Modern data engineering is evolving; and with Microsoft Fabric, the entire data platform experience is being redefined. This essential book offers a fresh, hands-on approach to navigating this shift. Rather than being an introduction to features, this guide explains how Fabric's key components—Lakehouse, Warehouse, and Real-Time Intelligence—work under the hood and how to put them to use in realistic workflows. Written by Christian Henrik Reich, a data engineering expert with experience that extends from Databricks to Fabric, this book is a blend of foundational theory and practical implementation of lakehouse solutions in Fabric. You'll explore how engines like Apache Spark and Fabric Warehouse collaborate with Fabric's Real-Time Intelligence solution in an integrated platform, and how to build ETL/ELT pipelines that deliver on speed, accuracy, and scale. Ideal for both new and practicing data engineers, this is your entry point into the fabric of the modern data platform. Acquire a working knowledge of lakehouses, warehouses, and streaming in Fabric Build resilient data pipelines across real-time and batch workloads Apply Python, Spark SQL, T-SQL, and KQL within a unified platform Gain insight into architectural decisions that scale with data needs Learn actionable best practices for engineering clean, efficient, governed solutions

Master end-to-end data engineering on Azure Databricks. From data ingestion and Delta Lake to CI/CD and real-time streaming, build secure, scalable, and performant data solutions with Spark, Unity Catalog, and ML tools. Key Features Build scalable data pipelines using Apache Spark and Delta Lake Automate workflows and manage data governance with Unity Catalog Learn real-time processing and structured streaming with practical use cases Implement CI/CD, DevOps, and security for production-ready data solutions Explore Databricks-native ML, AutoML, and Generative AI integration Book Description "Data Engineering with Azure Databricks" is your essential guide to building scalable, secure, and high-performing data pipelines using the powerful Databricks platform on Azure. Designed for data engineers, architects, and developers, this book demystifies the complexities of Spark-based workloads, Delta Lake, Unity Catalog, and real-time data processing. Beginning with the foundational role of Azure Databricks in modern data engineering, you’ll explore how to set up robust environments, manage data ingestion with Auto Loader, optimize Spark performance, and orchestrate complex workflows using tools like Azure Data Factory and Airflow. The book offers deep dives into structured streaming, Delta Live Tables, and Delta Lake’s ACID features for data reliability and schema evolution. You’ll also learn how to manage security, compliance, and access controls using Unity Catalog, and gain insights into managing CI/CD pipelines with Azure DevOps and Terraform. With a special focus on machine learning and generative AI, the final chapters guide you in automating model workflows, leveraging MLflow, and fine-tuning large language models on Databricks. Whether you're building a modern data lakehouse or operationalizing analytics at scale, this book provides the tools and insights you need. What you will learn Set up a full-featured Azure Databricks environment Implement batch and streaming ingestion using Auto Loader Optimize Spark jobs with partitioning and caching Build real-time pipelines with structured streaming and DLT Manage data governance using Unity Catalog Orchestrate production workflows with jobs and ADF Apply CI/CD best practices with Azure DevOps and Git Secure data with RBAC, encryption, and compliance standards Use MLflow and Feature Store for ML pipelines Build generative AI applications in Databricks Who this book is for This book is for data engineers, solution architects, cloud professionals, and software engineers seeking to build robust and scalable data pipelines using Azure Databricks. Whether you're migrating legacy systems, implementing a modern lakehouse architecture, or optimizing data workflows for performance, this guide will help you leverage the full power of Databricks on Azure. A basic understanding of Python, Spark, and cloud infrastructure is recommended.

In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture. Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions. Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using Databricks Leverage LLMs to extract deeper, actionable insights from your business data residing in lakehouses Discover how to integrate traditional ML and GenAI models for customized, scalable solutions Utilize open source models to control costs while maintaining model performance and efficiency Implement best practices for optimizing ML and GenAI models within the Databricks platform

