Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.

Apache Superset Quick Start Guide teaches you how to leverage Apache Superset to create interactive and insightful data visualizations. With this book, you'll understand how to integrate Superset with popular databases and build user-friendly dashboards tailored for business intelligence needs. What this Book will help me do Set up and configure Apache Superset for data visualization tasks. Integrate data from SQL databases into Superset for dashboards. Design dashboards tailored to represent business metrics and insights. Use Superset's visualization techniques to explore and present various datasets. Understand and apply user role management and security features in Superset. Author(s) None Shekhar is an experienced data visualization and business intelligence specialist with years of experience in working with Apache Superset. They have written several guides on utilizing open-source tools for enterprise needs. Their technical expertise and approachable writing style make this guide practical and engaging. Who is it for? This book is geared towards data analysts, business intelligence professionals, and developers. Beginners to Superset can quickly grasp the fundamentals, while those with prior experience in data visualization will appreciate the advanced techniques. It's perfect for anyone looking to enhance their data storytelling and dashboard design skills.

Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure. On completion, you’ll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You’ll also become familiar with machine learning algorithms with real-time usage. What You Will Learn Discover the functional programming features of Scala Understand the completearchitecture of Spark and its components Integrate Apache Spark with Hive and Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts and libraries using Spark's MLlib packages Who This Book Is For Developers and professionals who deal with batch and stream data processing.

This book, Hands-On Big Data Modeling, provides you with practical guidance on data modeling techniques, focusing particularly on the challenges of big data. You will learn the concepts behind various data models, explore tools and platforms for efficient data management, and gain hands-on experience with structured and unstructured data. What this Book will help me do Master the fundamental concepts of big data and its challenges. Explore advanced data modeling techniques using SQL, Python, and R. Design effective models for structured, semi-structured, and unstructured data types. Apply data modeling to real-world datasets like social media and sensor data. Optimize data models for performance and scalability in various big data platforms. Author(s) The authors of this book are experienced data architects and engineers with a strong background in developing scalable data solutions. They bring their collective expertise to simplify complex concepts in big data modeling, ensuring readers can effectively apply these techniques in their projects. Who is it for? This book is intended for data architects, business intelligence professionals, and any programmer interested in understanding and applying big data modeling concepts. If you are already familiar with basic data management principles and want to enhance your skills, this book is perfect for you. You will learn to tackle real-world datasets and create scalable models. Additionally, it is suitable for professionals transitioning to working with big data frameworks.

In "Hands-On Data Science with SQL Server 2017," you will discover how to implement end-to-end data analysis workflows, leveraging SQL Server's robust capabilities. This book guides you through collecting, cleaning, and transforming data, querying for insights, creating compelling visualizations, and even constructing predictive models for sophisticated analytics. What this Book will help me do Grasp the essential data science processes and how SQL Server supports them. Conduct data analysis and create interactive visualizations using Power BI. Build, train, and assess predictive models using SQL Server tools. Integrate SQL Server with R, Python, and Azure for enhanced functionality. Apply best practices for managing and transforming big data with SQL Server. Author(s) Marek Chmel and Vladimír Mužný bring their extensive experience in data science and database management to this book. Marek is a seasoned database specialist with a strong background in SQL, while Vladimír is known for his instructional expertise in analytics and data manipulation. Together, they focus on providing actionable insights and practical examples tailored for data professionals. Who is it for? This book is an ideal resource for aspiring and seasoned data scientists, data analysts, and database professionals aiming to deepen their expertise in SQL Server for data science workflows. Beginners with fundamental SQL knowledge will find it a guided entry into data science applications. It is especially suited for those who aim to implement data-driven solutions in their roles while leveraging SQL's capabilities.

