SQL is no longer just a querying language for relational databases—it's a foundational tool for building scalable, modern data solutions across real-time analytics, machine learning workflows, and even generative AI applications. Advanced SQL shows data professionals how to move beyond conventional SELECT statements and tap into the full power of SQL as a programming interface for today's most advanced data platforms. Written by seasoned data experts Rui Pedro Machado, Hélder Russa, and Pedro Esmeriz, this practical guide explores the role of SQL in streaming architectures (like Apache Kafka and Flink), data lake ecosystems, cloud data warehouses, and ML pipelines. Geared toward data engineers, analysts, scientists, and analytics engineers, the book combines hands-on guidance with architectural best practices to help you extend your SQL skills into emerging workloads and real-world production systems. Use SQL to design and deploy modern, end-to-end data architectures Integrate SQL with data lakes, stream processing, and cloud platforms Apply SQL in feature engineering and ML model deployment Master pipe syntax and other advanced features for scalable, efficient queries Leverage SQL to build GenAI-ready data applications and pipelines

Dive into the world of data analytics with 'SQL for Data Analytics'. This book takes you beyond simple query writing to teach you how to use SQL to analyze, interpret, and derive actionable insights from real-world data. By the end, you'll build technical skills that allow you to solve complex problems and demonstrate results using data. What this Book will help me do Understand how to create, manage, and utilize structured databases for analytics. Use advanced SQL techniques such as window functions and subqueries effectively. Analyze various types of data like geospatial, JSON, and time-series data in SQL. Apply statistical principles within the context of SQL for enhanced insights. Automate data workflows and presentations using SQL and Python integration. Author(s) The authors Jun Shan, Haibin Li, Matt Goldwasser, Upom Malik, and Benjamin Johnston bring together a wealth of knowledge in data analytics, database management, and applied statistics. Together, they aim to empower readers through clear explanations, practical examples, and a focus on real-world applicability. Who is it for? This book is aimed at data professionals and learners such as aspiring data analysts, backend developers, and anyone involved in data-driven decision-making processes. The ideal reader has a basic understanding of SQL and mathematics and is eager to extend their skills to tackle real-world data challenges effectively.

Unveil the data platform of the future with SQL Server 2025—guided by one of its key architects . With built-in AI for application development and advanced analytics powered by Microsoft Fabric, SQL Server 2025 empowers you to innovate—securely and confidently. This book shows you how. Author Bob Ward, Principal Architect for the Microsoft Azure Data team, shares exclusive insights drawn from over three decades at Microsoft. Having worked on every version of SQL Server since OS/2 1.1, Ward brings unmatched expertise and practical guidance to help you navigate this transformative release. Ward covers everything from setup and upgrades to advanced features in performance, high availability, and security. He also highlights what makes this the most developer-friendly release in a decade: support for JSON, RegEx, REST APIs, and event streaming. Most critically, Ward explores SQL Server 2025’s advanced, scalable AI integrations, showing you how to build AI-powered applications deeply integrated with the SQL engine—and elevate your analytics to the next level. But innovation doesn’t come at the cost of safety: this release is built on a foundation of enterprise-grade security, helping you adopt AI safely and responsibly. You control which models to use, how they interact with your data, and where they run—from ground to cloud, or integrated with Microsoft Fabric. With built-in features like Row-Level Security (RLS), Transparent Data Encryption (TDE), Dynamic Data Masking, and SQL Server Auditing, your data remains protected at every layer. The AI age is here. Make sure your SQL Server databases are ready—and built for secure, scalable innovation . What You Will Learn [if !supportLists] · [endif]Grasp the fundamentals of AI to leverage AI with your data, using the industry-proven security and scale of SQL Server [if !supportLists] · [endif]Utilize AI models of your choice, services, and frameworks to build new AI applications [if !supportLists] · [endif]Explore new developer features such as JSON, Regular Expressions, REST API, and Change Event Streaming [if !supportLists] · [endif]Discover SQL Server 2025's powerful new engine capabilities to increase application concurrency [if !supportLists] · [endif]Examine new high availability features to enhance uptime and diagnose complex HADR configurations [if !supportLists] · Use new query processing capabilities to extend the performance of your application [if !supportLists] · [endif]Connect SQL Server to Azure with Arc for advanced management and security capabilities [if !supportLists] · [endif]Secure and govern your data using Microsoft Entra [if !supportLists] · [endif]Achieve near-real-time analytics with the unified data platform Microsoft Fabric [if !supportLists] · [endif]Integrate AI capabilities with SQL Server for enterprise AI [if !supportLists] · [endif]Leverage new tools such as SQL Server Management Studio and Copilot experiences to assist your SQL Server journey Who This Book Is For The SQL Server community, including DBAs, architects, and developers eager to stay ahead with the latest advancements in SQL Server 2025, and those interested in the intersection of AI and data, particularly how artificial intelligence (AI) can be seamlessly integrated with SQL Server to unlock deeper insights and smarter solutions