Engineering Lakehouses with Open Table Formats introduces the architecture and capabilities of open table formats like Apache Iceberg, Apache Hudi, and Delta Lake. The book guides you through the design, implementation, and optimization of lakehouses that can handle modern data processing requirements effectively with real-world practical insights. What this Book will help me do Understand the fundamentals of open table formats and their benefits in lakehouse architecture. Learn how to implement performant data processing using tools like Apache Spark and Flink. Master advanced topics like indexing, partitioning, and interoperability between data formats. Explore data lifecycle management and integration with frameworks like Apache Airflow and dbt. Build secure lakehouses with regulatory compliance using best practices detailed in the book. Author(s) Dipankar Mazumdar and Vinoth Govindarajan are seasoned professionals with extensive experience in big data processing and software architecture. They bring their expertise from working with data lakehouses and are known for their ability to explain complex technical concepts clearly. Their collaborative approach brings valuable insights into the latest trends in data management. Who is it for? This book is ideal for data engineers, architects, and software professionals aiming to master modern lakehouse architectures. If you are familiar with data lakes or warehouses and wish to transition to an open data architectural design, this book is suited for you. Readers should have basic knowledge of databases, Python, and Apache Spark for the best experience.

Overcome challenges in building transactional guarantees on rapidly changing data by using Apache Hudi. With this practical guide, data engineers, data architects, and software architects will discover how to seamlessly build an interoperable lakehouse from disparate data sources and deliver faster insights using your query engine of choice. Authors Shiyan Xu, Prashant Wason, Bhavani Sudha Saktheeswaran, and Rebecca Bilbro provide practical examples and insights to help you unlock the full potential of data lakehouses for different levels of analytics, from batch to interactive to streaming. You'll also learn how to evaluate storage choices and leverage built-in automated table optimizations to build, maintain, and operate production data applications. Understand the need for transactional data lakehouses and the challenges associated with building them Explore data ecosystem support provided by Apache Hudi for popular data sources and query engines Perform different write and read operations on Apache Hudi tables and effectively use them for various use cases, including batch and stream applications Apply different storage techniques and considerations such as indexing and clustering to maximize your lakehouse performance Build end-to-end incremental data pipelines using Apache Hudi for faster ingestion and fresher analytics

"Extract, transform, load" (ETL) is at the center of every application of data, from business intelligence to AI. Constant shifts in the data landscape—including the implementations of lakehouse architectures and the importance of high-scale real-time data—mean that today's data practitioners must approach ETL a bit differently. This updated technical guide offers data engineers, engineering managers, and architects an overview of the modern ETL process, along with the challenges you're likely to face and the strategic patterns that will help you overcome them. You'll come away equipped to make informed decisions when implementing ETL and confident about choosing the technology stack that will help you succeed. Discover what ETL looks like in the new world of data lakehouses Learn how to deal with real-time data Explore low-code ETL tools Understand how to best achieve scale, performance, and observability

Revolutionize your understanding of modern data management with Apache Polaris (incubating), the open source catalog designed for data lakehouse industry standard Apache Iceberg. This comprehensive guide takes you on a journey through the intricacies of Apache Iceberg data lakehouses, highlighting the pivotal role of Iceberg catalogs. Authors Alex Merced, Andrew Madson, and Tomer Shiran explore Apache Polaris's architecture and features in detail, equipping you with the knowledge needed to leverage its full potential. Data engineers, data architects, data scientists, and data analysts will learn how to seamlessly integrate Apache Polaris with popular data tools like Apache Spark, Snowflake, and Dremio to enhance data management capabilities, optimize workflows, and secure datasets. Get a comprehensive introduction to Iceberg data lakehouses Understand how catalogs facilitate efficient data management and querying in Iceberg Explore Apache Polaris's unique architecture and its powerful features Deploy Apache Polaris locally, and deploy managed Apache Polaris from Snowflake and Dremio Perform basic table operations on Apache Spark, Snowflake, and Dremio

Data engineers proficient in Databricks are currently in high demand. As organizations gather more data than ever before, skilled data engineers on platforms like Databricks become critical to business success. The Databricks Data Engineer Associate certification is proof that you have a complete understanding of the Databricks platform and its capabilities, as well as the essential skills to effectively execute various data engineering tasks on the platform. In this comprehensive study guide, you will build a strong foundation in all topics covered on the certification exam, including the Databricks Lakehouse and its tools and benefits. You'll also learn to develop ETL pipelines in both batch and streaming modes. Moreover, you'll discover how to orchestrate data workflows and design dashboards while maintaining data governance. Finally, you'll dive into the finer points of exactly what's on the exam and learn to prepare for it with mock tests. Author Derar Alhussein teaches you not only the fundamental concepts but also provides hands-on exercises to reinforce your understanding. From setting up your Databricks workspace to deploying production pipelines, each chapter is carefully crafted to equip you with the skills needed to master the Databricks Platform. By the end of this book, you'll know everything you need to ace the Databricks Data Engineer Associate certification exam with flying colors, and start your career as a certified data engineer from Databricks! You'll learn how to: Use the Databricks Platform and Delta Lake effectively Perform advanced ETL tasks using Apache Spark SQL Design multi-hop architecture to process data incrementally Build production pipelines using Delta Live Tables and Databricks Jobs Implement data governance using Databricks SQL and Unity Catalog Derar Alhussein is a senior data engineer with a master's degree in data mining. He has over a decade of hands-on experience in software and data projects, including large-scale projects on Databricks. He currently holds eight certifications from Databricks, showcasing his proficiency in the field. Derar is also an experienced instructor, with a proven track record of success in training thousands of data engineers, helping them to develop their skills and obtain professional certifications.