Protect your data from attack by using SQL Server technologies to implement a defense-in-depth strategy for your database enterprise. This new edition covers threat analysis, common attacks and countermeasures, and provides an introduction to compliance that is useful for meeting regulatory requirements such as the GDPR. The multi-layered approach in this book helps ensure that a single breach does not lead to loss or compromise of confidential, or business sensitive data. Database professionals in today’s world deal increasingly with repeated data attacks against high-profile organizations and sensitive data. It is more important than ever to keep your company’s data secure. Securing SQL Server demonstrates how developers, administrators and architects can all play their part in the protection of their company’s SQL Server enterprise. This book not only provides a comprehensive guide to implementing the security model in SQLServer, including coverage of technologies such as Always Encrypted, Dynamic Data Masking, and Row Level Security, but also looks at common forms of attack against databases, such as SQL Injection and backup theft, with clear, concise examples of how to implement countermeasures against these specific scenarios. Most importantly, this book gives practical advice and engaging examples of how to defend your data, and ultimately your job, against attack and compromise. What You'll Learn Perform threat analysis Implement access level control and data encryption Avoid non-reputability by implementing comprehensive auditing Use security metadata to ensure your security policies are enforced Mitigate the risk of credentials being stolen Put countermeasures in place against common forms of attack Who This Book Is For Database administrators who need to understand and counteract the threat of attacks against their company’s data, and useful for SQL developers and architects

Get SQL Server up and running on the Linux operating system and containers. No database professional managing or developing SQL Server on Linux will want to be without this deep and authoritative guide by one of the most respected experts on SQL Server in the industry. Get an inside look at how SQL Server for Linux works through the eyes of an engineer on the team that made it possible. Microsoft SQL Server is one of the leading database platforms in the industry, and SQL Server 2017 offers developers and administrators the ability to run a database management system on Linux, offering proven support for enterprise-level features and without onerous licensing terms. Organizations invested in Microsoft and open source technologies are now able to run a unified database platform across all their operating system investments. Organizations are further able to take full advantage of containerization through popular platforms such as Docker and Kubernetes. Pro SQL Server on Linux walks you through installing and configuring SQL Server on the Linux platform. The author is one of the principal architects of SQL Server for Linux, and brings a corresponding depth of knowledge that no database professional or developer on Linux will want to be without. Throughout this book are internals of how SQL Server on Linux works including an in depth look at the innovative architecture. The book covers day-to-day management and troubleshooting, including diagnostics and monitoring, the use of containers to manage deployments, and the use of self-tuning and the in-memory capabilities. Also covered are performance capabilities, high availability, and disaster recovery along with security and encryption. The book covers the product-specific knowledge to bring SQL Server and its powerful features to life on the Linux platform, including coverage of containerization through Docker and Kubernetes. What You'll Learn Learn about the history and internal of the unique SQL Server on Linux architecture. Install and configure Microsoft’s flagship database product on the Linux platform Manage your deployments using container technology through Docker and Kubernetes Know the basics of building databases, the T-SQL language, and developing applications against SQL Server on Linux Use tools and features to diagnose, manage, and monitor SQL Server on Linux Scale your application by learning the performance capabilities of SQL Server Deliver high availability and disaster recovery to ensure business continuity Secure your database from attack, and protect sensitive data through encryption Take advantage of powerful features such as Failover Clusters, Availability Groups, In-Memory Support, and SQL Server’sSelf-Tuning Engine Learn how to migrate your database from older releases of SQL Server and other database platforms such as Oracle and PostgreSQL Build and maintain schemas, and perform management tasks from both GUI and command line Who This Book Is For Developers and IT professionals who are new to SQL Server and wish to configure it on the Linux operating system. This book is also useful to those familiar with SQL Server on Windows who want to learn the unique aspects of managing SQL Server on the Linux platform and Docker containers. Readers should have a grasp of relational database concepts and be comfortable with the SQL language.

In the 'Redash v5 Quick Start Guide', you'll learn everything you need to master the Redash data visualization platform and confidently create compelling dashboards. This book covers how to connect to different data sources, use SQL to query data, and design and share insightful visualizations. What this Book will help me do Understand how to install, configure, and troubleshoot Redash for your data projects. Gain skills in managing user roles and permissions to ensure secure data collaboration. Learn to connect Redash to various data sources and fetch, process, and handle data. Master the creation of advanced visualizations to effectively present complex data. Develop proficiency in utilizing the Redash API for integrating programmatic interactions. Author(s) None Leibzon is a recognized expert in data visualization and Business Intelligence tools, with years of experience working with data-driven systems. Drawing from his deep practical knowledge of Redash and its applications, None has crafted this guide to be accessible and highly practical. His goal is to enable learners and professionals to unlock the power of data storytelling through intuitive and actionable visualization. Who is it for? If you're a Data Analyst, BI professional, or Data Developer with basic SQL skills, this book is tailored for you. It assumes no prior knowledge of Redash but benefits those who understand fundamental Business Intelligence concepts. Whether you're looking to create your first visualization or streamline data collaboration, this guide will help you achieve your goals.

Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort. About the Technology Not all stream-based applications require a dedicated processing cluster. The lightweight Kafka Streams library provides exactly the power and simplicity you need for message handling in microservices and real-time event processing. With the Kafka Streams API, you filter and transform data streams with just Kafka and your application. About the Book Kafka Streams in Action teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you’ll explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You’ll even dive into streaming SQL with KSQL! Practical to the very end, it finishes with testing and operational aspects, such as monitoring and debugging. What's Inside Using the KStreams API Filtering, transforming, and splitting data Working with the Processor API Integrating with external systems About the Reader Assumes some experience with distributed systems. No knowledge of Kafka or streaming applications required. About the Author Bill Bejeck is a Kafka Streams contributor and Confluent engineer with over 15 years of software development experience. Quotes A great way to learn about Kafka Streams and how it is a key enabler of event-driven applications. - From the Foreword by Neha Narkhede, Cocreator of Apache Kafka A comprehensive guide to Kafka Streams—from introduction to production! - Bojan Djurkovic, Cvent Bridges the gap between message brokering and real-time streaming analytics. - Jim Mantheiy Jr., Next Century Valuable both as an introduction to streams as well as an ongoing reference. - Robin Coe, TD Bank

Identify and fix causes of poor performance. You will learn Query Store, adaptive execution plans, and automated tuning on the Microsoft Azure SQL Database platform. Anyone responsible for writing or creating T-SQL queries will find valuable the insight into bottlenecks, including how to recognize them and eliminate them. This book covers the latest in performance optimization features and techniques and is current with SQL Server 2017. If your queries are not running fast enough and you’re tired of phone calls from frustrated users, then this book is the answer to your performance problems. SQL Server 2017 Query Performance Tuning is about more than quick tips and fixes. You’ll learn to be proactive in establishing performance baselines using tools such as Performance Monitor and Extended Events. You’ll recognize bottlenecks and defuse them before the phone rings. You’ll learn some quick solutions too, but emphasis is on designing for performance and getting it right. The goal is to head off trouble before it occurs. What You'll Learn Use Query Store to understand and easily change query performance Recognize and eliminate bottlenecks leading to slow performance Deploy quick fixes when needed, following up with long-term solutions Implement best practices in T-SQL to minimize performance risk Design in the performance that you need through careful query and index design Utilize the latest performance optimization features in SQL Server 2017 Protect query performance during upgrades to the newer versions of SQL Server Who This Book Is For Developers and database administrators with responsibility for application performance in SQL Server environments. Anyone responsible for writing or creating T-SQL queries will find valuable the insight into bottlenecks, including how to recognize them and eliminate them.

"Data Science with SQL Server Quick Start Guide" introduces you to leveraging SQL Server's most recent features for data science projects. You will explore the integration of data science techniques using R, Python, and Transact-SQL within SQL Server's environment. What this Book will help me do Use SQL Server's capabilities for data science projects effectively. Understand and preprocess data using SQL queries and statistics. Design, train, and evaluate machine learning models in SQL Server. Visualize data insights through advanced graphing techniques. Deploy and utilize machine learning models within SQL Server environments. Author(s) Dejan Sarka is a data science and SQL Server expert with years of industry experience. He specializes in melding database systems with advanced analytics, offering practical guidance through real-world scenarios. His writing provides clear, step-by-step methods, making complex topics accessible. Who is it for? This book is tailored for professionals familiar with SQL Server who are looking to delve into data science. It is also ideal for data scientists aiming to incorporate SQL Server into their analytics workflows. The content assumes basic exposure to SQL Server, ensuring a straightforward learning curve for its audience.

