Shortlisted for the Women’s Prize for Non-Fiction 2024AI is changing what it means to be human. This is the unrivalled investigation into the impact of AI on how we live now.'The intimate investigation of AI that we’ve been waiting for, and it arrives not a moment too soon.' – Shoshana Zuboff, author of The Age of Surveillance CapitalismThrough the voices of ordinary people in places far removed from Silicon Valley, Code Dependent explores the impact of a set of powerful, flawed, and often exploitative technologies on individuals, communities, and our wider society. Madhumita Murgia, AI Editor at the FT, exposes how AI can strip away our collective and individual sense of agency – and shatter our illusion of free will.AI is already changing what it means to be human, in ways large and small. In this compelling work, Murgia reveals what could happen if we fail to reclaim our humanity.'A nuanced, thoughtful and very accessible picture of a world deeply affected by AI' – Martha Lane Fox'Highly readable and deeply important' – The Guardian'Code Dependent is the No Logo of the 2020s' – The Times Read more

A practical guide to making good decisions in a world of missing dataIn the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see.Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don’t know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions.Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones. Read more

System Design Interview - An Insider's Guide (Volume 2)This book can be seen as a sequel to the book: System Design Interview - An Insider’s Guide. It covers a different set of system design interview questions and solutions. Although reading Volume 1 is helpful, it is not required. This book should be accessible to readers who have a basic understanding of distributed systems.This volume provides a reliable strategy and knowledge base for approaching a broad range of system design questions that you may encounter. It will help you feel confident during this important interview. This book provides a step-by-step framework for how to tackle a system design question. It also includes many real-world examples to illustrate a systematic approach, with detailed and well-explained steps you can follow.What’s inside?- An insider’s take on what interviewers really look for and why.- A 4-step framework for solving any system design interview question.- 13 real system design interview questions with detailed solutions.- 300+ diagrams to visually explain how different systems work.Table of ContentsChapter 1: Proximity ServiceChapter 2: Nearby FriendsChapter 3: Google MapsChapter 4: Distributed Message QueueChapter 5: Metrics MonitoringChapter 6: Ad Click Event AggregationChapter 7: Hotel ReservationChapter 8: Distributed Email ServiceChapter 9: S3-like Object StorageChapter 10: Real-time Gaming LeaderboardChapter 11: Payment SystemChapter 12: Digital WalletChapter 13: Stock Exchange Read more

Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projectsKey FeaturesBecome well-versed in data architectures, data preparation, and data optimization skills with the help of practical examplesDesign data models and learn how to extract, transform, and load (ETL) data using PythonSchedule, automate, and monitor complex data pipelines in productionBook DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python.The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines.By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learnUnderstand how data engineering supports data science workflowsDiscover how to extract data from files and databases and then clean, transform, and enrich itConfigure processors for handling different file formats as well as both relational and NoSQL databasesFind out how to implement a data pipeline and dashboard to visualize resultsUse staging and validation to check data before landing in the warehouseBuild real-time pipelines with staging areas that perform validation and handle failuresGet to grips with deploying pipelines in the production environmentWho this book is forThis book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.Table of ContentsWhat is Data Engineering?Building Our Data Engineering Infrastructure Reading and Writing FilesWorking with DatabasesCleaning, Transforming, and Enriching DataBuilding a 311 Data PipelineFeatures of a Production PipelineVersion Control Using the NiFi RegistryMonitoring and Logging PipelinesDeploying Your PipelinesBuilding a Production Data PipelineBuilding a Kafka ClusterStreaming Data with Apache KafkaData Processing with Apache SparkReal-Time Edge Data with MiNiFi, Kafka, and SparkAppendix Read more

