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This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs.

Gain MongoDB expertise and discover advanced queries and Atlas insights with this ultimate guide to version 7.0Key FeaturesEnhance your proficiency in advanced queries, aggregation, and optimized indexing to achieve peak MongoDB performanceMonitor, back up, and integrate applications effortlessly with MongoDB AtlasImplement security thorough RBAC, auditing, and encryption to ensure comprehensive data protection and privacyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMastering MongoDB 7.0 explores the latest version of MongoDB, an exceptional NoSQL database solution that aligns with the needs of modern web applications. This book starts with an informative overview of MongoDB's architecture and developer tools, guiding you through the process of connecting to databases seamlessly. This MongoDB book explores advanced queries in detail, including aggregation pipelines and multi-document ACID transactions. It delves into the capabilities of the MongoDB Atlas developer data platform and the latest features, such as Atlas Vector Search, and their role in AI applications, enabling developers to build applications with the scalability and performance that today's organizations need. It also covers the creation of resilient search functionality using MongoDB Atlas Search. Mastering MongoDB 7.0's deep coverage of advanced techniques encompasses everything from role-based access control (RBAC) to user management, auditing practices, and encryption across data, network, and storage layers. By the end of this book, you'll have developed the skills necessary to create efficient, secure, and high-performing applications using MongoDB. You'll have the confidence to undertake complex queries, integrate robust applications, and ensure data security to overcome modern data challenges.What you will learnExecute advanced MongoDB queries for intricate data insightsHarness the power of aggregation pipelines to transform dataEnsure data integrity with multi-document ACID transactionsOptimize query performance using strategic indexing techniquesNavigate MongoDB Atlas seamlessly for monitoring and backupsEnable robust search functionality with Atlas SearchMaster RBAC, user management, and data encryption for securityImplement auditing practices for transparency and accountabilityWho this book is forThis book is for developers with an intermediate skill level who aim to become MongoDB experts. Working knowledge of MongoDB is recommended to gain the most from this guide. Ideal for database administrators, app developers, and software engineers, this book will assist you in developing advanced skills to conquer intricate data tasks.

Although AI has incredible potential, it has three weak links: 1. Blackbox, lack of explainability2. Silos, slews of siloed systems across the AI ecosystem3. Low-performance, most of ML/DL based AI systems are SLOW.Fixing these problems will pave the road to strong and effective AI. Graph databases, particularly high-performance graph database or graph computing, should allow this to happen.The Essential Criteria of Graph Databases simply broadens the horizon of graph applications. The book collects several truly innovative graph applications in asset-liability and liquidity risk management, which hopefully will spark readers'interest in further broaden the reach and applicable domains of graph systems. - Presents updates on the essential criteria of graph database(s) and how they are quite different from traditional relational database or other types of NoSQL DBMS or any of those big-data frameworks (i.e., Hadoop, Spark, etc.) - Clearly points out the key criteria that readers should pay attention to - Teaches users how to avoid common mistakes and how to get hands-on with system architecture design, benchmarking or selection of an appropriate graph platform/vendor-system

The most thorough SQL reference, now updated for SQL:2023 SQL All-in-One For Dummies has everything you need to get started with the SQL programming language, and then to level up your skill with advanced applications. This relational database coding language is one of the most used languages in professional software development. And, as it becomes ever more important to take control of data, there's no end in sight to the need for SQL know-how. You can take your career to the next level with this guide to creating databases, accessing and editing data, protecting data from corruption, and integrating SQL with other languages in a programming environment. Become a SQL guru and turn the page on the next chapter of your coding career. Get 7 mini-books in one, covering basic SQL, database development, and advanced SQL concepts Read clear explanations of SQL code and learn to write complex queries Discover how to apply SQL in real-world situations to gain control over large datasets Enjoy a thorough reference to common tasks and issues in SQL developmentThis Dummies All-in-One guide is for all SQL users—from beginners to more experienced programmers. Find the info and the examples you need to reach the next stage in your SQL journey.

Derzeit wird kaum noch ernsthaft bestritten, dass die Daten eines Unternehmens ein wichtiges Wirtschaftsgut darstellen und in erheblicher Weise zum Erfolg beitragen können. Allerdings gilt es, nicht nur den Wert der Daten zu erkennen, sondern diese auch in den zugehörigen Geschäftsprozessen gewinnbringend einzusetzen. Als zwingende Voraussetzung erweist sich dabei, eine organisatorische und technische Basis zu etablieren, die nachhaltig darauf ausgerichtet ist, die fachlichen Ausgabenstellungen bestmöglich zu unterstützen. Unter dem Oberbegriff Datenmanagement werden dazu diejenigen Führungs- und Durchführungstätigkeiten diskutiert, die einen tragfähigen Rahmen für den Umgang mit Daten in der Organisation aufspannen. Daneben erweisen sich vor allem die Verfahren zur Auswertung und Analyse der verfügbaren Datenbestände als leistungsfähige Instrumente, um langfristige Wettbewerbsvorteile zu erlangen. Vor diesem Hintergrund widmet sich das vorliegende Buch den stetig an Bedeutung gewinnenden Themenfeldern Datenmanagement und Datenanalyse, denen insbesondere im Rahmen der digitalen Transformation eine große Bedeutung zukommt.

This book provides an overview of both experimental and commercial real-time database systems (RTDBs) and a systematic approach to understanding, designing, and implementing them. To this end, the book is composed of four chapters: Chapter 1 “An Overview of Real-Time Database Systems” delves into the realm of RTDBs and discusses the specific requirements, transaction models, and scheduling algorithms that set RTDBs apart from conventional DBMs. Chapter 2 on “Experimental Real-Time Databases” presents various experimental RTDBs developed in academia with their architectures, features, and implementations, while chapter 3 on “Commercial Real-Time Databases” does so for systems developed and offered by commercial vendors as products or services. Eventually, chapter 4 on “Applications of Real-Time Database Systems” showcases various applications of RTDBs across different domains. This book will help researchers, graduate students and advanced professionals to get anoverview of the area and to understand the main challenges and systems available.