Deliver advanced functionality faster and cheaper by exploiting SQL Server's ever-growing amount of built-in support for modern data formats. Learn about the growing support within SQL Server for operations and data transformations that have previously required third-party software and all the associated licensing and development costs. Benefit through a better understanding of what can be done inside the database engine with no additional costs or development time invested in outside software. Widely used types such as JSON and XML are well-supported by the database engine. The same is true of hierarchical data and even temporal data. Knowledge of these advanced types is crucial to unleashing the full power that's available from your organization's SQL Server database investment. SQL Server Advanced Data Types explores each of the complex data types supplied within SQL Server. Common usage scenarios for eachcomplex data type are discussed, followed by a detailed discussion on how to work with each data type. Each chapter demystifies the complex data and you learn how to use the data types most efficiently. The book offers a practical guide to working with complex data, using real-world examples to demonstrate how each data type can be leveraged. Performance considerations are also discussed, including the implementation of special indexes such as XML indexes and spatial indexes. What You'll Learn Understand the implementation of basic data types and why using the correct type is so important Work with XML data through the XML data type Construct XML data from relational result sets Store and manipulate JSON data using the JSON data type Model and analyze spatial data for geographic information systems Define hierarchies and query them efficiently through the HierarchyID type Who This Book Is For SQL Server developers and application developers who need to store and access complex data structures

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it. Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications. What You Will Learn Understand Spark unified data processing platform Howto run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functions Build real-time applications using Spark Structured Streaming Develop intelligent applications with the Spark Machine Learning library Who This Book Is For Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.

Learn everything you need to manage Azure SQL Database with 'Professional Azure SQL Database Administration'. This book covers critical tasks such as migration, performance optimization, security, and disaster recovery. Perfect for those transitioning to the cloud, it equips you with skills to ensure your database runs smoothly and efficiently. What this Book will help me do Effectively migrate on-premise SQL Server databases to Azure. Master backup, restore, and security operations with Azure SQL Database. Optimize performance and scalability using monitoring and tuning techniques. Implement high availability and disaster recovery strategies. Simplify database management through automation and advanced techniques. Author(s) Ahmad Osama is a seasoned database admin and Azure expert with extensive experience in SQL Server and cloud database management. As a consultant and trainer, he has guided numerous organizations through cloud transitions. Ahmad's teaching philosophy blends practical insights with clear instruction. Who is it for? This book is intended for database administrators and developers looking to transition their skills to Azure SQL Database. If you have some experience with on-premise SQL Server and are familiar with PowerShell, you'll find this guide invaluable. Ideal for those wanting to develop, migrate, or manage Azure SQL solutions.

Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra

"Apache Hive Essentials" provides a focused guide to mastering the essential techniques of processing and analyzing big data with Apache Hive. What this Book will help me do Set up and configure a Hive environment for big data analysis. Compose effective queries using Hive's SQL-like language to extract insights. Optimize Hive performance to handle complex datasets efficiently. Implement data security and user-defined functions to extend capabilities. Integrate Hive with Hadoop tools for comprehensive data solutions. Author(s) Dayong Du, the author of "Apache Hive Essentials," has years of experience working with big data technologies and tools. With hands-on expertise in Hadoop and the entire ecosystem, he brings a practical and informed perspective to this complex field. His approach is to make these technologies accessible to developers and analysts of all levels. Who is it for? This book is perfect for data analysts, developers, or professionals familiar with SQL who are looking to start with Apache Hive for big data processing. It is suitable for those acquainted with Hadoop and its environment and want to expand their skills into efficient data querying and management. Readers should have an interest in how to leverage big data tools for real-world solutions.

Learn the new Document Store feature of MySQL 8 and build applications around a mix of the best features from SQL and NoSQL database paradigms. Don’t allow yourself to be forced into one paradigm or the other, but combine both approaches by using the Document Store. MySQL 8 was designed from the beginning to bridge the gap between NoSQL and SQL. Oracle recognizes that many solutions need the capabilities of both. More specifically, developers need to store objects as loose collections of schema-less documents, but those same developers also need the ability to run structured queries on their data. With MySQL 8, you can do both! Introducing the MySQL 8 Document Store presents new tools and features that make creating a hybrid database solution far easier than ever before. This book covers the vitally important MySQL Document Store, the new X Protocol for developing applications, and a new client shell called the MySQL Shell. Also covered are supporting technologies and concepts such as JSON, schema-less documents, and more. The book gives insight into how features work and how to apply them to get the most out of your MySQL experience. The book covers topics such as: The headline feature in MySQL 8 MySQL's answer to NoSQL New APIs and client protocols What You'll Learn Create NoSQL-style applications by using the Document Store Mix the NoSQL and SQL approaches by using each to its best advantage in a hybrid solution Work with the new X Protocol for application connectivity in MySQL 8 Master the new X Developer Application Programming Interfaces Combine SQL and JSON in the same database and application Migrate existing applications to MySQL Document Store Who This Book Is For Developers and database professionals wanting to learn about the most profound paradigm-changing features of the MySQL 8 Document Store