Choose the right Azure data service and correct model design for successful implementation of your data model with the help of this hands-on guideKey FeaturesDesign a cost-effective, performant, and scalable database in AzureChoose and implement the most suitable design for a databaseDiscover how your database can scale with growing data volumes, concurrent users, and query complexityBook DescriptionData is at the heart of all applications and forms the foundation of modern data-driven businesses. With the multitude of data-related use cases and the availability of different data services, choosing the right service and implementing the right design becomes paramount to successful implementation.Data Modeling for Azure Data Services starts with an introduction to databases, entity analysis, and normalizing data. The book then shows you how to design a NoSQL database for optimal performance and scalability and covers how to provision and implement Azure SQL DB, Azure Cosmos DB, and Azure Synapse SQL Pool. As you progress through the chapters, you'll learn about data analytics, Azure Data Lake, and Azure SQL Data Warehouse and explore dimensional modeling, data vault modeling, along with designing and implementing a Data Lake using Azure Storage. You'll also learn how to implement ETL with Azure Data Factory.By the end of this book, you'll have a solid understanding of which Azure data services are the best fit for your model and how to implement the best design for your solution.What you will learnModel relational database using normalization, dimensional, or Data Vault modelingProvision and implement Azure SQL DB and Azure Synapse SQL PoolsDiscover how to model a Data Lake and implement it using Azure StorageModel a NoSQL database and provision and implement an Azure Cosmos DBUse Azure Data Factory to implement ETL/ELT processesCreate a star schema model using dimensional modelingWho this book is forThis book is for business intelligence developers and consultants who work on (modern) cloud data warehousing and design and implement databases. Beginner-level knowledge of cloud data management is expected.Table of ContentsIntroduction to DatabasesEntity AnalysisNormalizing DataProvisioning and Implementing an Azure SQL DBDesign a NoSQL DatabaseProvisioning and Implementing an Azure Cosmos DB DatabaseDimensional ModelingProvision and implement an Azure Synapse SQL PoolData Vault ModelingDesigning and Implementing a Data Lake Using Azure StorageImplementing ETL Using Azure Data Factory Read more

Discover how Snowflake's unique objects and features can be used to leverage universal modeling techniques through real-world examples and SQL recipesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn core modeling techniques tied to practical examples using native Snowflake architectureAdopt a universal modeling language to communicate business value to functional teamsGo beyond physical modeling with SQL recipes to transform and shape your Snowflake dataBook DescriptionThe Snowflake Data Cloud is one of the fastest-growing platforms for data warehousing and application workloads. Snowflake's scalable, cloud-native architecture and expansive set of features and objects enables you to deliver data solutions quicker than ever before.Yet, we must ensure that these solutions are developed using recommended design patterns and accompanied by documentation that’s easily accessible to everyone in the organization.This book will help you get familiar with simple and practical data modeling frameworks that accelerate agile design and evolve with the project from concept to code. These universal principles have helped guide database design for decades, and this book pairs them with unique Snowflake-native objects and examples like never before – giving you a two-for-one crash course in theory as well as direct application.By the end of this Snowflake book, you’ll have learned how to leverage Snowflake’s innovative features, such as time travel, zero-copy cloning, and change-data-capture, to create cost-effective, efficient designs through time-tested modeling principles that are easily digestible when coupled with real-world examples.What you will learnDiscover the time-saving benefits and applications of data modelingLearn about Snowflake’s cloud-native architecture and its featuresUnderstand and apply modeling techniques using Snowflake objectsUniversal modeling concepts and language through Snowflake objectsGet comfortable reading and transforming semistructured dataLearn directly with pre-built recipes and examplesLearn to apply modeling frameworks from Star to Data VaultWho this book is forThis book is for developers working with SQL who are looking to build a strong foundation in modeling best practices and gain an understanding of where they can be effectively applied to save time and effort. Whether you’re an ace in SQL logic or starting out in database design, this book will equip you with the practical foundations of data modeling to guide you on your data journey with Snowflake. Developers who’ve recently discovered Snowflake will be able to uncover its core features and learn to incorporate them into universal modeling frameworks.Table of ContentsUnlocking the Power of ModelingAn Introduction to the Four Modeling TypesMastering Snowflake's Architecture Mastering Snowflake ObjectsSpeaking Modeling through Snowflake ObjectsSeeing Snowflake's Architecture through Modeling NotationPutting Conceptual Modeling into PracticePutting Logical Modeling into PracticeDatabase NormalizationDatabase Naming and StructurePutting Physical Modeling into PracticePutting Transformational Modeling into PracticeModeling Slowly Changing DimensionsModeling Facts for Rapid AnalysisModeling Semi-Structured DataModeling HierarchiesScaling Data Models through Modern TechniquesAppendix Read more

