Design great databases—from logical data modeling through physical schema definition. You will learn a framework that finally cracks the problem of merging data and process models into a meaningful and unified design that accounts for how data is actually used in production systems. Key to the framework is a method for taking the logical data model that is a static look at the definition of the data, and merging that static look with the process models describing how the data will be used in actual practice once a given system is implemented. The approach solves the disconnect between the static definition of data in the logical data model and the dynamic flow of the data in the logical process models. The design framework in this book can be used to create operational databases for transaction processing systems, or for data warehouses in support of decision support systems. The information manager can be a flat file, Oracle Database, IMS, NoSQL, Cassandra, Hadoop, or any other DBMS. Usage-Driven Database Design emphasizes practical aspects of design, and speaks to what works, what doesn't work, and what to avoid at all costs. Included in the book are lessons learned by the author over his 30+ years in the corporate trenches. Everything in the book is grounded on good theory, yet demonstrates a professional and pragmatic approach to design that can come only from decades of experience. Presents an end-to-end framework from logical data modeling through physical schema definition. Includes lessons learned, techniques, and tricks that can turn a database disaster into a success. Applies to all types of database management systems, including NoSQL such as Cassandra and Hadoop, and mainstream SQL databases such as Oracle and SQL Server What You'll Learn Create logical data models that accurately reflect the real world of the user Create usage scenarios reflecting how applications will use a new database Merge static data models with dynamic process models to create resilient yet flexible database designs Support application requirements by creating responsive database schemas in any database architecture Cope with big data and unstructured data for transaction processing and decision support systems Recognize when relational approaches won't work, and when to turn toward NoSQL solutions such as Cassandra or Hadoop Who This Book Is For System developers, including business analysts, database designers, database administrators, and application designers and developers who must design or interact with database systems

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning About This Book Build data science and data engineering solutions with ease An in-depth look at each stage of the data analysis process — from reading and collecting data to distributed analytics Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulations, and source code Who This Book Is For This learning path is perfect for those who are comfortable with Scala programming and now want to enter the field of data science. Some knowledge of statistics is expected. What You Will Learn Transfer and filter tabular data to extract features for machine learning Read, clean, transform, and write data to both SQL and NoSQL databases Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Load data from HDFS and HIVE with ease Run streaming and graph analytics in Spark for exploratory analysis Bundle and scale up Spark jobs by deploying them into a variety of cluster managers Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Master probabilistic models for sequential data In Detail Scala is especially good for analyzing large sets of data as the scale of the task doesn’t have any significant impact on performance. Scala’s powerful functional libraries can interact with databases and build scalable frameworks — resulting in the creation of robust data pipelines. The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Using real world examples, you will learn how to design scalable architecture to process and model data — starting from simple concurrency constructs and progressing to actor systems and Apache Spark. After this, you will also learn how to build interactive visualizations with web frameworks. Once you have become familiar with all the tasks involved in data science, you will explore data analytics with Scala in the second module. You’ll see how Scala can be used to make sense of data through easy to follow recipes. You will learn about Bokeh bindings for exploratory data analysis and quintessential machine learning with algorithms with Spark ML library. You’ll get a sufficient understanding of Spark streaming, machine learning for streaming data, and Spark graphX. Armed with a firm understanding of data analysis, you will be ready to explore the most cutting-edge aspect of data science — machine learning. The final module teaches you the A to Z of machine learning with Scala. You’ll explore Scala for dependency injections and implicits, which are used to write machine learning algorithms. You’ll also explore machine learning topics such as clustering, dimentionality reduction, Naïve Bayes, Regression models, SVMs, neural networks, and more. This learning path combines some of the best that Packt has to offer into one complete, curated package. It includes content from the following Packt products: Scala for Data Science, Pascal Bugnion Scala Data Analysis Cookbook, Arun Manivannan Scala for Machine Learning, Patrick R. Nicolas Style and approach A complete package with all the information necessary to start building useful data engineering and data science solutions straight away. It contains a diverse set of recipes that cover the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