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

Develop a comprehensive understanding of data wrangling with SQL to transform raw data into actionable insights. This hands-on guide, 'Data Wrangling with SQL,' leads you through fundamentals to advanced techniques for cleaning, analyzing, and engineering data. By mastering these techniques, you'll improve your data analysis capabilities and solve real-world data challenges efficiently. What this Book will help me do Understand and implement data wrangling steps using SQL, including handling missing data and optimizing queries. Master advanced SQL features like subqueries, aggregate functions, and common table expressions for effective data transformations. Apply data cleaning techniques to ensure data consistency and prepare it for deeper analysis and reporting. Optimize the structure and performance of SQL queries to work seamlessly with large datasets and improve decision-making processes. Gain practical skills with hands-on examples and exercises to consolidate your SQL abilities for real-world applications. Author(s) Raghav Kandarpa and Shivangi Saxena are experienced professionals in data analytics and database management. Their combined expertise in teaching SQL and working on real-world data analysis projects makes them ideal mentors for learning practical data wrangling concepts. They emphasize simplicity and clarity in their approach, offering a practical learning experience. Who is it for? This book is designed for data analysts, data scientists, and professionals dealing with business insights who aim to enhance their SQL skills for data wrangling and transformation. It suits those with basic SQL knowledge looking to refine their grasp of data manipulation techniques. Beginners to intermediate-level practitioners in data analysis will find practical guidance here for real-world data challenges. Readers aspiring to use SQL effectively for database analysis and decision-making will benefit greatly.

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.

SQL for Data Analytics is an accessible guide to helping readers efficiently use SQL for data analytics tasks. You will learn the ins and outs of writing SQL queries, preparing datasets, and utilizing advanced features like geospatial data handling and window functions. Demystify the process of harnessing SQL to tackle analytical data challenges in a structured and hands-on way. What this Book will help me do Become proficient in preparing and managing datasets using SQL. Learn to write efficient SQL queries for summarizing and analyzing data. Master advanced SQL features, including window functions and JSON handling. Optimize SQL queries and automate analytical tasks for efficiency. Gain practical experience analyzing data with real-world scenarios. Author(s) The authors, Jun Shan, Matt Goldwasser, Upom Malik, and Benjamin Johnston, are experienced professionals in data analytics and database management. They bring a blend of technical expertise and practical insights to teaching SQL for analytics. Their collective knowledge ensures that the book caters to all levels, from foundational concepts to advanced techniques. Who is it for? This book is ideal for database engineers transitioning into analytics, backend engineers looking to deepen their understanding of production data, and data scientists or business analysts seeking to boost their SQL analytics skills. Readers should have a basic grasp of SQL and familiarity with statistics and linear algebra to fully benefit from the contents.

Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications. Beginning Apache Spark 3 begins by explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack. Next, it offers an overview of Spark SQL before moving on to its advanced features. It covers tips and techniques for dealing with performance issues, followed by an overview of the structured streaming processing engine. It concludes with a demonstration of how to develop machine learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section. After reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications. What You Will Learn Master the Spark unified data analytics engine and its various components Work in tandem to provide a scalable, fault tolerant and performant data processing engine Leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQL Develop machine learning applications using Spark MLlib Manage the machine learning development lifecycle using MLflow Who This Book Is For Data scientists, data engineers and software developers.

Jump-start your career as a data scientist—l earn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!

Introducing Microsoft SQL Server 2019 is the must-have guide for database professionals eager to leverage the latest advancements in SQL Server 2019. This book covers the features and capabilities that make SQL Server 2019 a powerful tool for managing and analyzing data both on-premises and in the cloud. What this Book will help me do Understand the new features introduced in SQL Server 2019 and their practical applications. Confidently manage and analyze relational, NoSQL, and big data within SQL Server 2019. Implement containerization for SQL Server using Docker and Kubernetes. Migrate and integrate your databases effectively to use Power BI Report Server. Query data from Hadoop Distributed File System with Azure Data Studio. Author(s) The authors of 'Introducing Microsoft SQL Server 2019' are subject matter experts including Kellyn Gorman, Allan Hirt, and others. With years of professional experience in database management and SQL Server, they bring a wealth of practical insight and knowledge to the book. Their experience spans roles as administrators, architects, and educators in the field. Who is it for? This book is aimed at database professionals such as DBAs, architects, and big data engineers who are currently using earlier versions of SQL Server or other database platforms. It is particularly well-suited for professionals aiming to understand and implement SQL Server 2019's new features. Readers should have basic familiarity with SQL Server and RDBMS concepts. If you're looking to explore SQL Server 2019 to improve data management and analytics in your organization, this book is for you.