You don't have to go back to school in order to get ahead in today’s world…Do you have a burning desire to expand your skillset but don't have the time or care to go back to studying for the next 4+ years?Do you feel as if you are capable of so much more, and that you should be making a bigger contribution to the world?Are you ready to learn one of the most in-demand skills of the 21st century and set yourself up for outstanding success in your career -- success that will not only benefit you, but thousands, perhaps millions, of other people as well?Or, maybe you’ve already landed your dream job and now your boss needs you to fulfill the role as quickly as possible.Whatever the case may be, learning the ins and outs of the coding universe doesn't have to be some kind of big and complex ordeal.The internet might be abuzz with all kinds of confusing tutorials and partial playbooks making it seem like learning to code is harder than it really is, but rest assured, this is not true.Did you know that the average individual spends $20,000 on a course that is sometimes up to 24 weeks long just to learn the basics of coding?But this doesn't have to be you. No matter where you are in the coding journey, you can take the information provided and begin to apply it today.You can learn to code in the time it takes to read a book and skip all of the unnecessary schoolings, even if you’ve never coded anything before.In Learn SQL Quickly, you’ll discover:Simple, clear, to-the-point examples that will help you to fully understand everything you need to know about SQL coding An expert breakdown of exactly what SQL is, with explanations so perfect that anyone can walk away a master of the concept The most effective ways to set up your database management system, and why taking it slow in the beginning is the best way to go How to control the movement of your data in the most efficient way possible, saving you both time and potential headaches How to optimize the flow of any program, bringing you effortless success Why integrating real-life examples is a great way to learn, and how understanding these will set you apart from the competition Why working with MySQL is a better option for you than the alternatives, and how to get started today … and so much more!We are living in the age of information and there’s no denying it -- those who are best at organizing, understanding, and then using data to their advantage will be the ones on top.The skills themselves are simple and easy to learn and will give you a tremendous advantage in the highly competitive marketplace of today’s workforce.The only thing left is for you to give a little commitment and launch yourself into a whole new reality of success.If you’re ready to dive into SQL and get things done in the most efficient manner possible, then scroll up and click the “Add to Cart” button right now. Read more

R for Data Analysis: A Beginner's Guide to Statistical Modeling and VisualizationUnlock the power of R for data analysis with R for Data Analysis: A Beginner's Guide to Statistical Modeling and Visualization. This comprehensive guide is designed for beginners and aspiring data scientists who want to master data analysis with R, one of the most powerful and widely used programming languages for statistical computing and visualization.Whether you're a student, researcher, or professional, this book will equip you with essential skills to clean, manipulate, visualize, and model data effectively. With a hands-on approach, you’ll learn how to transform raw datasets into meaningful insights using R programming for data science.What You’ll Learn:✅ Set up your R environment with RStudio and essential packages like Tidyverse.✅ Master data manipulation with dplyr, tidyr, and data.table.✅ Visualize data beautifully using ggplot2, plotly, and advanced charting techniques.✅ Perform Exploratory Data Analysis (EDA) to uncover trends and patterns.✅ Build statistical models, including linear regression, hypothesis testing, and time series forecasting.✅ Work with large datasets efficiently and automate data workflows.✅ Integrate R with SQL for database-driven analysis.✅ Explore real-world case studies in finance, healthcare, marketing, and social media.Why Choose This Book?✔ Beginner-friendly with step-by-step tutorials.✔ Practical examples & hands-on exercises to reinforce learning.✔ Real-world applications for business and research.✔ Covers automation, web scraping, and advanced R techniques.By the end of this book, you’ll have the confidence to analyze complex datasets, create compelling visualizations, and apply statistical modeling techniques in real-world scenarios. Whether you're working on data analytics with R for business, research, or personal projects, this guide is your ultimate companion to R programming for data analysis.Start your journey today and master data analysis with R! 🚀 Read more

In today’s data-driven world, the products we use every day are increasingly powered by data, not just traditional code and features. Data Product Thinking: Crafting Intelligent Products in a Data-Driven World offers a comprehensive guide to designing, building, and scaling products that leverage the power of data to create personalized, impactful user experiences.This book introduces the concept of Data Product Thinking—a methodology that blends product development with advanced data science techniques. It explores the key components necessary for building data products, from developing a strong data strategy to integrating machine learning and AI. Readers will learn how to build dynamic products that continuously learn and improve over time by embedding data-driven insights into every aspect of the product lifecycle.With practical frameworks, actionable strategies, and real-world case studies from industry leaders like Netflix, Spotify, and Google, this book provides insights on creating products that evolve with user behavior and feedback. It also covers the critical role of cross-functional collaboration, data governance, and ethical considerations in crafting data products that users trust and value.Whether you're a product manager, data scientist, designer, or engineer, Data Product Thinking will equip you with the tools and mindset to develop smarter, more adaptive products in a world where data is the key to innovation. Explore how you can harness the full potential of data to create products that are not just functional but intelligent, and that grow more valuable with each interaction. Read more

