Analytics Engineering With Sql And Dbt
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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.
Sql For Data Analysis
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Author : Cathy Tanimura
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-09-09
Sql For Data Analysis written by Cathy Tanimura 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 2021-09-09 with Computers categories.
With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow. You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference. Learn the key steps for preparing your data for analysis Perform time series analysis using SQL's date and time manipulations Use cohort analysis to investigate how groups change over time Use SQL's powerful functions and operators for text analysis Detect outliers in your data and replace them with alternate values Establish causality using experiment analysis, also known as A/B testing
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.
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
Cloud First Data Engineering Architecting Scalable Pipelines And Analytics With Aws 2025
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Author : Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Cloud First Data Engineering Architecting Scalable Pipelines And Analytics With Aws 2025 written by Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN 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.
Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA ISBN - 978-93-6788-817-9 Preface In today’s digital economy, organizations generate more data in a single day than many legacy systems could process in years. The shift to cloud-first architectures has transformed how we collect, store, and analyze information—enabling businesses to respond faster to market changes, scale without upfront hardware investments, and foster innovation across teams. This book, Cloud-First Data Engineering: Architecting Scalable Pipelines and Analytics with AWS, is written for data engineers, architects, and technical leaders who seek to design robust, high-performing data platforms using Amazon Web Services. Over the past decade, AWS has introduced a rich portfolio of data services—ranging from serverless ETL (AWS Glue) and streaming solutions (Kinesis, MSK) to petabyte-scale analytics (Redshift, Athena) and machine learning integrations (SageMaker). Yet, with such breadth comes complexity: selecting the right components, designing for cost efficiency, maintaining security and compliance, and ensuring operational excellence are constant challenges. This book distills best practices, architectural patterns, and real-world examples into a cohesive roadmap. You will learn how to build end-to-end pipelines that evolve with your data volume, implement modern data Lakehouse strategies, enable real-time insights, and incorporate governance at every layer. Chapters progress from foundational concepts—such as cloud-first paradigms and core AWS data services—to advanced topics like Data Mesh, serverless Lakehouse’s, generative AI for data quality, and emerging roles in data organization. Each section demystifies the trade-offs, illustrates implementation steps, and highlights pitfalls to avoid. Whether you are migrating legacy workloads, optimizing existing pipelines, or pioneering new analytics capabilities, this book serves as both a practical guide and strategic playbook to navigate the ever-changing landscape of cloud data engineering on AWS. Authors
Practical Lakehouse Architecture
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Author : Gaurav Ashok Thalpati
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-07-24
Practical Lakehouse Architecture written by Gaurav Ashok Thalpati 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 2024-07-24 with Computers categories.
This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse
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
Data That Drives Engineering Bi And Etl For Business Transformation
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Author : Dhaval Patolia
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2025-05-23
Data That Drives Engineering Bi And Etl For Business Transformation written by Dhaval Patolia and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
Business Intelligence (BI) and Extract, Transform, and Load (ETL) procedures are becoming more important to organisations in today's data- driven economy. These processes are used to drive strategic decision-making and obtain a competitive edge. Within the context of facilitating business transformation, this chapter offers an examination of the crucial role that developing effective BI and ETL frameworks plays. Business intelligence systems are able to transform raw data into actionable insights that can be used to improve operational efficiency, customer engagement, and innovation. This is accomplished via the systematic collection, processing, and analysis of massive amounts of heterogeneous data and information. An emphasis is placed in the research on the architectural design of ETL pipelines that are scalable, adaptable, and real-time. These pipelines should guarantee that data is of high quality, consistent, and timely. It analyses contemporary data engineering approaches such as API integration, Change Data Capture (CDC), and stream processing, all of which make it possible to consume and convert data from a variety of sources in a seamless manner. In addition to this, the study emphasises the use of sophisticated analytics and visualisation technologies that provide stakeholders at all levels of the organisation additional leverage. This chapter explains, through the use of case studies and best practices, how well-engineered business intelligence (BI) and enterprise transaction flow (ETL) systems not only increase the accuracy of reporting and forecasting, but also allow proactive business plans, agile reactions to changes in the market, and continuous development. The results highlight how important it is to achieve alignment between data engineering and business objectives, governance regulations, and new technologies like as machine learning and cloud computing. The purpose of this work is to provide a thorough guide for data engineers, business analysts, and decision-makers who are interested in maximising the potential of their data assets in order to achieve real business change.
Advanced Data Analytics With Aws
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Author : Joseph Conley
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-04-17
Advanced Data Analytics With Aws written by Joseph Conley and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-17 with Computers categories.
Master the Fundamentals of Data Analytics at Scale KEY FEATURES ● Comprehensive guide to constructing data engineering workflows spanning diverse data sources ● Expert techniques for transforming and visualizing data to extract actionable insights ● Advanced methodologies for analyzing data and employing machine learning to uncover intricate patterns DESCRIPTION Embark on a transformative journey into the realm of data analytics with AWS with this practical and incisive handbook. Begin your exploration with an insightful introduction to the fundamentals of data analytics, setting the stage for your AWS adventure. The book then covers collecting data efficiently and effectively on AWS, laying the groundwork for insightful analysis. It will dive deep into processing data, uncovering invaluable techniques to harness the full potential of your datasets. The book will equip you with advanced data analysis skills, unlocking the ability to discern complex patterns and insights. It covers additional use cases for data analysis on AWS, from predictive modeling to sentiment analysis, expanding your analytical horizons. The final section of the book will utilize the power of data virtualization and interaction, revolutionizing the way you engage with and derive value from your data. Gain valuable insights into emerging trends and technologies shaping the future of data analytics, and conclude your journey with actionable next steps, empowering you to continue your data analytics odyssey with confidence. WHAT WILL YOU LEARN ● Construct streamlined data engineering workflows capable of ingesting data from diverse sources and formats. ● Employ data transformation tools to efficiently cleanse and reshape data, priming it for analysis. ● Perform ad-hoc queries for preliminary data exploration, uncovering initial insights. ● Utilize prepared datasets to craft compelling, interactive data visualizations that communicate actionable insights. ● Develop advanced machine learning and Generative AI workflows to delve into intricate aspects of complex datasets, uncovering deeper insights. WHO IS THIS BOOK FOR? This book is ideal for aspiring data engineers, analysts, and data scientists seeking to deepen their understanding and practical skills in data engineering, data transformation, visualization, and advanced analytics. It is also beneficial for professionals and students looking to leverage AWS services for their data-related tasks. TABLE OF CONTENTS 1. Introduction to Data Analytics and AWS 2. Getting Started with AWS 3. Collecting Data with AWS 4. Processing Data on AWS 5. Descriptive Analytics on AWS 6. Advanced Data Analysis on AWS 7. Additional Use Cases for Data Analysis 8. Data Visualization and Interaction on AWS 9. The Future of Data Analytics 10. Conclusion and Next Steps Index