle book includes a free PDF eBook Key Features Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasets Book Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You’ll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you’ll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you’ll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that’ll enable you to build reports with the BI tool of your choice.What you will learn Create a dbt Cloud account and understand the ELT workflow Combine Snowflake and dbt for building modern data engineering pipelines Use SQL to transform raw data into usable data, and test its accuracy Write dbt macros and use Jinja to apply software engineering principles Test data and transformations to ensure reliability and data quality Build a lightweight pragmatic data platform using proven patterns Write easy-to-maintain idempotent code using dbt materialization Who this book is for This book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started.

Dbt For Analytics Engineering

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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-20

Dbt For Analytics Engineering written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-20 with Computers categories.

"dbt for Analytics Engineering" "dbt for Analytics Engineering" is a comprehensive guide for modern data practitioners seeking to master the evolving discipline of analytics engineering. The book begins by tracing the origins of analytics engineering and examining the emergence of the modern data stack, with an in-depth look at dbt’s transformative role in shaping data workflows, architectural patterns, and large-scale organizational adoption. Through real-world case studies and expert insights, readers will gain a foundational understanding of how dbt enables efficient, collaborative, and scalable data transformation practices within diverse business contexts. Diving into advanced project architecture, the book offers practical frameworks for structuring scalable dbt projects, managing configurations across multiple environments, and implementing robust model materializations. Readers will learn to harness Jinja and macros for code reusability, ensure high-performance data modeling using dimensional and Data Vault approaches, and adopt modular design patterns that optimize both maintainability and analytical clarity. In addition, dedicated chapters address the rigorous testing, quality assurance, and data governance practices needed to ensure trust, compliance, and discoverability in enterprise data assets. The practical reach of "dbt for Analytics Engineering" extends to cloud warehouse optimization, orchestration, automation, and CI/CD integration, providing readers with strategies for deploying and managing analytics projects at enterprise scale. The book concludes by exploring the technological frontiers of analytics engineering—from integrating machine learning and real-time data streaming to building custom dbt plugins and embracing federated data models. With actionable guidance on scaling analytics teams, managing dependencies, and implementing secure, audit-ready workflows, this book is an indispensable resource for anyone seeking to lead or innovate in the era of modern analytics engineering.

Bigquery Foundations And Advanced Techniques

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-05

Bigquery Foundations And Advanced Techniques written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-05 with Computers categories.

"BigQuery Foundations and Advanced Techniques" Unlock the full potential of Google BigQuery with "BigQuery Foundations and Advanced Techniques," the definitive guide for data engineers, architects, and analytics professionals seeking to master serverless cloud data warehousing. This comprehensive volume delves deep into BigQuery’s architecture, exploring its serverless design, storage model, and tightly integrated GCP ecosystem. Readers will gain a clear understanding of data organization strategies, resource management, and robust API options, establishing a strong foundation for building scalable analytics solutions. Building on these core concepts, the book expertly navigates through essential practices in schema design, partitioning, clustering, and data lake architecture, empowering users to manage massive datasets efficiently. Practical, hands-on chapters cover batch and streaming ingestion, sophisticated ETL/ELT workflows, and advanced query optimization techniques—including the use of BigQuery’s SQL extensions, UDFs, materialized views, and caching mechanisms. The reader will also find invaluable insights into operational excellence, encompassing monitoring, automation, spend management, and CI/CD for modern analytics pipelines. Going beyond the fundamentals, the guide addresses advanced topics such as identity management, data protection, regulatory compliance, and fine-grained security, ensuring enterprise-grade governance. Readers will also explore the frontiers of analytics, from BigQuery ML and geospatial analysis to federated queries and hybrid cloud interoperability. Rounded out with best practices, anti-patterns, migration strategies, and a forward-looking perspective on data warehousing trends, "BigQuery Foundations and Advanced Techniques" is essential reading for professionals aiming to lead and innovate in today’s cloud analytics landscape.

Sqlfluff For Dbt Projects

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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-19

Sqlfluff For Dbt Projects written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.

"SQLFluff for dbt Projects" In the rapidly evolving world of analytics engineering, ensuring high-quality and maintainable SQL code is more critical than ever. "SQLFluff for dbt Projects" delivers a comprehensive roadmap to elevating code quality within modern data stacks, starting by exploring the foundational principles of SQL linting and static analysis. This authoritative guide examines the transformative role of dbt, the unique challenges it presents for SQL style and consistency, and introduces the SQLFluff project—showcasing its guiding philosophy, architecture, and its essential place within analytics tooling. Delving deep into technical implementation, the book expertly navigates the modular design of SQLFluff, rule execution mechanics, and the intricacies of linting templated SQL in dbt environments. Readers will find detailed guidance on configuring SQLFluff for compatibility with dbt’s Jinja templating, authoring and managing custom rules, and leveraging robust automation with CI/CD and GitOps workflows. Real-world strategies for troubleshooting, scaling, and achieving organizational compliance are paired with advanced techniques for monitoring, debugging, and continuously optimizing SQL quality at scale. Looking ahead, "SQLFluff for dbt Projects" charts the future of linting and code governance, reflecting on emerging trends such as AI-driven linting, integration with data observability, and fostering a collaborative culture around open-source best practices. With actionable insights on onboarding, large-scale adoption, and measuring quality outcomes, this book is an indispensable resource for analytics engineers, data teams, and organizations striving for excellence and efficiency in their data transformation projects.