As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. Data governance incorporates the ways people, processes, and technology work together to ensure data is trustworthy and can be used effectively. This practical guide shows you how to effectively implement and scale data governance throughout your organization.Chief information, data, and security officers and their teams will learn strategy and tooling to support democratizing data and unlocking its value while enforcing security, privacy, and other governance standards. Through good data governance, you can inspire customer trust, enable your organization to identify business efficiencies, generate more competitive offerings, and improve customer experience. This book shows you how.You'll learn:Data governance strategies addressing people, processes, and toolsBenefits and challenges of a cloud-based data governance approachHow data governance is conducted from ingest to preparation and useHow to handle the ongoing improvement of data qualityChallenges and techniques in governing streaming dataData protection for authentication, security, backup, and monitoringHow to build a data culture in your organization Read more

Everything you need to know to apply data contracts and build a truly data-driven organization that harnesses quality data to deliver tangible business valuePurchase of the print or Kindle book includes a free PDF eBookKey FeaturesUnderstand data contracts and their power to resolving the problems in contemporary data platformsLearn how to design and implement a cutting-edge data platform powered by data contractsAccess practical guidance from the pioneer of data contracts to get expert insights on effective utilizationBook DescriptionDespite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value.With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products.By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.What you will learnGain insights into the intricacies and shortcomings of today's data architecturesUnderstand exactly how data contracts can solve prevalent data challengesDrive a fundamental transformation of your data culture by implementing data contractsDiscover what goes into a data contract and why it's importantDesign a modern data architecture that leverages the power of data contractsExplore sample implementations to get practical knowledge of using data contractsEmbrace best practices for the successful deployment of data contractsWho this book is forIf you’re a data engineer, data leader, architect, or practitioner thinking about your data architecture and looking to design one that enables your organization to get the most value from your data, this book is for you. Additionally, staff engineers, product managers, and software engineering leaders and executives will also find valuable insights.Table of ContentsA Brief History of Data PlatformsIntroducing Data ContractsHow to Get Adoption in Your OrganizationBringing Data Consumers and Generators Closer TogetherEmbedding Data GovernanceWhat Makes Up a Data ContractA Contract-Driven Data ArchitectureA Sample ImplementationImplementing Data Contracts In Your OrganizationData Contracts in Practice Read more

Ace The SQL Interview 200 Most Asked SQL Questions in Top Product Based Companies.This book ONLY contains advanced level SQL questions and is very helpful if you want to expand your knowledge beyond the basic queries. These are the questions that are being asked to check your understanding of SQL. Following are the topics and number of questions completely solved in the book:Topic No. of QuestionsSQL Most Asked Query Interview Questions 34Basic SQL Questions Asked in Analytics Interview 11JSON Interview Questions 19Date and Time Interview Questions in SQL 10Most Asked SQL Interview Questions 10SQL Query Optimization Interview Questions 10SQL System Design Interview Questions 10Transactions and Concurrency Interview Questions 10Miscellaneous SQL Interview Questions 10SQL Hard Interview Questions 12Advanced SQL Aggregation Interview Questions 10Advanced Error Handling & Debugging interview questions 10Stored Procedure and CTE Interview Questions 12Advance Joins in SQL Interview Questions 10Complex Data Handling Interview Questions 10Data Modeling Interview Questions 13 Read more

A thorough update to the industry standard for designing, developing, and deploying data warehouse and business intelligence systemsThe world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. In that time, the data warehouse industry has reached full maturity and acceptance, hardware and software have made staggering advances, and the techniques promoted in the premiere edition of this book have been adopted by nearly all data warehouse vendors and practitioners. In addition, the term "business intelligence" emerged to reflect the mission of the data warehouse: wrangling the data out of source systems, cleaning it, and delivering it to add value to the business.Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. The authors understand first-hand that a data warehousing/business intelligence (DW/BI) system needs to change as fast as its surrounding organization evolves. To that end, they walk you through the detailed steps of designing, developing, and deploying a DW/BI system. You'll learn to create adaptable systems that deliver data and analyses to business users so they can make better business decisions. Read more

