Data Engineering With Dbt
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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.
Data Pipelines Pocket Reference
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Author : James Densmore
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-02-10
Data Pipelines Pocket Reference written by James Densmore 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 2021-02-10 with Computers categories.
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
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
Data Engineering With Scala And Spark
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Author : Eric Tome
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31
Data Engineering With Scala And Spark written by Eric Tome 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 2024-01-31 with Computers categories.
Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key Features Transform data into a clean and trusted source of information for your organization using Scala Build streaming and batch-processing pipelines with step-by-step explanations Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD) Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learn Set up your development environment to build pipelines in Scala Get to grips with polymorphic functions, type parameterization, and Scala implicits Use Spark DataFrames, Datasets, and Spark SQL with Scala Read and write data to object stores Profile and clean your data using Deequ Performance tune your data pipelines using Scala Who this book is for This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.
Data Engineering Fundamentals
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Author : Zhaolong Liu
language : en
Publisher: BPB Publications
Release Date : 2025-03-30
Data Engineering Fundamentals written by Zhaolong Liu and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-30 with Computers categories.
DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for anyone looking to build robust data pipelines and extract valuable insights. This book simplifies complex concepts and provides a clear pathway to understanding the core principles that power modern data solutions. It bridges the gap between raw data and actionable intelligence, making data engineering accessible to everyone. This book walks you through the entire data engineering lifecycle. Starting with foundational concepts and data ingestion from diverse sources, you will learn how to build efficient data lakes and warehouses. You will learn data transformation using tools like Apache Spark and the orchestration of data workflows with platforms like Airflow and Argo Workflow. Crucial aspects of data quality, governance, scalability, and performance monitoring are thoroughly covered, ensuring you understand how to maintain reliable and efficient data systems. Real-world use cases across industries like e-commerce, finance, and government illustrate practical applications, while a final section explores emerging trends such as AI integration and cloud advancements. By the end of this book, you will have a solid foundation in data engineering, along with practical skills to help enhance your career. You will be equipped to design, build, and maintain data pipelines, transforming raw data into meaningful insights. WHAT YOU WILL LEARN ● Understand data engineering base concepts and build scalable solutions. ● Master data storage, ingestion, and transformation. ● Orchestrates data workflows and automates pipelines for efficiency. ● Ensure data quality, governance, and security compliance. ● Monitor, optimize, and scale data solutions effectively. ● Explore real-world use cases and future data trends. WHO THIS BOOK IS FOR This book is for aspiring data engineers, analysts, and developers seeking a foundational understanding of data engineering. Whether you are a beginner or looking to deepen your expertise, this book provides you with the knowledge and tools to succeed in today’s data engineering challenges. TABLE OF CONTENTS 1. Understanding Data Engineering 2. Data Ingestion and Acquisition 3. Data Storage and Management 4. Data Transformation and Processing 5. Data Orchestration and Workflows 6. Data Governance Principles 7. Scaling Data Solutions 8. Monitoring and Performance 9. Real-world Data Engineering Use Cases 10. Future Trends in Data Engineering
Fundamentals Of Data Engineering
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Author : Joe Reis
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-06-22
Fundamentals Of Data Engineering written by Joe Reis 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 2022-06-22 with Computers categories.
Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle
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
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.
The Data Warehouse Toolkit
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Author : Ralph Kimball
language : en
Publisher: John Wiley & Sons
Release Date : 2011-08-08
The Data Warehouse Toolkit written by Ralph Kimball and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-08 with Computers categories.
This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.
The Ultimate Guide To Snowpark
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Author : Shankar Narayanan SGS
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-30
The Ultimate Guide To Snowpark written by Shankar Narayanan SGS 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 2024-05-30 with Computers categories.
Develop robust data pipelines, deploy mature machine learning models, and build secure data apps with Snowflake Snowpark using Python Key Features Get to grips with Snowflake Snowpark’s basic and advanced features Implement workloads in domains like data engineering, data science, and data applications using Snowpark with Python Deploy Snowpark in production with practical examples and best practices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionSnowpark is a powerful framework that helps you unlock numerous possibilities within the Snowflake Data Cloud. However, without proper guidance, leveraging the full potential of Snowpark with Python can be challenging. Packed with practical examples and code snippets, this book will be your go-to guide to using Snowpark with Python successfully. The Ultimate Guide to Snowpark helps you develop an understanding of Snowflake Snowpark and how it enables you to implement workloads in data engineering, data science, and data applications within the Data Cloud. From configuration and coding styles to workloads such as data manipulation, collection, preparation, transformation, aggregation, and analysis, this guide will equip you with the right knowledge to make the most of this framework. You'll discover how to build, test, and deploy data pipelines and data science models. As you progress, you’ll deploy data applications natively in Snowflake and operate large language models (LLMs) using Snowpark container services. By the end of this book, you'll be able to leverage Snowpark's capabilities and propel your career as a Snowflake developer to new heights.What you will learn Harness Snowpark with Python for diverse workloads Develop robust data pipelines with Snowpark using Python Deploy mature machine learning models Explore the process of developing, deploying, and monetizing native apps using Snowpark Deploy and operate containers in Snowpark Discover the pathway to adopting Snowpark effectively in production Who this book is for This book is for data engineers, data scientists, developers, and data practitioners seeking an in-depth understanding of Snowpark’s features and best practices for deploying various workloads in Snowpark using the Python programming language. Basic knowledge of SQL, proficiency in Python, an understanding of data engineering and data science basics, and familiarity with the Snowflake Data Cloud platform are required to get the most out of this book.