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics

Why do businesses continue to use Microsoft’s BizTalk Server as the backbone to integrate line-of-business applications with their trading partners and how do recent changes make it even more effective? With the advent of Azure, we have a unique opportunity to enhance BizTalk functionality including reducing the cost of operations and maintenance. This book offers three solutions for the reader on ways to leverage BizTalk to get more from existing deployments or find ways to modernize the deployment via Azure. Microsoft partners are playing a significant role in enhancing the capabilities of BizTalk and this book includes sections that provide an in-depth review of BizTalk 360 © and the WPC HIPAA DB Toolkit ©. Over the recent past, Web 3.0 has also introduced many new concepts and open source technologies and this book covers ways to leverage these to enhance your BizTalk deployment. The authors start with a survey of the existing BizTalk Server – its history, patterns, and state of affairs –and go on to provide an in-depth elaboration of three messaging patterns that customers use for BizTalk; the advantages of updating to SQL Server 2016; a review of partner solutions that enhance BizTalk; and BizTalk with Web 3.0 for custom solutions. The book concludes with a comparison of the three viable BizTalk Azure application solutions that will enable you to make the best choice for your business.

Explore cutting-edge web optimization techniques in 'Mastering The Faster Web with PHP, MySQL, and JavaScript'. This comprehensive guide equips developers with the tools and knowledge to create lightning-fast web applications using modern technologies, including PHP 7, asynchronous programming, advanced SQL, and efficient JavaScript. What this Book will help me do Efficiently use profiling and benchmarking tools to identify performance bottlenecks. Optimize PHP 7 applications through efficient data structures and logical improvements. Enhance database performance by identifying and solving inefficient SQL queries. Incorporate modern asynchronous programming and functional programming techniques into your workflow. Integrate seamless UI designs that prioritize application responsiveness and user experience. Author(s) None Caya is a seasoned web developer with extensive experience in PHP, MySQL, and JavaScript. Through their career, they have delved deep into profiling, optimization techniques, and modern web technologies to deliver high-performance web solutions. This book reflects their commitment to providing actionable insights and practical advice to fellow developers. Who is it for? Ideal readers of this book are PHP developers with foundational knowledge in programming and web technologies who aspire to build and optimize modern web applications. Experience in JavaScript is not required, as the book covers essential aspects needed for performance enhancements. If you're aiming to hone your skills in creating faster web solutions, this book suits your goals perfectly.

Essential Microsoft® SQL Server® 2017 installation, configuration, and management techniques for Linux Foreword by Kalen Delaney, Microsoft SQL Server MVP This comprehensive guide shows, step-by-step, how to set up, configure, and administer SQL Server 2017 on Linux for high performance and high availability. Written by a SQL Server expert and respected author, Microsoft SQL Server 2017 on Linux teaches valuable Linux skills to Windows-based SQL Server professionals. You will get clear coverage of both Linux and SQL Server and complete explanations of the latest features, tools, and techniques. The book offers clear instruction on adaptive query processing, automatic tuning, disaster recovery, security, and much more. •Understand how SQL Server 2017 on Linux works •Install and configure SQL Server on Linux •Run SQL Server on Docker containers •Learn Linux Administration •Troubleshoot and tune query performance in SQL Server •Learn what is new in SQL Server 2017 •Work with adaptive query processing and automatic tuning techniques •Implement high availability and disaster recovery for SQL Server on Linux •Learn the security features available in SQL Server

