The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you’ll discover: Deep learning from first principles The latest features of Keras 3 A primer on JAX, PyTorch, and TensorFlow Image classification and image segmentation Time series forecasting Large Language models Text classification and machine translation Text and image generation—build your own GPT and diffusion models! Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the Technology In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the Book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's Inside Hands-on, code-first learning Comprehensive, from basics to generative AI Intuitive and easy math explanations Examples in Keras, PyTorch, JAX, and TensorFlow About the Reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the Authors François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Quotes Perfect for anyone interested in learning by doing from one of the industry greats. - Anthony Goldbloom, Founder of Kaggle A sharp, deeply practical guide that teaches you how to think from first principles to build models that actually work. - Santiago Valdarrama, Founder of ml.school The most up-to-date and complete guide to deep learning you’ll find today! - Aran Komatsuzaki, EleutherAI Masterfully conveys the true essence of neural networks. A rare case in recent years of outstanding technical writing. - Salvatore Sanfilippo, Creator of Redis

Security teams rely on telemetry—the continuous stream of logs, events, metrics, and signals that reveal what’s happening across systems, endpoints, and cloud services. But that data doesn’t organize itself. It has to be collected, normalized, enriched, and secured before it becomes useful. That’s where data engineering comes in. In this hands-on guide, cybersecurity engineer James Bonifield teaches you how to design and build scalable, secure data pipelines using free, open source tools such as Filebeat, Logstash, Redis, Kafka, and Elasticsearch and more. You’ll learn how to collect telemetry from Windows including Sysmon and PowerShell events, Linux files and syslog, and streaming data from network and security appliances. You’ll then transform it into structured formats, secure it in transit, and automate your deployments using Ansible. You’ll also learn how to: Encrypt and secure data in transit using TLS and SSH Centrally manage code and configuration files using Git Transform messy logs into structured events Enrich data with threat intelligence using Redis and Memcached Stream and centralize data at scale with Kafka Automate with Ansible for repeatable deployments Whether you’re building a pipeline on a tight budget or deploying an enterprise-scale system, this book shows you how to centralize your security data, support real-time detection, and lay the groundwork for incident response and long-term forensics.

In "Redis Stack for Application Modernization," you will explore how the Redis Stack extends traditional Redis capabilities, allowing you to innovate in building real-time, scalable, multi-model applications. Through practical examples and hands-on sessions, this book equips you with skills to manage, implement, and optimize data flows and database features. What this Book will help me do Learn how to use Redis Stack for handling real-time data with JSON, hash, and other document types. Discover modern techniques for performing vector similarity searches and hybrid workflows. Become proficient in integrating Redis Stack with programming languages like Java, Python, and Node.js. Gain skills to configure Redis Stack server for scalability, security, and high availability. Master RedisInsight for data visualization, analysis, and efficient database management. Author(s) Luigi Fugaro and None Ortensi are experienced software professionals with deep expertise in database systems and application architecture. They bring years of experience working with Redis and developing real-world applications. Their hands-on approach to teaching and real-world examples make this book a valuable resource for professionals in the field. Who is it for? This book is ideal for database administrators, developers, and architects looking to leverage Redis Stack for real-time multi-model applications. It requires a basic understanding of Redis and any programming language such as Python or Java. If you wish to modernize your applications and efficiently manage databases, this book is for you.

Master web development with the FARM stack in this comprehensive guide. You'll learn to harness FastAPI for a secure and efficient backend, React for a dynamic frontend, and MongoDB for flexible data storage. Gain practical experience by building fully functional projects that you can deploy and fine-tune, opening doors to enhanced proficiency in modern web technologies. What this Book will help me do Build secure and performant backends using FastAPI and understand its integration with MongoDB. Develop responsive and dynamic user interfaces with React and incorporate server-side rendering for improved SEO. Explore the intricacies of deploying full-stack applications on platforms like Heroku and Netlify. Implement robust user authentication systems with JSON Web Tokens for securing your applications. Apply caching strategies with Redis to enhance the performance and scalability of applications. Author(s) Marko Aleksendrić, the author of this book, combines years of experience in software development with a passion for teaching. Specializing in full-stack web technologies, Marko has a track record of guiding developers in mastering modern tools like FastAPI and React. His practical approach focuses on equipping readers with real-world skills through projects and best practices. Who is it for? This book is ideal for developers with foundational knowledge in Python, JavaScript, and web basics who want to expand their expertise into full-stack development. Whether you're a professional seeking to enhance your project toolkit or a beginner aiming to tackle modern web applications, this guide provides a step-by-step approach tailored to your growth.

Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow. Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compilereusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes. ​Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production. ​ What You Will Learn Simplify data transformation with Spark Pipelines and Spark SQL Bridge data engineering with machine learning Architect modular data pipeline applications Build reusable application components and libraries Containerize your Spark applications for consistency and reliability Use Docker and Kubernetes to deploy your Spark applications Speed up application experimentation using Apache Zeppelin and Docker Understand serializable structured data and data contracts Harness effective strategies for optimizing data in your data lakes Build end-to-end Spark structured streaming applications using Redis and Apache Kafka Embrace testing for your batch and streaming applications Deploy and monitor your Spark applications Who This Book Is For Professional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness anduse Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world

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.

Learn the fundamentals of seven essential NoSQL databases in just one week with this book. Covering MongoDB, DynamoDB, Redis, Cassandra, Neo4j, InfluxDB, and HBase, you'll explore their functionalities and practical applications. Designed to give you a working understanding of NoSQL database types, this guide helps aspiring DBAs and developers comprehend and utilize modern data solutions. What this Book will help me do Master the fundamentals of MongoDB, including high-performance, high-availability, and scaling features. Gain hands-on experience with Neo4j to perform database queries and integrate with Python and Java applications. Learn efficient querying with Redis for storage and retrieval tasks. Understand Cassandra's powerful solution for scalable and fault-tolerant systems. Get well-versed with HBase for creating tables, and reading and writing data efficiently. Author(s) Sudarshan Kadambi and Xun (Brian) Wu bring a wealth of experience in database technologies. They have worked extensively in the software development and database management fields. With their practical and concise teaching approach, the authors make complex topics accessible for readers. Who is it for? This book is ideal for budding DBAs and developers looking to understand NoSQL databases. It is particularly useful for those transitioning from relational databases who want to learn about modern database technologies. Suitable for both beginners and those with some database knowledge, it aims to bridge skill gaps and expand the reader's technical expertise.

Redis 4.x Cookbook offers practical solutions for developers and administrators to master Redis, a popular key-value database. This book contains over 80 step-by-step recipes covering topics like installation, replication, high availability, and troubleshooting, making it an indispensable resource for enhancing your Redis expertise. What this Book will help me do Master the installation and configuration of a Redis instance for optimal setups. Learn how to use Redis data types effectively in various application scenarios. Implement replication and high availability to ensure reliability and scale. Gain skills to troubleshoot, benchmark, and fine-tune Redis deployments. Extend Redis functionalities with modules for custom needs. Author(s) The authors of Redis 4.x Cookbook are seasoned database administrators and developers with extensive expertise in Redis and distributed systems. Their practical experience shapes this book, offering proven insights and techniques. They are adept at conveying technical concepts in an engaging and clear manner. Who is it for? This book is ideal for developers, database administrators, and architects familiar with basic Redis concepts who want a comprehensive guide to address advanced Redis tasks. Readers seeking to implement, optimize, and troubleshoot Redis in production environments will find this resource invaluable.

Mastering Apache Storm is your step-by-step guide to mastering real-time data streaming with this robust framework. You'll learn how to process big data efficiently and integrate Apache Storm with popular technologies like Kafka, HBase, and Redis to maximize its potential. This book walks you through from basic concepts to advanced implementations of Apache Storm in real-world scenarios. What this Book will help me do Understand the core features and operation of Apache Storm for real-time data streaming. Integrate Apache Storm with other Big Data frameworks like Kafka, HBase, Redis, and Hadoop. Effectively deploy and manage multi-node Apache Storm clusters in real-world environments. Monitor and analyze your data streams and system health effectively using built-in and external tools. Learn to implement fault-tolerant, scalable, and distributed stream processing applications in Apache Storm. Author(s) None Jain is an experienced software developer and technical instructor specializing in distributed systems and real-time data processing. With years of experience working with Apache Storm and related technologies, their teachings focus on practical, hands-on learning to equip readers with actionable skills. Who is it for? This book is ideal for Java developers aspiring to build expertise in real-time data streaming and distributed processing applications using Apache Storm. Beginners can start with the fundamentals provided, while those with prior knowledge can delve into intermediate and advanced implementations.

Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects.

Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.

"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 Redmine Second Edition provides a comprehensive guide to the popular open source project management tool, Redmine. With this book, you'll gain a solid understanding of effective Redmine use, from installing and configuring to advanced customizations and integrations. Explore how to optimize your workflow and manage projects with clarity and precision. What this Book will help me do Confidently install and configure Redmine for your organization. Harness Redmine for effective issue tracking and project hosting. Understand and implement Redmine's rich text formatting and permissions systems. Utilize time tracking features and custom fields to enhance project management. Explore and integrate essential Redmine plugins for improved functionality. Author(s) Andriy Lesyuk, an experienced Redmine expert, brings years of hands-on experience managing and customizing Redmine instances. His passion for open source and practical approach to project management makes this guide an invaluable resource for learning Redmine. Who is it for? This book is ideal for project managers and Redmine administrators looking to deepen their understanding of Redmine. If you're familiar with the basics of Redmine and aim to optimize, customize, and expand its use, this guide is for you. Whether managing projects or improving team collaborations, you'll find actionable insights to elevate your use of Redmine.

