Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. You'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You'll especially enjoy the feeling of satisfaction as you crack problems that connect computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview!
what's inside
Search algorithms
Common techniques for graphs
Neural networks
Genetic algorithms
Adversarial search
Uses type hints throughout
Covers Python 3.7

Enterprise Java Microservices teaches you to design and build JVM-based microservices applications. You'll start by learning how microservices designs compare to traditional Java EE applications. Always practical, author Ken Finnigan introduces big-picture concepts along with the tools and techniques you'll need to implement them. You'll discover ecosystem components like Netflix Hystrix for fault tolerance and master the Just enough Application Server (JeAS) approach. To ensure smooth operations, you'll also examine monitoring, security, testing, and deploying to the cloud.

GraphQL is a new way of structuring and building web services, and the result is transformational. Find out how to offer a more tailored, cohesive experience to your users, easily aggregate data from different data sources, and improve your back end’s maintainability with Absinthe’s declarative approach to defining how your API works.
Build a GraphQL-based API from scratch using Absinthe, starting from core principles. Learn the type system and how to expand your schema to suit your application’s needs. Discover a growing ecosystem of tools and utilities to understand, debug, and document your API. Take it to production, but do it safely with solid best practices in mind. Find out how complexity analysis and persisted queries can let you support your users flexibly, but responsibly too. Along the way, discover how Elixir makes all the difference for a high performance, fault-tolerant API. Use asynchronous and batching execution, or write your own custom add-ons to extend Absinthe. Go live with subscriptions, delivering data over websockets on top of Elixir (and Erlang/OTP’s) famous solid performance and real-time capabilities.
Transform your applications with the powerful combination of Elixir and GraphQL, using Absinthe.

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

PostGIS in Action, Third Edition teaches you to solve real-world geodata problems. It first gives you a background in vector-, raster-, and topology-based GIS and then quickly moves into analyzing, viewing, and mapping data. You'll learn how to optimize queries for maximum speed, simplify geometries for greater efficiency, and create custom functions for your own applications. You'll also learn how to apply your existing GIS knowledge to PostGIS and integrate with other GIS tools. Fully updated to the latest versions of PostGIS and PostgreSQL, this Third Edition covers new PostGIS features including Foreign Data Wrappers, Database as a Service, parallelization of queries, and new JSON and Vector Tiles functions that help in creating web mapping applications.
what's inside
An introduction to spatial databases
Geometry, geography, raster, and topology spatial types, functions, and queries
Applying PostGIS to real-world problems
Extending PostGIS to web and desktop applications
Updated for PostGIS 3 and PostgreSQL 12

Practical SQL is an approachable and fast-paced guide to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. The book focuses on using SQL to find the story your data tells, with the popular open-source database PostgreSQL and the pgAdmin interface as its primary tools.
You’ll first cover the fundamentals of databases and the SQL language, then build skills by analyzing data from the U.S. Census and other federal and state government agencies. With exercises and real-world examples in each chapter, this book will teach even those who have never programmed before all the tools necessary to build powerful databases and access information quickly and efficiently.
You’ll learn how to:
Create databases and related tables using your own data
Define the right data types for your information
Aggregate, sort, and filter data to find patterns
Use basic math and advanced statistical functions
Identify errors in data and clean them up
Import and export data using delimited text files
Write queries for geographic information systems (GIS)
Create advanced queries and automate tasks
Learning SQL doesn’t have to be dry and complicated. Practical SQL delivers clear examples with an easy-to-follow approach to teach you the tools you need to build and manage your own databases.
This book uses PostgreSQL, but the SQL syntax is applicable to many database applications, including Microsoft SQL Server and MySQL.

How Machine Learning Works gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore supervised and unsupervised learning. Blending theoretical foundations with practical ML skills, you’ll learn to read existing datasets using pandas, a fast and powerful Python library for data analysis and manipulation. Then, you’ll move on to choosing and implementing ML models with scikit-learn, a popular Python framework that provides a diverse range of ML models and algorithms.
Along the way, you’ll be practicing important math skills, including working with probability, random variables, mean, variance, vectors, matrices, linear algebra, and statistics. You’ll also discover similarity-based methods like K-nearest neighbor and K-means clustering; decision tree-based methods like classification and regression trees; and linear methods like regularization and logical regression. Instead of simply applying black-box methods and techniques to ML problems, you’ll grok their underlying structure and apply a robust mathematical understanding alongside your practical skills. By the end of this comprehensive guide, you’ll be able to comfortably explore and understand the latest ML research as well as identify and tackle novel ML problems!
what's inside
Understanding machine learning problems
A review of probability and statistics
Similarity-based, tree-based, and linear ML methods
Working with neural networks
An introduction to deep learning
Probabilistic models