"HBase High Performance Cookbook" is your guide to mastering the optimization, scaling, and tuning of HBase systems. Covering everything from configuring HBase clusters to designing scalable table structures and performance tuning, this comprehensive book provides practical advice and strategies for leveraging HBase's full potential. By following this book's recipes, you'll supercharge your HBase expertise. What this Book will help me do Understand how to configure HBase for optimal performance, improving your data system's efficiency. Learn to design table structures to maximize scalability and functionality in HBase. Gain skills in performing CRUD operations and using advanced features like MapReduce within HBase. Discover practices for integrating HBase with other technologies such as ElasticSearch. Master the steps involved in setting up and optimizing HBase in cloud environments for enhanced performance. Author(s) Ruchir Choudhry is a seasoned data management professional with extensive experience in distributed database systems. He possesses deep expertise in HBase, Hadoop, and other big data technologies. His practical and engaging writing style aims to demystify complex technical topics, making them accessible to developers and architects alike. Who is it for? This book is tailored for developers and system architects looking to deepen their understanding of HBase. Whether you are experienced with other NoSQL databases or are new to HBase, this book provides extensive practical knowledge. Ideal for professionals working in big data applications or those eager to optimize and scale their database systems effectively.

Leverage Phoenix as an ANSI SQL engine built on top of the highly distributed and scalable NoSQL framework HBase. Learn the basics and best practices that are being adopted in Phoenix to enable a high write and read throughput in a big data space. This book includes real-world cases such as Internet of Things devices that send continuous streams to Phoenix, and the book explains how key features such as joins, indexes, transactions, and functions help you understand the simple, flexible, and powerful API that Phoenix provides. Examples are provided using real-time data and data-driven businesses that show you how to collect, analyze, and act in seconds. Pro Apache Phoenix covers the nuances of setting up a distributed HBase cluster with Phoenix libraries, running performance benchmarks, configuring parameters for production scenarios, and viewing the results. The book also shows how Phoenix plays well with other key frameworks in the Hadoop ecosystem such as Apache Spark, Pig, Flume, and Sqoop. You will learn how to: Handle a petabyte data store by applying familiar SQL techniques Store, analyze, and manipulate data in a NoSQL Hadoop echo system with HBase Apply best practices while working with a scalable data store on Hadoop and HBase Integrate popular frameworks (Apache Spark, Pig, Flume) to simplify big data analysis Demonstrate real-time use cases and big data modeling techniques Who This Book Is For Data engineers, Big Data administrators, and architects

Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards. This book will help those familiar with Tableau software chart their journey to being a visualization expert. Pro Tableau demonstrates the power of visual analytics and teaches you how to: Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc. Write your own custom SQL, etc. Perform statistical analysis in Tableau using R Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.) What you'll learn Connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc. Leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc. Integrate Tableau with R Tell a compelling story with data by creating highly interactive dashboards Who this book is for All levels of IT professionals, from executives responsible for determining IT strategies to systems administrators, to data analysts, to decision makers responsible for driving strategic initiatives, etc. The book will help those familiar with Tableau software chart their journey to a visualization expert.

Learn the fundamental foundations and concepts of the Apache HBase (NoSQL) open source database. It covers the HBase data model, architecture, schema design, API, and administration. Apache HBase is the database for the Apache Hadoop framework. HBase is a column family based NoSQL database that provides a flexible schema model. What You'll Learn Work with the core concepts of HBase Discover the HBase data model, schema design, and architecture Use the HBase API and administration Who This Book Is For Apache HBase (NoSQL) database users, designers, developers, and admins.