Redis Essentials is your go-to guide for understanding and mastering Redis, the leading in-memory data structure store. In this book, you will explore the powerful features offered by Redis, such as real-time data processing, highly scalable architectures, and practical implementations for web applications. You'll complete the journey equipped to handle and optimize Redis for your development projects. What this Book will help me do Design analytics applications with advanced data structures like Bitmaps and HyperLogLogs. Scale your application infrastructure using Redis Sentinel, Twemproxy, and Redis Cluster. Develop custom Redis commands and extend its functionality with the Lua scripting language. Implement robust security measures for Redis, including SSL encryption and firewall rules. Master the usage of Redis client libraries in PHP, Python, Node.js, and Ruby for seamless development. Author(s) Maxwell Dayvson da Silva is an experienced software engineer and author with expertise in designing high-performance systems. With a strong focus on practical knowledge and hands-on solutions, Maxwell brings over a decade of experience using Redis to this book. His approachable teaching style ensures learners grasp complex topics easily while emphasizing their practical application to real-world challenges. Who is it for? Redis Essentials is aimed at developers looking to enhance their system's performance and scalability using Redis. Whether you're moderately familiar with key-value stores or new to Redis, this book will provide the explanations and hands-on examples you need. Recommended for developers with experience in data architectures, the book bridges the gap between understanding Redis features and their real-world application. Start here to bring high-performance in-memory data solutions to your projects.

Dive into Redis, and discover how this powerful key-value database can enhance your web and business applications. "Learning Redis" explains the fundamentals of Redis and walks you through the processes of installation, configuration, and hands-on application development. By the end, you will have gained the knowledge to apply Redis in innovative ways to improve scalability and performance. What this Book will help me do Understand and install Redis to start using this NoSQL database efficiently. Master Redis commands and data structures for scalable application designs. Configure and optimize a Redis server setup to enhance performance and manage persistence. Implement solutions using Redis for real-world web application development. Learn best practices for Redis backups, recovery, and fault management to ensure reliability. Author(s) Vinoo Das, a seasoned software engineer with extensive experience in database solutions, wrote this book with a practical approach to learning Redis. Known for his clear explanations, Vinoo deeply understands how to make technical concepts accessible. His professional career spans significant contributions to SQL and NoSQL database development, making him an ideal guide for learning Redis. Who is it for? This book is perfect for SQL developers ready to deepen their expertise by exploring NoSQL databases like Redis. With only basic programming experience required, readers will appreciate the practical approach and the focus on actionable insights. Whether you're looking to build high-performance apps or understand key-value storage systems, this book will meet your needs.

NoSQL was developed to overcome the limitations of relational databases in the largest Web applications at companies such as Google, Yahoo and Facebook. As it is applied more widely, developers are finding that it can simplify scalability while requiring far less coding and management overhead. However, NoSQL requires fundamentally different approaches to database design and modeling, and many conventional relational techniques lead to suboptimal results. NoSQL for Mere Mortals is an easy, practical guide to succeeding with NoSQL in your environment. Following the classic, best-selling format pioneered in SQL Queries for Mere Mortals, enterprise database expert Dan Sullivan guides you step-by-step through choosing technologies, designing high-performance databases, and planning for long-term maintenance. Sullivan introduces each type of NoSQL database, shows how to install and manage them, and demonstrates how to leverage their features while avoiding common mistakes that lead to poor performance and unmet requirements. He uses four popular NoSQL databases as reference models: MongoDB, a document database; Cassandra, a column family data store; Redis, a key-value database; and Neo4j, a graph database. You'll find explanations of each database's structure and capabilities, practical guidelines for choosing amongst them, and expert guidance on designing databases with them. Packed with examples, NoSQL for Mere Mortals is today's best way to master NoSQL—whether you're a DBA, developer, user, or student.