Knative in Action shows you how to build more complex and capable serverless applications with Knative. You’ll start by getting up to speed with the basics of serverless computing, Knative’s design concepts, and the tricky problems that the toolkit works to simplify. Next, Knative expert Jacques Chester takes you through how to use Knative Serving to build your systems, baking in autoscaling, auto-updating, and easy deployment right from the start. From there you’ll learn how to use Knative Eventing to wire together disparate systems into an elegant, functional whole. Finally, you'll learn how Knative can be used to smooth your shipping pipeline, from initial development right through to monitoring your software in production.
what's inside
Deploying a service with Knative Serving
Connecting systems with Knative Eventing
Autoscaling for differing server loads
Developing, shipping, and operating software with Knative

Objects are the central concept of languages like Java, Python, C#. Applying best practices for object design means that your code will be easy to read, write, and maintain. Object Design Style Guide captures dozens of techniques for creating pro-quality OO code that can stand the test of time. Examples are in an instantly-familiar pseudocode, teaching techniques you can apply to any OO language, from C++ to PHP.

Deep Learning for Vision Systems teaches you to apply deep learning techniques to solve real-world computer vision problems. In his straightforward and accessible style, DL and CV expert Mohamed Elgendy introduces you to the concept of visual intuition—how a machine learns to understand what it sees. Then you’ll explore the DL algorithms used in different CV applications. You’ll drill down into the different parts of the CV interpreting system, or pipeline. Using Python, OpenCV, Keras, Tensorflow, and Amazon’s MxNet, you’ll discover advanced DL techniques for solving CV problems.
Applications of focus include image classification, segmentation, captioning, and generation as well as face recognition and analysis. You’ll also cover the most important deep learning architectures including artificial neural networks (ANNs), convolutional networks (CNNs), and recurrent networks (RNNs), knowledge that you can apply to related deep learning disciplines like natural language processing and voice user interface. Real-life, scalable projects from Amazon, Google, and Facebook drive it all home. With this invaluable book, you’ll gain the essential skills for building amazing end-to-end CV projects that solve real-world problems.
What's inside
Introduction to computer vision
Deep learning and neural networks
Transfer learning and advanced CNN architectures
Image classification and captioning
Object detection with YOLO, SSD and R-CNN
Style transfer
AI ethics
Real-world projects

In 25 concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through pictures, analogies, and metaphors.
This book bridges the gap that exists between purely technical books about the blockchain and purely business-focused books. It does so by explaining both the technical concepts that make up the blockchain and their role in business-relevant applications.
What You'll Learn
What the blockchain is
Why it is needed and what problem it solves
Why there is so much excitement about the blockchain and its potential
Major components and their purpose
How various components of the blockchain work and interact
Limitations, why they exist, and what has been done to overcome them
Major application scenarios
Who This Book Is For
Everyone who wants to get a general idea of what blockchain technology is, how it works, and how it will potentially change the financial system as we know it

Probabilistic Deep Learning with Python shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results.
Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties.

内容介绍
这是一部从工作原理和工程实践两个维度深入剖析JVM的著作，是计算机领域公认的经典，繁体版在台湾也颇受欢迎。
自2011年上市以来，前两个版本累计印刷36次，销量超过30万册，两家主要网络书店的评论近90000条，内容上近乎零差评，是原创计算机图书领域不可逾越的丰碑。
第3版在第2版的基础上做了重大修订，内容更丰富、实战性更强：根据新版JDK对内容进行了全方位的修订和升级，围绕新技术和生产实践新增逾10万字，包含近50%的全新内容，并对第2版中含糊、瑕疵和错误内容进行了修正。
全书一共13章，分为五大部分：
第一部分（第1章）走近Java
系统介绍了Java的技术体系、发展历程、虚拟机家族，以及动手编译JDK，了解这部分内容能对学习JVM提供良好的指引。
第二部分（第2~5章）自动内存管理
详细讲解了Java的内存区域与内存溢出、垃圾收集器与内存分配策略、虚拟机性能监控与故障排除等与自动内存管理相关的内容，以及10余个经典的性能优化案例和优化方法；
第三部分（第6~9章）虚拟机执行子系统
深入分析了虚拟机执行子系统，包括类文件结构、虚拟机类加载机制、虚拟机字节码执行引擎，以及多个类加载及其执行子系统的实战案例；
第四部分（第10~11章）程序编译与代码优化
详细讲解了程序的前、后端编译与优化，包括前端的易用性优化措施，如泛型、主动装箱拆箱、条件编译等的内容的深入分析；以及后端的性能优化措施，如虚拟机的热点探测方法、HotSpot 的即时编译器、提前编译器，以及各种常见的编译期优化技术；
第五部分（第12~13章）高效并发
主要讲解了Java实现高并发的原理，包括Java的内存模型、线程与协程，以及线程安全和锁优化。
全书以实战为导向，通过大量与实际生产环境相结合的案例分析和展示了解决各种Java技术难题的方案和技巧。