This book, "Building Modern Data Applications Using Databricks Lakehouse," provides a comprehensive guide for data professionals to master the Databricks platform. You'll learn to effectively build, deploy, and monitor robust data pipelines with Databricks' Delta Live Tables, empowering you to manage and optimize cloud-based data operations effortlessly. What this Book will help me do Understand the foundations and concepts of Delta Live Tables and its role in data pipeline development. Learn workflows to process and transform real-time and batch data efficiently using the Databricks lakehouse architecture. Master the implementation of Unity Catalog for governance and secure data access in modern data applications. Deploy and automate data pipeline changes using CI/CD, leveraging tools like Terraform and Databricks Asset Bundles. Gain advanced insights in monitoring data quality and performance, optimizing cloud costs, and managing DataOps tasks effectively. Author(s) Will Girten, the author, is a seasoned Solutions Architect at Databricks with over a decade of experience in data and AI systems. With a deep expertise in modern data architectures, Will is adept at simplifying complex topics and translating them into actionable knowledge. His books emphasize real-time application and offer clear, hands-on examples, making learning engaging and impactful. Who is it for? This book is geared towards data engineers, analysts, and DataOps professionals seeking efficient strategies to implement and maintain robust data pipelines. If you have a basic understanding of Python and Apache Spark and wish to delve deeper into the Databricks platform for streamlining workflows, this book is tailored for you.

This book is your comprehensive guide to building robust Generative AI solutions using the Databricks Data Intelligence Platform. Databricks is the fastest-growing data platform offering unified analytics and AI capabilities within a single governance framework, enabling organizations to streamline their data processing workflows, from ingestion to visualization. Additionally, Databricks provides features to train a high-quality large language model (LLM), whether you are looking for Retrieval-Augmented Generation (RAG) or fine-tuning. Databricks offers a scalable and efficient solution for processing large volumes of both structured and unstructured data, facilitating advanced analytics, machine learning, and real-time processing. In today's GenAI world, Databricks plays a crucial role in empowering organizations to extract value from their data effectively, driving innovation and gaining a competitive edge in the digital age. This book will not only help you master the Data Intelligence Platform but also help power your enterprise to the next level with a bespoke LLM unique to your organization. Beginning with foundational principles, the book starts with a platform overview and explores features and best practices for ingestion, transformation, and storage with Delta Lake. Advanced topics include leveraging Databricks SQL for querying and visualizing large datasets, ensuring data governance and security with Unity Catalog, and deploying machine learning and LLMs using Databricks MLflow for GenAI. Through practical examples, insights, and best practices, this book equips solution architects and data engineers with the knowledge to design and implement scalable data solutions, making it an indispensable resource for modern enterprises. Whether you are new to Databricks and trying to learn a new platform, a seasoned practitioner building data pipelines, data science models, or GenAI applications, or even an executive who wants to communicate the value of Databricks to customers, this book is for you. With its extensive feature and best practice deep dives, it also serves as an excellent reference guide if you are preparing for Databricks certification exams. What You Will Learn Foundational principles of Lakehouse architecture Key features including Unity Catalog, Databricks SQL (DBSQL), and Delta Live Tables Databricks Intelligence Platform and key functionalities Building and deploying GenAI Applications from data ingestion to model serving Databricks pricing, platform security, DBRX, and many more topics Who This Book Is For Solution architects, data engineers, data scientists, Databricks practitioners, and anyone who wants to deploy their Gen AI solutions with the Data Intelligence Platform. This is also a handbook for senior execs who need to communicate the value of Databricks to customers. People who are new to the Databricks Platform and want comprehensive insights will find the book accessible.