Get started with the Hibernate 5 persistence layer and gain a clear introduction to the current standard for object-relational persistence in Java. This updated edition includes the new Hibernate 5.0 framework as well as coverage of NoSQL, MongoDB, and other related technologies, ranging from applications to big data. Beginning Hibernate is ideal if you're experienced in Java with databases (the traditional, or connected, approach), but new to open-source, lightweight Hibernate. The book keeps its focus on Hibernate without wasting time on nonessential third-party tools, so you'll be able to immediately start building transaction-based engines and applications. Experienced authors Joseph Ottinger with Dave Minter and Jeff Linwood provide more in-depth examples than any other book for Hibernate beginners. They present their material in a lively, example-based manner—not a dry, theoretical, hard-to-read fashion. What You'll Learn Build enterprise Java-based transaction-type applications that access complex data with Hibernate Work with Hibernate 5 using a present-day build process Use Java 8 features with Hibernate Integrate into the persistence life cycle Map using Java's annotations Search and query with the new version of Hibernate Integrate with MongoDB using NoSQL Keep track of versioned data with Hibernate Envers Who This Book Is For Experienced Java developers interested in learning how to use and apply object-relational persistence in Java and who are new to the Hibernate persistence framework.

Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. In just 24 lessons of one hour or less, Sams Teach Yourself Apache Spark in 24 Hours helps you build practical Big Data solutions that leverage Spark’s amazing speed, scalability, simplicity, and versatility. This book’s straightforward, step-by-step approach shows you how to deploy, program, optimize, manage, integrate, and extend Spark–now, and for years to come. You’ll discover how to create powerful solutions encompassing cloud computing, real-time stream processing, machine learning, and more. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success. Whether you are a data analyst, data engineer, data scientist, or data steward, learning Spark will help you to advance your career or embark on a new career in the booming area of Big Data. Learn how to • Discover what Apache Spark does and how it fits into the Big Data landscape • Deploy and run Spark locally or in the cloud • Interact with Spark from the shell • Make the most of the Spark Cluster Architecture • Develop Spark applications with Scala and functional Python • Program with the Spark API, including transformations and actions • Apply practical data engineering/analysis approaches designed for Spark • Use Resilient Distributed Datasets (RDDs) for caching, persistence, and output • Optimize Spark solution performance • Use Spark with SQL (via Spark SQL) and with NoSQL (via Cassandra) • Leverage cutting-edge functional programming techniques • Extend Spark with streaming, R, and Sparkling Water • Start building Spark-based machine learning and graph-processing applications • Explore advanced messaging technologies, including Kafka • Preview and prepare for Spark’s next generation of innovations Instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid pitfalls. By the time you're finished, you'll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.

Fragmented, disparate backend data systems have become the norm in today’s enterprise, where you’ll find a mix of relational databases, Hadoop stores, and NoSQL engines, with access and analytics tools bolted on every which way. This mishmash of options presents a real challenge when it comes to choosing frontend analytics and visualization tools. How did we get here? In this O’Reilly report, IT veteran Rich Morrow takes you through the rapid changes to both backend storage and frontend analytics over the past decade, and provides a pragmatic list of requirements for an analytics stack that will centralize access to all of these data systems. You’ll examine current analytics platforms, including Looker—a new breed of analytics and visualization tools built specifically to handle our fragmented data space. Understand why and how data became so fractured so quickly Explore the tangled web of data and backend tools in today’s enterprises Learn the tool requirements for accessing and analyzing the full spectrum of data Examine the relative strengths of popular analytics and visualization tools, including Looker, Tableau, and MicroStrategy Inspect Looker’s unique focus on both the frontend and backend

The database pendulum is in full swing. Ten years ago, web-scale companies began moving away from proprietary relational databases to handle big data use cases with NoSQL and Hadoop. Now, for a variety of reasons, the pendulum is swinging back toward SQL-based solutions. What many companies really want is a system that can handle all of their operational, OLTP, BI, and analytic workloads. Could such an all-in-one database exist? This O’Reilly report examines this quest for database nirvana, or what Gartner recently dubbed Hybrid Transaction/Analytical Processing (HTAP). Author Rohit Jain takes an in-depth look at the possibilities and the challenges for companies that long for a single query engine to rule them all. With this report, you’ll explore: The challenges of having one query engine support operational, BI, and analytical workloads Efforts to produce a query engine that supports multiple storage engines Attempts to support multiple data models with the same query engine Why an HTAP database engine needs to provide enterprise-caliber capabilities, including high availability, security, and manageability How to assess various options for meeting workload requirements with one database engine, or a combination of query and storage engines