Welcome to Microsoft Access. This book is intended for all those who want to increase their knowledge about the database management and its structure. You will study the tables, the fields, the primary and foreign keys, the queries, the underlying SQL language, the forms, the macros and the Visual Basic language that can help you to develop applications adapted to your needs. The strong point of Access is its ability to link tables together, thanks to the relational model. Once the tables are joined, you can display information with the queries that bring a great liberty for extracting multiple data. I hope this book will improve your skills in order to manipulate any data.Enjoy your reading.ABOUT THE AUTHORRémy Lentzner has been an IT trainer since 1985. Specialized in mastering office automation tools, he supports companies in the professional training of their employees. Self-taught, he has several computer books to his credit.

Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learning Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesExtract meaningful information from graph data with Neo4j's latest version 5Use Graph Algorithms into a regular Machine Learning pipeline in PythonLearn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.Book DescriptionNeo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You'll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you'll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you'll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you'll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.What you will learnUse the Cypher query language to query graph databases such as Neo4jBuild graph datasets from your own data and public knowledge graphsMake graph-specific predictions such as link predictionExplore the latest version of Neo4j to build a graph data science pipelineRun a scikit-learn prediction algorithm with graph dataTrain a predictive embedding algorithm in GDS and manage the model storeWho this book is forIf you're a data scientist or data professional with a foundation in the basics of Neo4j and are now ready to understand how to build advanced analytics solutions, you'll find this graph data science book useful. Familiarity with the major components of a data science project in Python and Neo4j is necessary to follow the concepts covered in this book.

Database Concepts is your hands-on guide to creating and managing small databases. This flexible text from 2 of the world's leading database authorities focuses on database concepts (rather than the features and functions of a particular product). As a result, it can be used with whichever software suits your needs. You'll practice working with 3 complete sample databases that run throughout key portions of the text. You'll also apply concepts and techniques to real business situations as you work through 3 ongoing projects. The 10th Edition emphasizes the cloud's essential role in database concepts, beginning in Ch. 1 with a thorough introduction to the cloud environment and its underlying technology. The text has been updated to reflect Microsoft 365, with Access for Microsoft 365 (shown running on Microsoft Windows 11) as the basic software used. Screenshots and examples feature the latest possible releases of Microsoft 365 (Office 2021), MySQL, Microsoft Azure and ArangoDB.

This book discusses in detail a series of examples drawn from scholarly projects that use the OCHRE database platform (Online Cultural and Historical Research Environment). These case studies illustrate the wide range of data that can be managed with this platform and the wide variety of problems solved by OCHRE's item-based graph data model. The unique features and design principles of the OCHRE platform are explained and justified, helping readers to imagine how the system could be used for their own data.Data generated by studies in the humanities and social sciences is often semi-structured, fragmented, highly variable, and subject to many interpretations, making it difficult to represent adequately in a conventional database. The authors examine commonly used methods of data management in the humanities and offer a compelling argument for a different approach that takes advantage of powerful computational techniques for organizing scholarly information.This book is achallenge to scholars in the humanities and social sciences, asking them to expect more from technology as they pursue their research goals. Written jointly by a software engineer and a research scholar, each with many years of experience in applying database methods to diverse kinds of scholarly data, it shows how scholars can make the most of their existing data while going beyond the limitations of commonly used software tools to represent their objects of study in a more accurate, nuanced, and flexible way.

Discover critical considerations and best practices for improving database performance based on what has worked, and failed, across thousands of teams and use cases in the field. This open access book provides practical guidance for understanding the database-related opportunities, trade-offs, and traps you might encounter while trying to optimize data-intensive applications for high throughput and low latency. Whether you are building a new system from the ground up or trying to optimize an existing use case for increased demand, this book covers the essentials.The book begins with a look at the many factors impacting database performance at the extreme scale that today's game changing applications face—or at least hope to achieve. You'll gain insight into the performance impact of both technical and business requirements, and how those should influence your decisions around database infrastructure and topology. The authors share an inside perspective on often-overlooked engineering details that could be constraining—or helping—your team's database performance. The book also covers benchmarking and monitoring practices by which to measure and validate the outcomes from the decisions that you make.The ultimate goal of the book is to help you discover new ways to optimize database performance for your team's specific use cases, requirements, and expectations.What You Will LearnUnderstand often overlooked factors that impact database performance at scaleRecognize data-related performance and scalability challenges associated with your project Select a database architecture that's suited to your workloads, use cases, and requirementsAvoid common mistakes that could impede your long-term agility and growthJumpstart teamwide adoption of best practices for optimizing database performance at scaleWho ThisBook Is ForIndividuals and teams looking to optimize distributed database performance for an existing project or to begin a new performance-sensitive project with a solid and scalable foundation. This will likely include software architects, database architects, and senior software engineers who are either experiencing or anticipating pain related to database latency and/or throughput.

Learn to write SQL queries to select and analyze data, and improve your ability to manipulate data. This book will help you take your existing skills to the next level. Author Mark Simon kicks things off with a quick review of basic SQL knowledge, followed by a demonstration of how efficient SQL databases are designed and how to extract just the right data from them. You'll then learn about each individual table's structure and how to work with the relationships between tables. As you progress through the book, you will learn more sophisticated techniques such as using common table expressions and subqueries, analyzing your data using aggregate and windowing functions, and how to save queries in the form of views and other methods. This book employs an accessible approach to work through a realistic sample, enabling you to learn concepts as they arise to improve parts of the database or to work with the data itself. After completing this book, you will have a more thorough understanding of database structure and how to use advanced techniques to extract, manage, and analyze data. What Will You Learn Gain a stronger understanding of database design principles, especially individual tablesUnderstand the relationships between tablesUtilize techniques such as views, subqueries, common table expressions, and windowing functions Who Is This Book For: SQL Databases users who want to improve their knowledge and techniques.