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

Dive into the Databricks Data Intelligence Platform and learn how to harness its full potential for creating, deploying, and maintaining machine learning solutions. This book covers everything from setting up your workspace to integrating state-of-the-art tools such as AutoML and VectorSearch, imparting practical skills through detailed examples and code. What this Book will help me do Set up and manage a Databricks workspace tailored for effective data science workflows. Implement monitoring to ensure data quality and detect drift efficiently. Build, fine-tune, and deploy machine learning models seamlessly using Databricks tools. Operationalize AI projects including feature engineering, data pipelines, and workflows on the Databricks Lakehouse architecture. Leverage integrations with popular tools like OpenAI's ChatGPT to expand your AI project capabilities. Author(s) This book is authored by Stephanie Rivera, Anastasia Prokaieva, Amanda Baker, and Hayley Horn, seasoned experts in data science and machine learning from Databricks. Their collective years of expertise in big data and AI technologies ensure a rich and insightful perspective. Through their work, they strive to make complex concepts accessible and actionable. Who is it for? This book serves as an ideal guide for machine learning engineers, data scientists, and technically inclined managers. It's well-suited for those transitioning to the Databricks environment or seeking to deepen their Databricks-based machine learning implementation skills. Whether you're an ambitious beginner or an experienced professional, this book provides clear pathways to success.

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool—a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.

Learn and explore the capabilities of Microsoft Fabric, the latest evolution in cloud analytics suites. This book will help you understand how users can leverage Microsoft Office equivalent experience for performing data management and advanced analytics activity. The book starts with an overview of the analytics evolution from on premises to cloud infrastructure as a service (IaaS), platform as a service (PaaS), and now software as a service (SaaS version) and provides an introduction to Microsoft Fabric. You will learn how to provision Microsoft Fabric in your tenant along with the key capabilities of SaaS analytics products and the advantage of using Fabric in the enterprise analytics platform. OneLake and Lakehouse for data engineering is discussed as well as OneLake for data science. Author Ghosh teaches you about data warehouse offerings inside Microsoft Fabric and the new data integration experience which brings Azure Data Factory and Power Query Editor of Power BI together in a single platform. Also demonstrated is Real-Time Analytics in Fabric, including capabilities such as Kusto query and database. You will understand how the new event stream feature integrates with OneLake and other computations. You also will know how to configure the real-time alert capability in a zero code manner and go through the Power BI experience in the Fabric workspace. Fabric pricing and its licensing is also covered. After reading this book, you will understand the capabilities of Microsoft Fabric and its Integration with current and upcoming Azure OpenAI capabilities. What You Will Learn Build OneLake for all data like OneDrive for Microsoft Office Leverage shortcuts for cross-cloud data virtualization in Azure and AWS Understand upcoming OpenAI integration Discover new event streaming and Kusto query inside Fabric real-time analytics Utilize seamless tooling for machine learning and data science Who This Book Is For Citizen users and experts in the data engineering and data science fields, along with chief AI officers

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, as well as how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll: Gain a working understanding of several data architectures Learn the strengths and weaknesses of each approach Distinguish data architecture theory from reality Pick the best architecture for your use case Understand the differences between data warehouses and data lakes Learn common data architecture concepts to help you build better solutions Explore the historical evolution and characteristics of data architectures Learn essentials of running an architecture design session, team organization, and project success factors Free from product discussions, this book will serve as a timeless resource for years to come.

Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines. What You Will Learn Understand the core concepts underlying modern data warehouses Design and build cloud-native data warehousesGain a practical approach to architecting and building data warehouses on Azure and AWS Implement modern data warehousing components such as Data Mesh, Data Lake, Delta Lake, and Lakehouse Process data through pandas and evaluate your model’s performance using metrics such as F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Experienced developers, cloud architects, and technology enthusiasts looking to build cloud-based modern data warehouses using Azure and AWS

With the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS. This practical book shows data engineers, data scientists, and data analysts how to get Delta Lake and its features up and running. The ultimate goal of building data pipelines and applications is to gain insights from data. You'll understand how your storage solution choice determines the robustness and performance of the data pipeline, from raw data to insights. You'll learn how to: Use modern data management and data engineering techniques Understand how ACID transactions bring reliability to data lakes at scale Run streaming and batch jobs against your data lake concurrently Execute update, delete, and merge commands against your data lake Use time travel to roll back and examine previous data versions Build a streaming data quality pipeline following the medallion architecture