Re-architect relational applications to NoSQL, integrate relational database management systems with the Hadoop ecosystem, and transform and migrate relational data to and from Hadoop components. This book covers the best-practice design approaches to re-architecting your relational applications and transforming your relational data to optimize concurrency, security, denormalization, and performance. Winner of IBM's 2012 Gerstner Award for his implementation of big data and data warehouse initiatives and author of Practical Hadoop Security, author Bhushan Lakhe walks you through the entire transition process. First, he lays out the criteria for deciding what blend of re-architecting, migration, and integration between RDBMS and HDFS best meets your transition objectives. Then he demonstrates how to design your transition model. Lakhe proceeds to cover the selection criteria for ETL tools, the implementation steps for migration with SQOOP- and Flume-based data transfers, and transition optimization techniques for tuning partitions, scheduling aggregations, and redesigning ETL. Finally, he assesses the pros and cons of data lakes and Lambda architecture as integrative solutions and illustrates their implementation with real-world case studies. Hadoop/NoSQL solutions do not offer by default certain relational technology features such as role-based access control, locking for concurrent updates, and various tools for measuring and enhancing performance. Practical Hadoop Migration shows how to use open-source tools to emulate such relational functionalities in Hadoop ecosystem components. What You'll Learn Decide whether you should migrate your relational applications to big data technologies or integrate them Transition your relational applications to Hadoop/NoSQL platforms in terms of logical design and physical implementation Discover RDBMS-to-HDFS integration, data transformation, and optimization techniques Consider when to use Lambda architecture and data lake solutions Select and implement Hadoop-based components and applications to speed transition, optimize integrated performance, and emulate relational functionalities Who This Book Is For Database developers, database administrators, enterprise architects, Hadoop/NoSQL developers, and IT leaders. Its secondary readership is project and program managers and advanced students of database and management information systems.

Unlock the potential of big data analytics by mastering R programming with this comprehensive guide. This book takes you step-by-step through real-world scenarios where R's capabilities shine, providing you with practical skills to handle, process, and analyze large and complex datasets effectively. What this Book will help me do Understand the latest big data processing methods and how R can enhance their application. Set up and use big data platforms such as Hadoop and Spark in conjunction with R. Utilize R for practical big data problems, such as analyzing consumption and behavioral datasets. Integrate R with SQL and NoSQL databases to maximize its versatility in data management. Discover advanced machine learning implementations using R and Spark MLlib for predictive analytics. Author(s) None Walkowiak is an experienced data analyst and R programming expert with a passion for data engineering and machine learning. With a deep knowledge of big data platforms and extensive teaching experience, they bring a clear and approachable writing style to help learners excel. Who is it for? Ideal for data analysts, scientists, and engineers with fundamental data analysis knowledge looking to enhance their big data capabilities using R. If you aim to adapt R for large-scale data management and analysis workflows, this book is your ideal companion to bridge the gap.

Get started on mastering the one language binding the entire database industry. That language is SQL, and how it works is must-have knowledge for anyone involved with relational databases, and surprisingly also for anyone involved with NoSQL databases. SQL is universally used in querying and reporting on large data sets in order to generate knowledge to drive business decisions. Good knowledge of SQL is crucial to anyone working with databases, because it is with SQL that you retrieve data, manipulate data, and generate business results. Every relational database supports SQL for its expressiveness in writing queries underlying reports and business intelligence dashboards. Knowing how to write good queries is the foundation for all work done in SQL, and it is a foundation that Clare Churcher's book, , 2nd Edition, lays well. Beginning SQL Queries What You Will Learn Write simple queries to extract data from a single table Combine data from many tables into one business result using set operations Translate natural language questions into database queries providing meaningful information to the business Avoid errors associated with duplicated and null values Summarize data with amazing ease using the newly-added feature of window functions Tackle tricky queries with confidence that you are generating correct results Investigate and understand the effects of indexes on the efficiency of queries Who This Book Is For Beginning SQL Queries, 2nd Edition is aimed at intelligent laypeople who need to extract information from a database, and at developers and other IT professionals who are new to SQL. The book is especially useful for business intelligence analysts who must ask more complex questions of their database than their GUI—based reporting software supports. Such people might be business owners wanting to target specific customers, scientists and students needing to extract subsets of their research data, or end users wanting to make the best use of databases for their clubs and societies.