SQL (Structured Query Language) is a query language for relational database management. In this book, you will discover the basics of its organization and its characteristics. Using the PhpMyAdmin environment, you will learn how to create a database, tables, columns, constraints, keys (primary and foreign), joins and many other specificities. SQL has a small set of statements and enables you to select records from one or more tables using the universal SELECT command. Other expressions like UPDATE, INSERT or DELETE will help you to update, insert or delete records. You will also study several functions that manipulate text, dates and numbers.Thanks to the exercises in this book, you will understand how a small set of statements can perform accurate actions and enhance your skills.Enjoy your reading!ABOUT THE AUTHORRémy Lentzner has been an IT trainer since 1985. Specialized in mastering office automation tools, he supports companies in the professional training of their employees. Self-taught, he has several computer books to his credit.

Master Java persistence using the industry-leading tools Spring Data and Hibernate.In Java Persistence with Spring Data and Hibernate you will learn: Mapping persistent classes, value types, and inheritance Mapping collections and entity associations Processing transactions with Spring Data and Hibernate Creating fetch plans, strategies, and profiles Filtering data Building Spring Data REST projects Using Java persistence with non-relational databases Querying JPA with QueryDSL Testing Java persistence applications Java Persistence with Spring Data and Hibernate teaches you the ins-and-outs of Java persistence with hands-on examples using Spring Data, JPA, and Hibernate. The book carefully analyzes the capabilities of the major Java persistence tools, and guides you through the most common use cases. By comparing and contrasting the alternatives, you'll find it easy to choose the right tool choice for your applications. You'll learn how to make and utilize mapping strategies, about the different approach to transactions for both Hibernate and Spring Data, and even how to efficiently test Java persistence applications. The practical techniques are demonstrated with both relational and non-relational databases. Forewords by Dmitry Aleksandrov and Mohamed Taman. About the technology Effectively managing application data is essential for any serious application. Spring Data and Hibernate bridge the gap between object-oriented code and relational data stores, radically simplifying Java persistence. By implementing the Java Persistence API (JPA) standard, these powerful tools help you avoid common bugs related to state and application data storage. About the book Java Persistence with Spring Data and Hibernate explores Java persistence using industry-standard tools. Hands-on examples introduce object-relational mapping and guide you through different mapping strategies to suit your needs. Covering transactions, persistent application testing, and non-relational databases, this book is your go-to resource for managing data in Java applications. What's inside Mapping persistent classes, value types, and inheritance Creating fetch plans, strategies, and profiles Building Spring Data REST projects Querying JPA with QueryDSL About the reader For intermediate Java programmers. About the author Catalin Tudose has more than 20 years of experience in the Java community. Christian Bauer, Gavin King, and Gary Gregory are the authors of Java Persistence with Hibernate, Second Edition, on which this book is based. Table of Contents PART 1 - GETTING STARTED WITH ORM 1 Understanding object/relational persistence 2 Starting a project 3 Domain models and metadata 4 Working with Spring Data JPA PART 2 - MAPPING STRATEGIES 5 Mapping persistent classes 6 Mapping value types 7 Mapping inheritance 8 Mapping collections and entity associations 9 Advanced entity association mappings PART 3 - TRANSACTIONAL DATA PROCESSING 10 Managing data 11 Transactions and concurrency 12 Fetch plans, strategies, and profiles 13 Filtering data PART 4 - BUILDING JAVA PERSISTENCE APPLICATIONS WITH SPRING 14 Integrating JPA and Hibernate with Spring 15 Working with Spring Data JDBC 16 Working with Spring Data REST PART 5 - BUILDING JAVA PERSISTENCE APPLICATIONS WITH SPRING 17 Working with Spring Data MongoDB 18 Working with Hibernate OGM PART 6 - WRITING QUERIES AND TESTING JAVA PERSISTENCE APPLICATIONS 19 Querying JPA with Querydsl 20 Testing Java persistence applications

This textbook offers a comprehensive introduction to relational (SQL) and non-relational (NoSQL) databases. The authors thoroughly review the current state of database tools and techniques and examine upcoming innovations.In the first five chapters, the authors analyze in detail the management, modeling, languages, security, and architecture of relational databases, graph databases, and document databases. Moreover, an overview of other SQL- and NoSQL-based database approaches is provided. In addition to classic concepts such as the entity and relationship model and its mapping in SQL database schemas, query languages or transaction management, other aspects for NoSQL databases such as non-relational data models, document and graph query languages (MQL, Cypher), the Map/Reduce procedure, distribution options (sharding, replication) or the CAP theorem (Consistency, Availability, Partition Tolerance) are explained.This 2nd English edition offers a new in-depth introduction to document databases with a method for modeling document structures, an overview of the document-oriented MongoDB query language MQL as well as security and architecture aspects. The topic of database security is newly introduced as a separate chapter and analyzed in detail with regard to data protection, integrity, and transactions. Texts on data management, database programming, and data warehousing and data lakes have been updated. In addition, the book now explains the concepts of JSON, JSON schema, BSON, index-free neighborhood, cloud databases, search engines and time series databases.The book includes more than 100 tables, examples and illustrations, and each chapter offers a list of resources for further reading. It conveys an in-depth comparison of relational and non-relational approaches and shows how to undertake development for big data applications. This way, it benefits students and practitioners working across the broad field of data science and applied information technology.

Begin your journey toward efficient data manipulation with this robust technical guide and enhance your aggregation skills while building efficient pipelines for a variety of tasksKey FeaturesBuild effective aggregation pipelines for increased productivity and performanceSolve common data manipulation and analysis problems with the help of practical examplesLearn essential strategies to aggregate time series data in financial datasets and IoTPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionOfficially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks. This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale. Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you'll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.What you will learnDevelop dynamic aggregation pipelines tailored to changing business requirementsMaster essential techniques to optimize aggregation pipelines for rapid data processingAchieve optimal efficiency for applying aggregations to vast datasets with effective sharding strategiesEliminate the performance penalties of processing data externally by filtering, grouping, and calculating aggregated values directly within the databaseUse pipelines to help you secure your data access and distributionWho this book is forThis book is for intermediate-level developers, architects, analysts, engineers, and data scientists who are interested in learning about aggregation capabilities in MongoDB. Working knowledge of MongoDB is needed to get the most out of this book.