Dive into the world of ETL (Extract, Transform, Load) with 'Hands-On Data Warehousing with Azure Data Factory'. This book guides readers through the essential techniques for working with Azure Data Factory and SQL Server Integration Services to design, implement, and optimize ETL solutions for both on-premises and cloud data environments. What this Book will help me do Understand and utilize Azure Data Factory and SQL Server Integration Services to build ETL solutions. Design scalable and high-performance ETL architectures tailored to modern data problems. Integrate various Azure services, such as Azure Data Lake Analytics, Machine Learning, and Databricks Spark, into your workflows. Troubleshoot and optimize ETL pipelines and address common challenges in data processing. Create insightful Power BI dashboards to visualize and interact with data from your ETL workflows. Author(s) Authors None Cote, Michelle Gutzait, and Giuseppe Ciaburro bring a wealth of experience in data engineering and cloud technologies to this practical guide. Combining expertise in Azure ecosystem and hands-on Data Warehousing, they deliver actionable insights for working professionals. Who is it for? This book is crafted for software professionals working in data engineering, especially those specializing in ETL processes. Readers with a foundational knowledge of SQL Server and cloud infrastructures will benefit most. If you aspire to implement state-of-the-art ETL pipelines or enhance existing workflows with ADF and SSIS, this book is an ideal resource.

PostgreSQL 10 High Performance provides you with all the tools to maximize the efficiency and reliability of your PostgreSQL 10 database. Written for database admins and architects, this book offers deep insights into optimizing queries, configuring hardware, and managing complex setups. By integrating these best practices, you'll ensure scalability and stability in your systems. What this Book will help me do Optimize PostgreSQL 10 queries for improved performance and efficiency. Implement database monitoring systems to identify and resolve issues proactively. Scale your database by implementing partitioning, replication, and caching strategies. Understand PostgreSQL hardware compatibility and configuration for maximum throughput. Learn how to design high-performance solutions tailored for large and demanding applications. Author(s) Enrico Pirozzi is a seasoned database professional with extensive experience in PostgreSQL management and optimization. Having worked on large-scale database infrastructures, Enrico shares his hands-on knowledge and practical advice for achieving high performance with PostgreSQL. His approachable style makes complex topics accessible to every reader. Who is it for? This book is intended for database administrators and system architects who are working with or planning to adopt PostgreSQL 10. Readers should have a foundational knowledge of SQL and some prior exposure to PostgreSQL. If you're aiming to design efficient, scalable database solutions while ensuring high availability, this book is for you.

A Deep Dive into NoSQL Databases: The Use Cases and Applications, Volume 109, the latest release in the Advances in Computers series first published in 1960, presents detailed coverage of innovations in computer hardware, software, theory, design and applications. In addition, it provides contributors with a medium in which they can explore their subjects in greater depth and breadth. This update includes sections on NoSQL and NewSQL databases for big data analytics and distributed computing, NewSQL databases and scalable in-memory analytics, NoSQL web crawler application, NoSQL Security, a Comparative Study of different In-Memory (No/New)SQL Databases, NoSQL Hands On-4 NoSQLs, the Hadoop Ecosystem, and more. Provides a very comprehensive, yet compact, book on the popular domain of NoSQL databases for IT professionals, practitioners and professors Articulates and accentuates big data analytics and how it gets simplified and streamlined by NoSQL database systems Sets a stimulating foundation with all the relevant details for NoSQL database researchers, developers and administrators

Write queries using little-known, but powerful, SQL features implemented in Oracle's database engine. You will be able to take advantage of Oracle’s power in implementing business logic, thereby maximizing return from your company’s investment in Oracle Database products. Important features and aspects of SQL covered in this book include the model clause, row pattern matching, analytic and aggregate functions, and recursive subquery factoring, just to name a few. The focus is on implementing business logic in pure SQL, with a comparison of different approaches that can be used to write SELECT statements to return results that drive good decision making and competitive action in the marketplace. This book covers features that are often not well known, and sometimes not implemented in competing products. Chapters on query transformation and logical execution order provide a grasp of the big picture in which the individual SQL features described in the other chapters are executed. Also included are a discussion on when to use the procedural capabilities from PL/SQL, and a series of examples showing different mixes of SQL features being applied in common types of queries that you are likely to encounter. What You Will Learn Gain competitive advantage from Oracle SQL Know when to step up to PL/SQL versus staying in SQL Become familiar with query transformations and join mechanics Apply the model clause and analytic functions to business intelligence queries Make use of features that are specific to Oracle Database, such as row pattern matching Understand the pros and cons of different SQL approaches to solving common query tasks Traverse hierarchies using CONNECT BY and recursive subquery factoring Who This Book Is For Database programmers withsome Oracle Database experience. The book is also for SQL developers who are moving to the Oracle Database platform or want to learn unique features of its query engine. Both audiences will learn to apply the full power of Oracle’s own SQL dialect to commonly encountered types of business questions and query challenges.