Perform fast interactive analytics against different data sources using the Trino high-performance distributed SQL query engine. In the second edition of this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's a data lake using Hive, a modern lakehouse with Iceberg or Delta Lake, a different system like Cassandra, Kafka, or SingleStore, or a relational database like PostgreSQL or Oracle. Analysts, software engineers, and production engineers learn how to manage, use, and even develop with Trino and make it a critical part of their data platform. Authors Matt Fuller, Manfred Moser, and Martin Traverso show you how a single Trino query can combine data from multiple sources to allow for analytics across your entire organization. Explore Trino's use cases, and learn about tools that help you connect to Trino for querying and processing huge amounts of data Learn Trino's internal workings, including how to connect to and query data sources with support for SQL statements, operators, functions, and more Deploy and secure Trino at scale, monitor workloads, tune queries, and connect more applications Learn how other organizations apply Trino successfully

Discover the power of business intelligence through Databricks SQL. This comprehensive guide explores the features and tools of the Databricks Lakehouse Platform, emphasizing how it leverages data lakes and warehouses for scalable analytics. You'll gain hands-on experience with Databricks SQL, enabling you to manage data efficiently and implement cutting-edge analytical solutions. What this Book will help me do Comprehend the core features of Databricks SQL and its role in the Lakehouse architecture. Master the use of Databricks SQL for conducting scalable and efficient data queries. Implement data management techniques, including security and cataloging, with Databricks. Optimize data performance using Delta Lake and Photon technologies with Databricks SQL. Compose advanced SQL scripts for robust data ingestion and analytics workflows. Author(s) Vihag Gupta, acclaimed data engineer and BI expert, brings a wealth of experience in large-scale data analytics to this work. With a career deeply rooted in cutting-edge data warehousing technologies, Vihag combines expertise with an approachable teaching style. This book reflects his commitment to empowering data professionals with tools for next-gen analytics. Who is it for? Ideal for data engineers, business intelligence analysts, and warehouse administrators aiming to enhance their practice with Databricks SQL. This book suits those with fundamental knowledge of SQL and data platforms seeking to adopt Lakehouse methodologies. Whether a novice to Databricks or looking to master advanced features, this guide will support professional growth.

Design and implement a modern data lakehouse on the Azure Data Platform using Delta Lake, Apache Spark, Azure Databricks, Azure Synapse Analytics, and Snowflake. This book teaches you the intricate details of the Data Lakehouse Paradigm and how to efficiently design a cloud-based data lakehouse using highly performant and cutting-edge Apache Spark capabilities using Azure Databricks, Azure Synapse Analytics, and Snowflake. You will learn to write efficient PySpark code for batch and streaming ELT jobs on Azure. And you will follow along with practical, scenario-based examples showing how to apply the capabilities of Delta Lake and Apache Spark to optimize performance, and secure, share, and manage a high volume, high velocity, and high variety of data in your lakehouse with ease. The patterns of success that you acquire from reading this book will help you hone your skills to build high-performing and scalable ACID-compliant lakehouses using flexible and cost-efficient decoupled storage and compute capabilities. Extensive coverage of Delta Lake ensures that you are aware of and can benefit from all that this new, open source storage layer can offer. In addition to the deep examples on Databricks in the book, there is coverage of alternative platforms such as Synapse Analytics and Snowflake so that you can make the right platform choice for your needs. After reading this book, you will be able to implement Delta Lake capabilities, including Schema Evolution, Change Feed, Live Tables, Sharing, and Clones to enable better business intelligence and advanced analytics on your data within the Azure Data Platform. What You Will Learn Implement the Data Lakehouse Paradigm on Microsoft’s Azure cloud platform Benefit from the new Delta Lake open-source storage layer for data lakehouses Take advantage of schema evolution, change feeds, live tables, and more Writefunctional PySpark code for data lakehouse ELT jobs Optimize Apache Spark performance through partitioning, indexing, and other tuning options Choose between alternatives such as Databricks, Synapse Analytics, and Snowflake Who This Book Is For Data, analytics, and AI professionals at all levels, including data architect and data engineer practitioners. Also for data professionals seeking patterns of success by which to remain relevant as they learn to build scalable data lakehouses for their organizations and customers who are migrating into the modern Azure Data Platform.