This book details recent developments in the emerging area of plug-and-play (PnP) visual subgraph query interfaces (VQI). These PnP interfaces are grounded in the principles of human-computer interaction (HCI) and cognitive psychology to address long-standing limitations to bottom-up search capabilities in graph databases using traditional graph query languages, which often require domain experts and specialist programmers. This book explains how PnP interfaces go against the traditional mantra of VQI construction by taking a data-driven approach and giving end users the freedom to easily and quickly construct and maintain a VQI for any data sources without resorting to coding. The book walks readers through the intuitive PnP interface that uses templates where the underlying graph repository represents the socket and user-specified requirements represent the plug. Hence, a PnP interface enables an end user to change the socket (i.e., graph repository) or the plug (i.e., requirements) as necessary to automatically and effortlessly generate VQIs. The book argues that such a data-driven paradigm creates several benefits, including superior support for visual subgraph query construction, significant reduction in the manual cost of constructing and maintaining a VQI for any graph data source, and portability of the interface across diverse sources and querying applications. This book provides a comprehensive introduction to the notion of PnP interfaces, compares it to its classical manual counterpart, and reviews techniques for automatic construction and maintenance of these new interfaces. In synthesizing current research on plug-and-play visual subgraph query interface management, this book gives readers a snapshot of the state of the art in this topic as well as future research directions.

Databases have become an integral part of modern life. Today's society is an information-driven society, and database technology has a direct impact on all aspects of daily life. Decisions are routinely made by organizations based on the information collected and stored in databases. Database management systems such as Oracle are crucial to apply data in industrial or commercial systems. Equally crucial is a graphical user interface (GUI) to enable users to access and manipulate data in databases. The Apache NetBeans IDE with Java is an ideal candidate for developing a GUI with programming functionality.Oracle Database Programming with Java: Ideas, Designs, and Implementations is written for college students and software programmers who want to develop practical and commercial database programming with Java and relational databases such as Oracle Database XE 18c. The book details practical considerations and applications of database programming with Java and is filled with authentic examples as well as detailed explanations. Advanced topics in Java Web, like Java Web Applications and Java Web Services, are covered in real project examples to show how to handle the database programming issues in the Apache NetBeans IDE environment.This book features: A real sample database, CSE _ DEPT, which is built with Oracle SQL Developer, provided and used throughout the book Step by step, detailed illustrations and descriptions of how to design and build a practical relational database Fundamental and advanced Java database programming techniques practical to both beginning students and experienced programmers Updated Java desktop and Web database programming techniques, such as Java Enterprise Edition 7, JavaServer Pages, JavaServer Faces, Enterprise Java Beans, Web applications and Web services, including GlassFish and Tomcat Web servers More than 30 real database programming projects with detailed illustrations Actual JDBC APIs and JDBC drivers, along with code explanations Homework and selected solutions for each chapter to strengthen and improve students'learning and understanding of the topics they have studied

Get acquainted with Cypher in a guided manner quickly and learn how to query the graph databases with efficient and performant queriesKey FeaturesWork with Cypher syntax and semantics while building graph traversal queriesGet up and running with advanced Cypher concepts like List, Maps, OPTIONAL MATCHMaster best practices in writing effective queries leveraging data modeling and patternsBook DescriptionWhile it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way. This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You'll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users. After having worked through this book, you'll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.What you will learnWrite Cypher queries from basic to advanced levelMap the source data to the graph data model in an iterative fashionLoad the data into a graph using LOAD CSV, APOC, and client driversMap the business questions to graph queries effectivelyIdentify query performance issues and fix themExtend capabilities of Cypher using APOC utilitiesWork with graph visualization tools like Bloom and BrowserWho this book is forThis book is targeted at Database Administrator, Database Developers, Graph Database Developers, and Graph Database Architects. This book will also help someone migrate from a DBA role to a graph data engineer or data scientist If you are working with graph databases and need to learn Cypher, or are a basic Cypher developer who wants to get better at data modeling and tuning queries to build performant Cypher queries, then this is the book for you.

A Comprehensive Study of SQL - Practice and Implementation is designed as a textbook and provides a comprehensive approach to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. It demonstrates how to leverage the two most vital tools for data query and analysis – SQL and Excel – to perform comprehensive data analysis without the need for a sophisticated and expensive data mining tool or application.Features The book provides a complete collection of modeling techniques, beginning with fundamentals and gradually progressing through increasingly complex real-world case studies It explains how to build, populate, and administer high-performance databases and develop robust SQL-based applications It also gives a solid foundation in best practices and relational theory The book offers self-contained lessons on key SQL concepts or techniques at the end of each chapter using numerous illustrations and annotated examples This book is aimed primarily at advanced undergraduates and graduates with a background in computer science and information technology. Researchers and professionals will also find this book useful.

This textbook covers both fundamental and advanced Java database programming techniques for beginning and experienced students as well as programmers (courses related to database programming in Java with Apache NetBeans IDE 12 environment). A sample SQL Server 2019 Express database, CSE_DEPT, is created and implemented in all example projects throughout this textbook. Over 40 real sample database programming projects are covered in this textbook with detailed illustrations and explanations to help students understand the key techniques and programming technologies. Chapters include homework and selected solutions to strengthen and improve students'learning and understanding for topics they study in the classroom. Both Java desktop and Web applications with SQL Server database programming techniques are discussed and analyzed. Some updated Java techniques, such as Java Server Pages (JSP), Java Server Faces (JSF), Java Web Service (JWS), JavaServer Pages Standard Tag Library (JSTL), JavaBeans and Java API for XML Web Services (JAX-WS) are also discussed and implemented in the real projects developed in this textbook.This textbook targets mainly advanced-level students in computer science, but it also targets entry-level students in computer science and information system. Programmers, software engineers and researchers will also find this textbook useful as a reference for their projects.