SQL for Data Analytics provides readers with the tools and knowledge to use SQL effectively for extracting, analyzing, and interpreting complex datasets. Whether you're working with time-series data, geospatial data, or textual data, this book combines insightful explanations with practical guidance to enhance your data analysis capabilities. What this Book will help me do Perform advanced statistical calculations using SQL functions like WINDOW. Develop and optimize queries for better performance and faster results. Analyze and work with geospatial, time-series, and text datasets effectively. Debug problematic SQL queries and ensure their correctness. Create robust SQL pipelines and integrate them with other analytics tools. Author(s) The authors of SQL for Data Analytics, Upom Malik, Matt Goldwasser, and Benjamin Johnston, are seasoned professionals experienced in both the practical and theoretical aspects of SQL and data analysis. They bring their collective expertise to guide readers through the essentials and advanced usage of SQL in analytics. Who is it for? This book is aimed at database engineers aspiring to delve into analytics, backend developers wanting to improve their data handling skills, and data professionals aiming to enhance their SQL proficiency. A basic understanding of SQL and databases will help readers follow along and maximize their learning.

Now expanded and updated with modern best practices, this is the most complete guide to Microsoft's DAX language for business intelligence, data modeling, and analytics. Expert Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You'll learn exactly what happens under the hood when you run a DAX expression, and use this knowledge to write fast, robust code. This edition focuses on examples you can build and run with the free Power BI Desktop, and helps you make the most of the powerful syntax of variables (VAR) in Power BI, Excel, or Analysis Services. Want to leverage all of DAX's remarkable capabilities? This no-compromise "deep dive" is exactly what you need. Related Content Video: Introduction to Microsoft Power BI (Video), Data Analysis Fundamentals with Excel (Video) Perform powerful data analysis with DAX for Power BI, SQL Server, and Excel · Master core DAX concepts, including calculated columns, measures, and calculation groups · Work efficiently with basic and advanced table functions · Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions · Perform time-based calculations · Use calculation groups and calculation items · Use syntax of variables (VAR) to write more readable, maintainable code · Express diverse and unusual relationships with DAX, including many-to-many relationships and bidirectional filters · Master advanced optimization techniques, and improve performance in aggregations · Optimize data models to achieve better compression · Measure DAX query performance with DAX Studio and learn how to optimize your DAX

"Learning Spark SQL" takes you from data exploration to designing scalable applications with Apache Spark SQL. Through hands-on examples, you will comprehend real-world use cases and gain practical skills crucial for working with Spark SQL APIs, data frames, streaming data, and optimizing Spark applications. What this Book will help me do Understand the principles of Spark SQL and its APIs for building scalable distributed applications. Gain hands-on experience performing data wrangling and visualization using Spark SQL and real-world datasets. Learn how to design and optimize applications for performance and scalability with Spark SQL. Develop the skills to integrate Spark SQL with other frameworks like Apache Kafka for streaming analytics. Master the techniques required to architect machine learning and deep learning solutions using Spark SQL. Author(s) None Sarkar is an experienced technologist and trainer specializing in big data, streaming analytics, and scalable architectures using Apache Spark. With years of practical experience in implementing Spark solutions, Sarkar draws from real-world projects to provide readers with valuable insights. Sarkar's approachable and detailed writing style ensures readers grasp both the theory and the practice of Spark SQL. Who is it for? This book is ideal for software developers, data engineers, and architects aspiring to harness Apache Spark for robust, scalable applications. It suits readers with some SQL querying experience and a basic knowledge of programming in languages like Scala, Java, or Python. Whether you're a Spark newcomer or advancing your capabilities in scalable data processing, this resource will accelerate your learning journey.

Improve your ability to develop, manage, and troubleshoot SQL Server solutions by learning how different components work "under the hood," and how they communicate with each other. The detailed knowledge helps in implementing and maintaining high-throughput databases critical to your business and its customers. You'll learn how to identify the root cause of each problem and understand how different design and implementation decisions affect performance of your systems. New in this second edition is coverage of SQL Server 2016 Internals, including In-Memory OLTP, columnstore enhancements, Operational Analytics support, Query Store, JSON, temporal tables, stretch databases, security features, and other improvements in the new SQL Server version. The knowledge also can be applied to Microsoft Azure SQL Databases that share the same code with SQL Server 2016. Pro SQL Server Internals is a book for developers and database administrators, and it covers multiple SQL Server versions starting with SQL Server 2005 and going all the way up to the recently released SQL Server 2016. The book provides a solid road map for understanding the depth and power of the SQL Server database server and teaches how to get the most from the platform and keep your databases running at the level needed to support your business. The book: Provides detailed knowledge of new SQL Server 2016 features and enhancements Includes revamped coverage of columnstore indexes and In-Memory OLTP Covers indexing and transaction strategies Shows how various database objects and technologies are implemented internally, and when they should or should not be used Demonstrates how SQL Server executes queries and works with data and transaction log What You Will Learn Design and develop database solutions with SQL Server. Troubleshoot design, concurrency, and performance issues. Choose the right database objects and technologies for the job. Reduce costs and improve availability and manageability. Design disaster recovery and high-availability strategies. Improve performance of OLTP and data warehouse systems through in-memory OLTP and Columnstore indexes. Who This Book Is For Developers and database administrators who want to design, develop, and maintain systems in a way that gets the most from SQL Server. This book is an excellent choice for people who prefer to understand and fix the root cause of a problem rather than applying a 'band aid' to it.