"Mastering Redis" is your comprehensive guide to truly leveraging the power of the Redis data structure server. This hands-on resource offers detailed insights into scaling data with Redis clusters, optimizing memory, scripting with Lua, and integrating Redis with other NoSQL technologies to create robust, efficient applications. What this Book will help me do Select and utilize the appropriate Redis data structure to solve specific use cases efficiently. Implement Lua scripts on Redis for complex workflows and custom functionality. Optimize Redis configurations to achieve efficient memory usage and server performance. Integrate Redis with other NoSQL databases, such as MongoDB and Elasticsearch, for enhanced capabilities. Set up Redis Clusters and use Redis Sentinel for distributed and highly available setups. Author(s) Vidyasagar N V and None Nelson bring a wealth of expertise in software development and distributed systems to this book. Vidyasagar has extensive hands-on experience with Redis, enabling him to provide practical insights and best practices. Nelson complements this with deep knowledge of database optimization, making their combined perspective invaluable for anyone diving deep into Redis. Who is it for? This book is aimed at software developers who have an understanding of Redis basics and want to advance their proficiency. It is also targeted at developers aiming to implement Redis in production efficiently. By reading this book, readers will deepen their Redis skills and learn how to integrate it with other technologies to develop scalable, high-performance applications.

Mastering Hibernate is your comprehensive guide to understanding and mastering Hibernate, a powerful Object-Relational Mapping tool for Java and .Net applications. Through this book, you will dive deep into the mechanics of Hibernate, exploring its core concepts and architecture. Whether you're working with SQL or NoSQL data stores, this book ensures you can unlock Hibernate's full potential. What this Book will help me do Grasp the internal workings of Hibernate, including its session management and entity lifecycle. Optimize mapping between Java classes and relational database structures for better performance. Effectively manage relationships and collections within your data models using Hibernate features. Utilize Hibernate's caching systems to improve application performance and scalability. Handle multi-tenant database configurations with confidence using Hibernate's architectural capabilities. Author(s) None Rad is an experienced software developer and educator specializing in Java-based applications and enterprise architecture. With years of hands-on practice using Hibernate in real-world scenarios, None Rad has curated this book to serve as a clear and practical guide. Their writing reflects deep technical expertise combined with an approachable and illustrative teaching style, ensuring learning is both effective and engaging. Who is it for? This book is ideal for software developers and engineers who are familiar with Java or other similar object-oriented programming languages. Whether you're a professional looking to deepen your understanding of Hibernate's internals or a developer aiming to create more efficient ORM solutions, this book has something for you. Readers should have a basic understanding of Java and relational databases, but no prior Hibernate expertise is required. By the end, you'll be equipped to confidently apply Hibernate to sophisticated data challenges.

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You'll explore data visualization, graph databases, the use of NoSQL, and the data science process. You'll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you'll have the solid foundation you need to start a career in data science. What's Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Quotes Read this book if you want to get a quick overview of data science, with lots of examples to get you started! - Alvin Raj, Oracle The map that will help you navigate the data science oceans. - Marius Butuc, Shopify Covers the processes involved in data science from end to end… A complete overview. - Heather Campbell, Kainos A must-read for anyone who wants to get into the data science world. - Hector Cuesta, Big Data Bootcamp