Now in its fourth edition and covering Oracle Database 21c, this best-selling book continues to bring you some of the best thinking on how to apply Oracle Database to produce scalable applications that perform well and deliver correct results. Tom Kyte and Darl Kuhn share a simple philosophy:'you can treat Oracle as a black box and just stick data into it, or you can understand how it works and exploit it as a powerful computing environment.'If you choose the latter, then you'll find that there are few information management problems that you cannot solve quickly and elegantly.This fully revised fourth edition covers the developments and new features up to Oracle Database 21c. Up-to-date features are covered for tables, indexes, data types, sequences, partitioning, data loading, temporary tables, and more. All the examples are demonstrated using modern techniques and are executed in container and pluggable databases. The book's proof-by-example approach encourages you to let evidence be your guide. Try something. See the result. Understand why the result is what it is. Apply your newfound knowledge with confidence. The book covers features by explaining how each one works, how to implement software using it, and the common pitfalls associated with it. Don't treat Oracle Database as a black box. Get this book. Dive deeply into Oracle Database's most powerful features that many do not invest the time to learn about. Set yourself apart from your competition and turbo-charge your career.What You Will LearnIdentify and effectively resolve application performance issues and bottlenecksArchitect systems to leverage the full power and feature set of Oracle's database engineConfigure a database to maximize the use of memory structures and background processesUnderstand internal locking and latching technology and how it impacts your systemProactively recommend best practices around performance for table and index structuresTake advantage of advanced features such as table partitioning and parallel executionWho This Book Is ForOracle developers and Oracle DBAs. If you're a developer and want a stronger understanding of Oracle features and architecture that will enable your applications to scale regardless of the workload, this book is for you. If you're a DBA and want to intelligently work with developers to design applications that effectively leverage Oracle technology, then look no further.

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

Get a practical introduction to CockroachDB. This book starts with installation and foundational concepts and takes you through to creating clusters that are ready for production environments. You will learn how to create, optimize, and operate CockroarchDB clusters in single and multi-region environments. You will encounter anti-patterns to avoid, as well as testing techniques for integration and load testing. The book explains why CockroachDB exists, goes over its major benefits, and quickly transitions into installing and configuring CockroachDB. Just as quickly, you'll be creating databases, getting data into those databases, and querying that data from your applications. You'll progress to data privacy laws such as GDPR and CCPA, and learn how CockroachDB's global distribution features can help you comply with ever-shifting data sovereignty regulations. From there, you'll move into deployment topologies, guidance on integration testing and load testing, best practices, and a readiness checklist for production deployments. What You Will LearnDeploy and interact with CockroachDBDesign and optimize databases and tablesChoose the correct data types for modeling your dataProtect data with database and table encryptionAchieve compliance with international data privacy regulationsScale your databases in a way that enhances their performanceMonitor changes to the data and health of your databasesWho This Book Is ForDevelopers and database administrators who want to provide a secure, reliable, and effortlessly distributed home for their data; those who wish to use a modern tool to tackle the kinds of scaling challenges that have previously required dedicated teams of people to solve; anyone who wants to leverage their database to solve non-trivial, real-world challenges while protecting their data and users

SQL is the ubiquitous language for software developers working with structured data. Most developers who rely on SQL are experts in their favorite language (such as Java, Python, or Go), but they're not experts in SQL. They often depend on antipatterns - solutions that look right but become increasingly painful to work with as you uncover their hidden costs. Learn to identify and avoid many of these common blunders. Refactor an inherited nightmare into a data model that really works. Updated for the current versions of MySQL and Python, this new edition adds a dozen brand new mini-antipatterns for quick wins. No matter which platform, framework, or language you use, the database is the foundation of your application, and the SQL database language is the standard for working with it. Antipatterns are solutions that look simple at the surface, but soon mire you down with needless work. Learn to identify these traps, and craft better solutions for the often-asked questions in this book. Avoid the mistakes that lead to poor performance and quality, and master the principles that make SQL a powerful and flexible tool for handling data and logic. Dive deep into SQL and database design, and learn to recognize the most common missteps made by software developers in database modeling, SQL query logic, and code design of data-driven applications. See practical examples of misconceptions about SQL that can lure software projects astray. Find the greatest value in each group of data. Understand why an intersection table may be your new best friend. Store passwords securely and don't reinvent the wheel. Handle NULL values like a pro. Defend your web applications against the security weakness of SQL injection. Use SQL the right way - it can save you from headaches and needless work, and let your application really shine! What You Need: The SQL examples use the MySQL 8.0 flavor, but other popular brands of RDBMS are mentioned. Other code examples use Python 3.9+ or Ruby 2.7+.

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 LearnImplement the Data Lakehouse Paradigm on Microsoft's Azure cloud platformBenefit from the new Delta Lake open-source storage layer for data lakehouses Take advantage of schema evolution, change feeds, live tables, and moreWritefunctional PySpark code for data lakehouse ELT jobsOptimize Apache Spark performance through partitioning, indexing, and other tuning optionsChoose between alternatives such as Databricks, Synapse Analytics, and SnowflakeWho This Book Is ForData, 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.

Essential to database design, entity-relationship (ER) diagrams are known for their usefulness in data modeling and mapping out clear database designs. They are also well-known for being difficult to master. With Database Design Using Entity-Relationship Diagrams, Third Edition, database designers, developers, and students preparing to enter the field can quickly learn the ins and outs of data modeling through ER diagramming. Building on the success of the bestselling first and second editions, this accessible text includes a new chapter on the relational model and functional dependencies. It also includes expanded chapters on Enhanced Entity-Relationship (EER) diagrams and reverse mapping. It uses cutting-edge case studies and examples to help readers master database development basics and defines ER and EER diagramming in terms of requirements (end user requests) and specifications (designer feedback to those requests), facilitating agile database development. This book Describes a step-by-step approach for producing an ER diagram and developing a relational database from it Contains exercises, examples, case studies, bibliographies, and summaries in each chapter Details the rules for mapping ER diagrams to relational databases Explains how to reverse engineer a relational database back to an entity-relationship model Includes grammar for the ER diagrams that can be presented back to the user, facilitating agile database development The updated exercises and chapter summaries provide the real-world understanding needed to develop ER and EER diagrams, map them to relational databases, and test the resulting relational database. Complete with a wealth of additional exercises and examples throughout, this edition should be a basic component of any database course. Its comprehensive nature and easy-to-navigate structure make it a resource that students and professionals will turn to throughout their careers.

The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today's corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.