Making Big Data Work: Real-World Use Cases and Examples, Practical Code, Detailed Solutions Large-scale data analysis is now vitally important to virtually every business. Mobile and social technologies are generating massive datasets; distributed cloud computing offers the resources to store and analyze them; and professionals have radically new technologies at their command, including NoSQL databases. Until now, however, most books on “Big Data” have been little more than business polemics or product catalogs. is different: It’s a completely practical and indispensable guide for every Big Data decision-maker, implementer, and strategist. Data Just Right Michael Manoochehri, a former Google engineer and data hacker, writes for professionals who need practical solutions that can be implemented with limited resources and time. Drawing on his extensive experience, he helps you focus on building applications, rather than infrastructure, because that’s where you can derive the most value. Manoochehri shows how to address each of today’s key Big Data use cases in a cost-effective way by combining technologies in hybrid solutions. You’ll find expert approaches to managing massive datasets, visualizing data, building data pipelines and dashboards, choosing tools for statistical analysis, and more. Throughout, the author demonstrates techniques using many of today’s leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery. Coverage includes Mastering the four guiding principles of Big Data success—and avoiding common pitfalls Emphasizing collaboration and avoiding problems with siloed data Hosting and sharing multi-terabyte datasets efficiently and economically “Building for infinity” to support rapid growth Developing a NoSQL Web app with Redis to collect crowd-sourced data Running distributed queries over massive datasets with Hadoop, Hive, and Shark Building a data dashboard with Google BigQuery Exploring large datasets with advanced visualization Implementing efficient pipelines for transforming immense amounts of data Automating complex processing with Apache Pig and the Cascading Java library Applying machine learning to classify, recommend, and predict incoming information Using R to perform statistical analysis on massive datasets Building highly efficient analytics workflows with Python and Pandas Establishing sensible purchasing strategies: when to build, buy, or outsource Previewing emerging trends and convergences in scalable data technologies and the evolving role of the Data Scientist

Redis in Action introduces Redis and walks you through examples that demonstrate how to use it effectively. You'll begin by getting Redis set up properly and then exploring the key-value model. Then, you'll dive into real use cases including simple caching, distributed ad targeting, and more. You'll learn how to scale Redis from small jobs to massive datasets. Experienced developers will appreciate chapters on clustering and internal scripting to make Redis easier to use. About the Technology When you need near-real-time access to a fast-moving data stream, key-value stores like Redis are the way to go. Redis expands on the key-value pattern by accepting a wide variety of data types, including hashes, strings, lists, and other structures. It provides lightning-fast operations on in-memory datasets, and also makes it easy to persist to disk on the fly. Plus, it's free and open source. About the Book What's Inside Redis from the ground up Preprocessing real-time data Managing in-memory datasets Pub/sub and configuration Persisting to disk About the Reader Written for developers familiar with database concepts. No prior exposure to Redis or other NoSQL databases required. Appropriate for systems administrators comfortable with programming. About the Author Dr. Josiah L. Carlson is a seasoned database professional and an active contributor to the Redis community. Quotes A great addition to the Redis ecosystem. - From the Foreword by Salvatore Sanfilippo, Creator of Redis The examples, taken from real-world use cases, are one of the major strengths of the book. - Filippo Pacini, SG Consulting From beginner to expert with real and comprehensive examples. - Felipe Gutierrez, VMware/Spring Source Excellent in-depth analysis ... insightful real-world examples. - Bobby Abraham, Integri LLC Pure gold! - Leo Cassarani, Unboxed Consulting

Cloud services are just as susceptible to network outages as any other platform. This concise book shows you how to prepare for potentially devastating interruptions by building your own resilient and reliable applications in the public cloud. Guided by engineers from 9apps—an independent provider of Amazon Web Services and Eucalyptus cloud solutions—you’ll learn how to combine AWS with open source tools such as PostgreSQL, MongoDB, and Redis. This isn’t a book on theory. With detailed examples, sample scripts, and solid advice, software engineers with operations experience will learn specific techniques that 9apps routinely uses in its cloud infrastructures. Build cloud applications with the "rip, mix, and burn" approach Get a crash course on Amazon Web Services Learn the top ten tips for surviving outages in the cloud Use elasticsearch to build a dependable NoSQL data store Combine AWS and PostgreSQL to build an RDBMS that scales well Create a highly available document database with MongoDB Replica Set and SimpleDB Augment Redis with AWS to provide backup/restore, failover, and monitoring capabilities Work with CloudFront and Route 53 to safeguard global content delivery

Even as big data is turning the world upside down, the next phase of the revolution is already taking shape: real-time data analysis. This hands-on guide introduces you to Storm, a distributed, JVM-based system for processing streaming data. Through simple tutorials, sample Java code, and a complete real-world scenario, you’ll learn how to build fast, fault-tolerant solutions that process results as soon as the data arrives. Discover how easy it is to set up Storm clusters for solving various problems, including continuous data computation, distributed remote procedure calls, and data stream processing. Learn how to program Storm components: spouts for data input and bolts for data transformation Discover how data is exchanged between spouts and bolts in a Storm topology Make spouts fault-tolerant with several commonly used design strategies Explore bolts—their life cycle, strategies for design, and ways to implement them Scale your solution by defining each component’s level of parallelism Study a real-time web analytics system built with Node.js, a Redis server, and a Storm topology Write spouts and bolts with non-JVM languages such as Python, Ruby, and Javascript