Relational Database Design and Implementation: Clearly Explained, Fourth Edition, provides the conceptual and practical information necessary to develop a database design and management scheme that ensures data accuracy and user satisfaction while optimizing performance. Database systems underlie the large majority of business information systems. Most of those in use today are based on the relational data model, a way of representing data and data relationships using only two-dimensional tables. This book covers relational database theory as well as providing a solid introduction to SQL, the international standard for the relational database data manipulation language. The book begins by reviewing basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL. Topics such as the relational data model, normalization, data entities, and Codd's Rules (and why they are important) are covered clearly and concisely. In addition, the book looks at the impact of big data on relational databases and the option of using NoSQL databases for that purpose. Features updated and expanded coverage of SQL and new material on big data, cloud computing, and object-relational databases Presents design approaches that ensure data accuracy and consistency and help boost performance Includes three case studies, each illustrating a different database design challenge Reviews the basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL

GET MORE WITH MANNING An eBook copy of the previous edition, MongoDB in Action (First Edition), is included at no additional cost. It will be automatically added to your Manning Bookshelf within 24 hours of purchase. MongoDB in Action, Second Edition is a completely revised and updated version. It introduces MongoDB 3.0 and the document-oriented database model. This perfectly paced book gives you both the big picture you'll need as a developer and enough low-level detail to satisfy system engineers. About the Technology This document-oriented database was built for high availability, supports rich, dynamic schemas, and lets you easily distribute data across multiple servers. MongoDB 3.0 is flexible, scalable, and very fast, even with big data loads. About the Book MongoDB in Action, Second Edition is a completely revised and updated version. It introduces MongoDB 3.0 and the document-oriented database model. This perfectly paced book gives you both the big picture you'll need as a developer and enough low-level detail to satisfy system engineers. Lots of examples will help you develop confidence in the crucial area of data modeling. You'll also love the deep explanations of each feature, including replication, auto-sharding, and deployment. What's Inside Indexes, queries, and standard DB operations Aggregation and text searching Map-reduce for custom aggregations and reporting Deploying for scale and high availability Updated for Mongo 3.0 About the Reader Written for developers. No previous MongoDB or NoSQL experience is assumed. About the Authors After working at MongoDB, Kyle Banker is now at a startup. Peter Bakkum is a developer with MongoDB expertise. Shaun Verch has worked on the core server team at MongoDB. A Genentech engineer, Doug Garrett is one of the winners of the MongoDB Innovation Award for Analytics. A software architect, Tim Hawkins has led search engineering at Yahoo Europe. Technical Contributor: Wouter Thielen Technical Editor: Mihalis Tsoukalos Quotes A thorough manual for learning, practicing, and implementing MongoDB - Jeet Marwah, Acer Inc. A must-read to properly use MongoDB and model your data in the best possible way. - Hernan Garcia, Betterez Inc. Provides all the necessary details to get you jump-started with MongoDB. - Gregor Zurowski, Independent Software Development Consultant Awesome! MongoDB in a nutshell. - Hardy Ferentschik, Red Hat

Dive into the world of NoSQL databases with RethinkDB, a modern and powerful document-oriented database designed for developing real-time applications. Through this book, you'll explore essential RethinkDB features and learn how to integrate it seamlessly with Node.js, enabling you to build and deploy responsive web apps. What this Book will help me do Master the basics of installing and configuring RethinkDB on your system. Learn how to use the intuitive ReQL language to perform complex queries. Set up and manage RethinkDB clusters by mastering sharding and replication. Optimize database performance using indexing and advanced query techniques. Develop interactive real-time applications by integrating RethinkDB with Node.js. Author(s) None Tiepolo is an experienced developer and educator specializing in real-time database technologies. With extensive expertise in NoSQL solutions and hands-on experience in software engineering, Tiepolo combines a teacher's clarity with a programmer's practicality to make complex topics accessible. Who is it for? This book is tailored for developers eager to grasp RethinkDB, particularly those with an interest in building real-time applications. If you are new to database programming, you'll find accessible guidance here. Developers with basic experience in JavaScript or Node.js will gain further insights into real-world applications of these skills.