'The most comprehensive reference for EF Core that does—or ever will—exist.'- Stephen Byrne, Intel Corporation Entity Framework Core in Action, Second Edition teaches you to write flawless database interactions for.NET applications. Summary Entity Framework Core in Action, Second Edition is an in-depth guide to reading and writing databases with EF Core. Revised from the bestselling original edition, it's filled with over 100 diagrams, code snippets, and examples—including building and scaling your own bookselling web application. Learn from author Jon Smith's extensive experience working with EF Core in production, as you discover time-saving patterns and best practices for security, performance tuning, and unit testing. All of the book's code is available on GitHub. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Entity Framework radically simplifies data access in.NET applications. This easy-to-use object-relational mapper (ORM) lets you write database code in pure C#. It automatically maps classes to database tables and enables queries with standard LINQ commands. It even generates SQL, so you don't have to! About the book Entity Framework Core in Action, Second Edition teaches you to write flawless database interactions for.NET applications. Following relevant examples from author Jon Smith's extensive experience, you'll progress quickly from EF basics to advanced techniques. In addition to the latest EF features, this book addresses performance, security, refactoring, and unit testing. This updated edition also contains new material on NoSQL databases. What's inside Configure EF to define every table and column Update your schema as your app grows Integrating EF with existing C# application Write and test business logic for database access Applying a Domain-Driven Design to EF Core Getting the best performance out of EF Core About the reader For.NET developers familiar with relational databases. About the author Jon P. Smith is a freelance software developer and architect with a special focus on.NET and Azure. Table of Contents PART 1 1 Introduction to Entity Framework Core 2 Querying the database 3 Changing the database content 4 Using EF Core in business logic 5 Using EF Core in ASP.NET Core web applications 6 Tips and techniques for reading and writing with EF Core PART 2 7 Configuring nonrelational properties 8 Configuring relationships 9 Handling database migrations 10 Configuring advanced features and handling concurrency conflicts 11 Going deeper into the DbContext PART 3 12 Using entity events to solve business problems 13 Domain-Driven Design and other architectural approaches 14 EF Core performance tuning 15 Master class on performance-tuning database queries 16 Cosmos DB, CQRS, and other database types 17 Unit testing EF Core applications

Use the code-driven approach of Entity Framework Core 5 to build a functional web application that accesses a database on the backend server. This book covers the common use cases of Entity Framework that a developer needs to master in order to begin building applications that run against a database. Throughout the book you will be shown how to use Entity Framework Core 5 by implementing a simple ASP.NET Core Razor Pages line-of-business application. This example application will be similar to those you might write yourself and deploy to your users on a web or intranet site via a browser. This book takes a code-first approach in which your database will be created and seeded programmatically. You won't need to create the database through your database engine's interface. Instead, you will be shown how to define your data model in Entity Framework, and then let Entity Framework do the work of creating your database and schema for you. From there you will learn how to seed your database with example data, then to implement the common, so-called CRUD operations consisting of creating, retrieving, updating, and deleting rows of data. By the end of the book you will have built a well-designed application that you can use as the basis for future applications that you create in your job. What You Will LearnDownload and install Entity Framework Core 5Perform create, read, update, and delete (CRUD) operationsCreate and seed a database with example data using Entity Framework Core 5Incrementally add new database functionality through Entity Framework Core 5 migrationsRecognize when to take advantage of new features introduced in Entity Framework Core 5Deliver line-of-business applications using ASP.NET Core that run in a browserWho This Books Is ForDevelopers who are familiar with C# and the.NET Framework who want to learn database access using Entity Framework Core 5. For developers creating web-based, line-of-business applications who want to create those applications more quickly and efficiently when databases are involved.

Grundlagen und Schlüsseltechniken verstehen und mit vielen Beispielen vertiefen Konzepte und typische Arbeitsschritte verständlich und kompakt erklärt Die SQL-Beispiele laufen ohne Änderungen auf MySQL und (ggf. mit geringfügigen Änderungen) auf neueren Versionen von Oracle Database, DB2 und SQL Server Mit SQL Daten von diversen Plattformen und nicht-relationalen Datenbanken verarbeiten SQL-Kenntnisse sind nach wie vor unverzichtbar, um das Beste auf Ihren Daten herauszuholen. In seinem Handbuch vermittelt Alan Beaulieu die nötigen SQL-Grundlagen, um Datenbankanwendungen zu schreiben, administrative Aufgaben durchzuführen und Berichte zu erstellen. Sie finden neue Kapitel zu analytischen Funktionen, zu Strategien für die Arbeit mit großen Datenbanken sowie zu SQL und großen Datenmengen.Jedes Kapitel präsentiert eine in sich geschlossene Lektion zu einem Schlüsselkonzept oder einer Schlüsseltechnik von SQL und nutzt hierfür zahlreiche Abbildungen und kommentierte Beispiele. Durch Übungen vertiefen Sie die erlernten Fähigkeiten.

Know how locks work in MySQL and how they relate to transactions. This book explains the major role that locks play in database systems, showing how locks are essential in allowing high-concurrency workloads. You will learn about lock access levels and lock granularities from the user level as well as table locks to record and gap locks. Most importantly, the book covers troubleshooting techniques when locking becomes a pain point. Several of the lock types in MySQL have a duration of a transaction. For this reason, it is important to understand how transactions work. This book covers the basics of transactions as well as transaction isolation levels and how they affect locking. The book is meant to be your go-to resource for solving locking contention and similar problems in high-performance MySQL database applications. Detecting locking issues when they occur is the first key to resolving such issues. MySQL Concurrency provides techniques for detecting locking issues such as contention. The book shows how to analyze locks that are causing contention to see why those locks are in place. A collection of six comprehensive case studies combine locking and transactional theory with realistic lock conflicts. The case studies walk you through the symptoms to look for in order to identify which issue you are facing, the cause of the conflict, its analysis, solution, and how to prevent the issue in the future. What You Will LearnUnderstand which lock types exist in MySQL and how they are usedChoose the best transaction isolation level for a given transactionDetect and analyze lock contention when it occursReduce locking issues in your applicationsResolve deadlocks between transactionsResolve InnoDB record-level locking issuesResolve issues from metadata and schema locksWho This Book Is ForDatabase administrators and SQL developers who are familiar with MySQL and want to gain a better understanding of locking and transactions as well as how to work with them. While some experience with MySQL is required, no prior knowledge of locks and transactions is needed.