Is your business truly ready for the AI revolution? Discover how to unlock the full potential of artificial intelligence and future-proof your organization with a winning AI strategy.AI is transforming every industry - is your business keeping up? In this essential guide, bestselling author and futurist Bernard Marr provides a comprehensive playbook for leaders looking to harness the power of AI. From understanding AI's transformative impact to developing a clear, actionable strategy, this book equips leaders with the tools they need to drive innovation, manage risks, and stay ahead in an AI-driven world.AI Strategy is the definitive guide for leaders ready to unlock AI's potential at scale. Covering every aspect of AI adoption - from ethical considerations and data management to employee upskilling and tech infrastructure - this book delivers real-world examples across multiple organizations and industries, from energy, healthcare and education to marketing and HR. Whether you're just starting or scaling fast, this must-read guide will help you confidently build and execute a winning AI strategy. Read more

This book is a comprehensive guide to MongoDB for application developers. The book begins by explaining what makes MongoDB unique and describing it's ideal use cases.A series of chapters designed for MongoDB mastery then leads into detailed examples for leveraging MongoDB in e-commerce, social networking, analytics, and other common applications. Numerous examples will help you develop confidence in the crucial area of data modeling.This is well-organized book which provides proper explaination you'll need as a student and enough detail to satisfy a developer. Several examples will help you develop confidence in the crucial area of data modeling. You'll also love the deep explanations of each feature, including replication, auto-sharding, and deployment.This book is very compact with less than 100 pages. But it is also incredibly detailed and wastes no time diving right into the action and ease of use......What's inside● NoSQL, Architecture of MongoDB● Standard DB operations, Indexes, Queries● Map-Reduce for custom aggregations and reporting● Java, Python and PHP Connectivity● Schema design patterns Read more

As a forward-thinking business leader, do you know how to make best use of data and AI, to improve your business and act strategically and decisively?This fully updated second edition of Data Means Business is a comprehensive and pragmatic guide for leaders that sets out a proven approach for developing the mindset and strategies required to generate value from data and AI. Jargon-free and packed with the latest practical advice, examples and real stories, Data Means Business will help you start or scale successfully and deliver positive impact.Read this book to:Understand the impact and value delivered by data and AIDefine a data, AI and technology strategy that is aligned to your businessBuild robust foundations for data and AICreate a culture of using data and AI to drive decision-makingHave an approach that focuses on scaling results Read more

Data projects are an intrinsic part of an organization’s technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.Throughout this journey, you’ll use open source data tools and public cloud services to apply each pattern. You'll learn:Challenges data engineers face and their impact on data systemsHow these challenges relate to data system componentsUseful applications of data engineering patternsHow to identify and fix issues with your current data componentsTTechnology-agnostic solutions to new and existing data projects, with open source implementation examplesBartosz Konieczny is a freelance data engineer who's been coding since 2010. He's held various senior hands-on positions that allowed him to work on many data engineering problems in batch and stream processing. Read more

Unlock the Power of SQL with "Learn SQL in 24 Hours: The Complete Beginner’s Guide"Your ultimate roadmap to mastering SQL and navigating databases like a pro! Designed for beginners and professionals, this comprehensive SQL book blends simplicity and practicality to teach you essential SQL concepts, queries, and real-world applications. Whether new to databases or looking to enhance your SQL for data analysis skills, this SQL crash course is tailored to help you succeed.Why Learn SQL?SQL (Structured Query Language) is the backbone of data management and an essential tool for web development, SQL for data analytics, and business intelligence. It’s one of the most sought-after skills in tech. With this SQL book, you’ll gain hands-on experience and confidence in working with relational databases efficiently, making it an indispensable resource among SQL programming books.What’s Inside?This learning SQL guide is organized into 24 hours of structured lessons, filled with practical exercises, real-world examples, and SQL for data analytics projects:Hour 1-2: Introduction to SQL, setting up your database environment, and understanding relational databases.Hour 3-4: SQL syntax essentials – writing your first SQL queries and mastering SQL statements (SELECT, INSERT, UPDATE, DELETE).Hour 5-8: Understanding data types, constraints, keys, and normalization for efficient database design.Hour 9-12: Advanced querying – working with JOINs, subqueries, UNIONs, set operations, and nested queries.Hour 13-16: Data aggregation, sorting, filtering, and using SQL functions (SUM, AVG, COUNT, MAX, MIN).Hour 17-20: Deep dive into SQL Server – stored procedures, views, transactions, triggers, and error handling.Hour 21-24: Practical projects – designing databases, implementing CRUD operations, troubleshooting common issues, and optimizing performance using indexing and query plans.Who Should Read This SQL Book?This SQL cookbook-style guide is perfect for:Beginners seeking a clear and easy introduction to learning SQL.Developers and analysts looking for a concise SQL for data analytics resources.Professionals aiming to enhance their SQL for data analysis and reporting skills.What You’ll Learn:Simple and effective installation and setup of SQL environments.Core SQL concepts, including SELECT statements, JOIN operations, and WHERE clauses.Writing complex queries using subqueries, SQL functions, and aggregate functions.How to design well-structured databases and enforce data integrity.Best practices for SQL Server management, stored procedures, views, and triggers.Real-world SQL for data analytics projects, demonstrating SQL’s role in data manipulation, reporting, and web applications.Advanced SQL for data analysis skills, including query optimization and performance troubleshooting.Why This SQL Book Stands OutWith its hands-on approach, step-by-step instructions, and practical SQL cookbook examples, Learn SQL in 24 Hours is your all-in-one solution to becoming proficient in SQL quickly. Unlike other SQL programming books, this guide ensures that you build a strong foundation for working on SQL for data analysis, SQL for data analytics, and data-driven projects.Start Your SQL Learning Journey Today!Whether a student, developer or working professional, this SQL book empowers you to stand out in today’s data-driven world. Unlock the full potential of databases with Learn SQL in 24 Hours: The Complete Beginner’s Guide and master the language that powers the world’s data! Read more