Get a comprehensive overview on how to set up and design an effective database with MySQL. This thoroughly updated edition covers MySQL's latest version, including its most important aspects. Whether you're deploying an environment, troubleshooting an issue, or engaging in disaster recovery, this practical guide provides the insights and tools necessary to take full advantage of this powerful RDBMS.Authors Vinicius Grippa and Sergey Kuzmichev from Percona show developers and DBAs methods for minimizing costs and maximizing availability and performance. You'll learn how to perform basic and advanced querying, monitoring and troubleshooting, database management and security, backup and recovery, and tuning for improved efficiency. This edition includes new chapters on high availability, load balancing, and using MySQL in the cloud.Get started with MySQL and learn how to use it in productionDeploy MySQL databases on bare metal, on virtual machines, and in the cloudDesign database infrastructuresCode highly efficient queriesMonitor and troubleshoot MySQL databasesExecute efficient backup and restore operationsOptimize database costs in the cloudUnderstand database concepts, especially those pertaining to MySQL

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 LearnMaster the Spark unified data analytics engine and its various componentsWork in tandem to provide a scalable, fault tolerant and performant data processing engineLeverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQLDevelop machine learning applications using Spark MLlibManage the machine learning development lifecycle using MLflowWho This Book Is ForData scientists, data engineers and software developers.

Data engineers who need to hit the ground running will use this book to build skills in Azure Data Factory v2 (ADF). The tutorial-first approach to ADF taken in this book gets you working from the first chapter, explaining key ideas naturally as you encounter them. From creating your first data factory to building complex, metadata-driven nested pipelines, the book guides you through essential concepts in Microsoft's cloud-based ETL/ELT platform. It introduces components indispensable for the movement and transformation of data in the cloud. Then it demonstrates the tools necessary to orchestrate, monitor, and manage those components.The hands-on introduction to ADF found in this book is equally well-suited to data engineers embracing their first ETL/ELT toolset as it is to seasoned veterans of Microsoft's SQL Server Integration Services (SSIS). The example-driven approach leads you through ADF pipeline construction from the ground up, introducing important ideas and making learning natural and engaging. SSIS users will find concepts with familiar parallels, while ADF-first readers will quickly master those concepts through the book's steady building up of knowledge in successive chapters. Summaries of key concepts at the end of each chapter provide a ready reference that you can return to again and again.What You Will LearnCreate pipelines, activities, datasets, and linked servicesBuild reusable components using variables, parameters, and expressionsMove data into and around Azure services automaticallyTransform data natively using ADF data flows and Power Query data wranglingMaster flow-of-control and triggers for tightly orchestrated pipeline executionPublish and monitor pipelines easily and with confidenceWho This Book Is ForData engineers and ETL developers taking their first steps in Azure Data Factory, SQL Server Integration Services users making the transition toward doing ETL in Microsoft's Azure cloud, and SQL Server database administrators involved in data warehousing and ETL operations

Modern businesses depend on data for their very survival, creating a need for sophisticated databases and database technologies to help store, organise and transport their valuable data.This updated and expanded, easy-to-read textbook/reference presents a comprehensive introduction to databases, opening with a concise history of databases and of data as an organisational asset. As relational database management systems are no longer the only database solution, the book takes a wider view of database technology, encompassing big data, NoSQL, object and object-relational, and in-memory databases. Presenting both theoretical and practical elements, the new edition also examines the issues of scalability, availability, performance and security encountered when building and running a database in the real world.Topics and features:Presents review and discussion questions at the end of each chapter, in addition toskill-building, hands-on exercisesProvides new material on database adaptiveness, integration, and efficiency in relation to data growthIntroduces a range of commercial databases and encourages the reader to experiment with these in an associated learning environmentReviews use of a variety of databases in business environments, including numerous examplesDiscusses areas for further research within this fast-moving domainWith its learning-by-doing approach, supported by both theoretical and practical examples, this clearly-structured textbook will be of great value to advanced undergraduate and postgraduate students of computer science, software engineering, and information technology. Practising database professionals and application developers will also find the book an ideal reference that addresses today's business needs.

This book is a greatly supplemented translation from Portuguese, originally published in 2015. It discusses the most appropriate ways to create databases for research on history and other humanities, including an extensive debate about the usages that historians have made of computing since the 1950s. It has four chapters: the first is dedicated to theoretical and methodical questions about the usage of databases in history; the second is about technical issues; the third presents the concept of research engineering (how to improve research in groups); the last is about the construction of databases. The author states that the use of technology in research in history and humanities should be preceded and mediated by theories and methods which deal with these disciplines and not by technical issues. The historian must know how to think “correctly” in order to use the technological tools in an autonomous way. The book provides a background, demonstrating how theory, methodology, and technique are always articulated in historical research, and will appeal to history students and researchers.

Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players'physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made. Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data—from students to professional researchers and from early-career to seasoned professionals.Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability.This, the 47th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, constitutes a special issue focusing on Digital Ecosystems and Social Networks. The 9 revised selected papers cover topics that include Social Big Data, Data Analysis, Cloud-Based Feedback, Experience Ecosystems, Pervasive Environments, and Smart Systems.

This book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users'fingertips in the form of an iPhone app.About the AuthorDr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Get the most out of MongoDB using a problem-solution approach. This book starts with recipes on the MongoDB query language, including how to query various data structures stored within documents. These self-contained code examples allow you to solve your MongoDB problems without fuss.MongoDB Recipes describes how to use advanced querying in MongoDB, such as indexing and the aggregation framework. It demonstrates how to use the Compass function, a GUI client interacting with MongoDB, and how to apply data modeling to your MongoDB application. You'll see recipes on the latest features of MongoDB 4 allowing you to manage data in an efficient manner using MongoDB.What You Will Learn Work with the MongoDB document model Design MongoDB schemas Use the MongoDB query languageHarness the aggregation frameworkCreate replica sets and sharding in MongoDBWho This Book Is ForDevelopers and professionals who work with MongoDB.

Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This third edition—updated for Cassandra 4.0—provides the technical details and practical examples you need to put this database to work in a production environment.Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's nonrelational design, with special attention to data modeling. If you're a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra's speed and flexibility.Understand Cassandra's distributed and decentralized structureUse the Cassandra Query Language (CQL) and cqlsh—the CQL shellCreate a working data model and compare it with an equivalent relational modelDevelop sample applications using client drivers for languages including Java, Python, and Node.jsExplore cluster topology and learn how nodes exchange data

Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learningKey FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook DescriptionNeo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You'll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You'll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You'll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you'll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you'll get to grips with structuring a web application for production using Neo4j. By the end of this book, you'll not only be able to harness the power of graphs to handle a broad range of problem areas, but you'll also have learned how to use Neo4j efficiently to identify complex relationships in your data.What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is forThis book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.

With the aim of simplifying relational database modeling, Database Modeling Step-by-Step presents the standard approach to database normalization and then adds its own approach, which is a more simplistic, intuitive way to building relational database models. Going from basics to contemporary topics, the book opens with relational data modeling and ends with BigData database modeling following a road map of the evolution in relational modeling and including brief introductions to data warehousing and BigData modeling. A break-down of the elements of a model explains what makes up a relational data model. This is followed by a comparison between standard normalization and a more simplistic intuitive approach to data modeling that a beginner can follow and understand. A brief chapter explains how to use the database programming language SQL (Structured Query Language), which reads from and writes to a relational database. SQL is fundamental to data modeling because it helps in understanding how the model is used. In addition to the relational model, the last three chapters cover important modern world topics including denormalization that leads into data warehouses and BigData database modeling. The book explains how there is not much to logical data modeling in BigData databases because as they are often schema-less, which means that BigData databases do not have schemas embedded into the database itself, they have no metadata and thus not much of a logical data model. Online bonus chapters include a case study that covers relational data modeling and are available at the author's web site: www.oracletroubleshooter.com/datamodeling.html

Strengthen your understanding of database management today with the thorough, hands-on presentation found in CONCEPTS OF DATABASE MANAGEMENT, 10th Edition. Real cases, practical examples, helpful screenshots and concise explanations help clarify concepts, such as database design, data integrity, normalization, concurrent updates, data security and big data. Completely updated content reflects Microsoft® Access 2019, Office 365 standards and SQL Server 2019, while exploring SQL in a database-neutral environment. Detailed coverage presents the relational model (including QBE and SQL), normalization and views as well as database administration and management. You also examine advanced topics, such as distributed databases, data warehouses, stored procedures, triggers, data macros and Web Apps. Completely redesigned MindTap digital resources provide step-by-step practice using Access 2019 with instant feedback. Trust this contemporary introduction to help you master today's database techniques to advance your career in any field.

In the computer science industry, high levels of performance remain the focal point in software engineering. This quest has made current systems exceedingly complex, as practitioners strive to discover novel approaches to increase the capabilities of modern computer structures. A prevalent area of research in recent years is scalable transaction processing and its usage in large databases and cloud computing. Despite its popularity, there remains a need for significant research in the understanding of scalability and its performance within distributed databases. Handling Priority Inversion in Time-Constrained Distributed Databases provides emerging research exploring the theoretical and practical aspects of database transaction processing frameworks and improving their performance using modern technologies and algorithms. Featuring coverage on a broad range of topics such as consistency mechanisms, real-time systems, and replica management, this book is ideally designed for IT professionals, computing specialists, developers, researchers, data engineers, executives, academics, and students seeking research on current trends and developments in distributed computing and databases.

Strengthen your understanding of database management today with the thorough, hands-on presentation found in CONCEPTS OF DATABASE MANAGEMENT, 10th Edition. Real cases, practical examples, helpful screenshots and concise explanations help clarify concepts, such as database design, data integrity, normalization, concurrent updates, data security and big data. Completely updated content reflects Microsoft® Access 2019, Office 365 standards and SQL Server 2019, while exploring SQL in a database-neutral environment. Detailed coverage presents the relational model (including QBE and SQL), normalization and views as well as database administration and management. You also examine advanced topics, such as distributed databases, data warehouses, stored procedures, triggers, data macros and Web Apps. Completely redesigned MindTap digital resources provide step-by-step practice using Access 2019 with instant feedback. Trust this contemporary introduction to help you master today's database techniques to advance your career in any field.

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Get to grips with SQL fundamentals and learn how to efficiently create, read and update information stored in databasesKey FeaturesUnderstand the features and syntax of SQL and use them to query databasesLearn how to create databases and tables and manipulate the data within themCreate advanced queries and apply them on realistic databases with hands-on activitiesBook DescriptionMany software applications are backed by powerful relational database systems, meaning that the skills to be able to maintain a SQL database and reliably retrieve data are in high demand. With its simple syntax and effective data manipulation capabilities, SQL enables you to manage relational databases with ease. The SQL Workshop will help you progress from basic to advanced-level SQL queries in order to create and manage databases successfully. This Workshop begins with an introduction to basic CRUD commands and gives you an overview of the different data types in SQL. You'll use commands for narrowing down the search results within a database and learn about data retrieval from single and multiple tables in a single query. As you advance, you'll use aggregate functions to perform calculations on a set of values, and implement process automation using stored procedures, functions, and triggers. Finally, you'll secure your database against potential threats and use access control to keep your data safe. Throughout this Workshop, you'll use your skills on a realistic database for an online shop, preparing you for solving data problems in the real world. By the end of this book, you'll have built the knowledge, skills and confidence to creatively solve real-world data problems with SQL.What you will learnCreate databases and insert data into themUse SQL queries to create, read, update, and delete dataMaintain data integrity and consistency through normalizationCustomize your basic SQL queries to get the desired outputRefine your database search using the WHERE and HAVING clausesUse joins to fetch data from multiple tables and create custom reportsImprove web application performance by automating processesSecure a database with GRANT and REVOKE privilegesWho this book is forThis Workshop is suitable for anyone who wants to learn how to use SQL to work with databases. No prior SQL or database experience is necessary. Whether you're an aspiring software developer, database engineer, data scientist, or systems administrator, this Workshop will quickly get you up and running.