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 analysisPerform time series analysis using SQL's date and time manipulationsUse cohort analysis to investigate how groups change over timeUse SQL's powerful functions and operators for text analysisDetect outliers in your data and replace them with alternate valuesEstablish causality using experiment analysis, also known as A/B testing Read more

You want the rigor of good data architecture at the speed of agile? Then this is the missing link - your step-by-step guide to Data Vault success. Success with a Data Vault starts with the business and ends with the business. Sure, there's some technical stuff in the middle, and it is absolutely essential - but it's not sufficient on its own. This book will help you shape the business perspective, and weave it into the more technical aspects of Data Vault modeling. You can read the foundational books and go on courses, but one massive risk still remains. Dan Linstedt, the founder of the Data Vault, very clearly directs those building a Data Vault to base its design on an "enterprise ontology". And Hans Hultgren similarly stresses the importance of the business concepts model. So it's important. We get that. But: What on earth is an enterprise ontology/business concept model, 'cause I won't know if I've got one if I don't know what I'm looking for? If I can't find one, how do I get my hands on such a thing? Even if I have one of these wonderful things, how do I apply it to get the sort of Data Vault that's recommended? It's actually not as hard as some would fear to answer all of these questions, and it's certainly worth the effort. This book just might save you a world of pain. It's a supplement to other material on Data Vault modeling, but it's the vital missing link to finding simplicity for Data Vault success. "Data Warehousing in the context of large healthcare organizations is notoriously difficult - largely in part due to challenges around Health Information Modeling. The Elephant in the Fridge provides clear and rational perspectives that have allowed my team to breakthrough some of our circular debates and 'too hard basket' challenges. Success in this space will require technical and health professionals to co-design solutions around a shared information model. This book is written in a way that they can both consume." Benson Choy, Enterprise Information Architect, eHealth Queensland. "John has a wonderful way of explaining complicated topics in an uncomplicated way. If you've heard about taxonomies and are wondering how to apply these to your Data Warehouse, then this is the book for you. Data modelling is about the business, and John explains how this can be achieved by using proven business templates which help to quickly and efficiently define a solid data model. Don't re-invent the wheel, but start with the model patterns that are already available! This is a highly relevant book, because it helps to match the information delivery to business expectations - by correctly applying taxonomies and (business) model archetypes in a clear and simple way." Roelant Vos, General Manager - Enterprise Data Management "It has been a delight and privilege working with John on a Data Vault project. I have enjoyed his practical and pragmatic approach and readiness to share his wealth of knowledge. This is an excellent book written based on real life experience. It provides valuable and timely insights into Data Vault modeling and delivery, and can be easily understood by those who are new to the Data Vault. The book contains practical advice and sample patterns to help people get started on a Data Vault project and avoid costly mistakes." Natalia Bulashenko, Information Solution Architect "This book covers off one of the key aspects of Data Vault - the Modelling. Far too many DV practitioners come from a technical database background and lack the fundamental Data Modelling skills necessary to identify the "Key Business Concepts" that are core to successful DV projects. " Peter Dudley, Consultant, Interactive Innovations Read more