How can you use data in a way that protects individual privacy, but still ensures that data analytics will be useful and meaningful? With this practical book, data architects and engineers will learn how to implement and deploy anonymization solutions within a data collection pipeline. You’ll establish and integrate secure, repeatable anonymization processes into your data flows and analytics in a sustainable manner.
Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing data, based on data collection models and use cases enabled by real business needs. These examples come from some of the most demanding data environments, using approaches that have stood the test of time.

Core Kubernetes is a reference guide designed to teach operators, SREs, and developers how to improve reliability and performance of Kubernetes-based systems. In it, Kubernetes experts Chris Love and Jay Vyas provide a guided tour through all major aspects of Kubernetes, from managing iptables to setting up dynamically scaled clusters that respond to changes in load. You’ll understand the unique security concerns of container-based applications, discover tips to minimize costly unused capacity, and get pro tips for maximizing performance. This awesome collection of undocumented internals, expert techniques, and practical guidance has invaluable information you won’t find anywhere else.
what's inside
Kubernetes base components
Kubernetes networking
Storage and the Container Storage Interface
External load balancing and ingress
Kubernetes security
Different ways of creating a Kubernetes cluster
Configuring Kubernetes to use a GPU

Seriously Good Software takes you on a journey through software development best practices as you develop a simple inefficient implementation into a robust, professional-quality application. The deceptively-simple problem—balancing water levels in multiple connected containers—presents some interesting complexities as you optimize the code for speed, efficiency, and durability. Author Marco Faella’s passion for teaching shines bright as you learn to assess performance and write thread-safe code that’s easily readable, testable, and maintainable. Along the way, you’ll gain a keen sense of the trade-offs inherent to every programming task, no matter how simple.
Each chapter contains hands-on exercises with complete, clear solutions that relate the techniques to real-world applications. Occasional pop quizzes ensure you’ve got a firm grasp on the concepts as you go along. When you’re done with this book, evaluating software qualities, considering tradeoffs and operational constraints, and asking all the right questions will be a reflex!
what's inside
Evaluating software qualities
Distinguishing between qualities that are internal, external, functional, and non-functional
Assessing tradeoffs and interactions between software qualities
Fulfilling different quality objectives for a single unifying task
Hands-on exercises and detailed solutions
Exercises covering the inner workings of the Java API

Learn dbatools in a Month of Lunches is a practical hands-on guide to automating SQL Server with PowerShell and the awesome dbatools module. You’ll master techniques you can immediately put into practice, from daily duties like backups and restores right through to performing security audits. Stabilize and standardize your SQL server environment, and simplify your tasks by building automation, alerting, and reporting with this powerful tool. Each lesson delivers another skill that you can use to speed through your core tasks as a SQL Server DBA!
what's inside
Installing dbatools in secure environments
Bulk-import any type of data into SQL Server
Instance-to-instance and customized migrations
Schedule and monitor PowerShell tasks in SQL Server Agent

Create database designs that scale, meet business requirements, and inherently work toward keeping your data structured and usable in the face of changing business models and software systems.
This book is about database design theory. Design theory is the scientific foundation for database design, just as the relational model is the scientific foundation for database technology in general. Databases lie at the heart of so much of what we do in the computing world that negative impacts of poor design can be extraordinarily widespread.
This second edition includes greatly expanded coverage of exotic and little understood normal forms such as: essential tuple normal form (ETNF), redundancy free normal form (RFNF), superkey normal form (SKNF), sixth normal form (6NF), and domain key normal form (DKNF). Also included are new appendixes, including one that provides an in-depth look into the crucial notion of data consistency. Sequencing of topics has been improved, and many explanations and examples have been rewritten and clarified based upon the author’s teaching of the content in instructor-led courses.
This book aims to be different from other books on design by bridging the gap between the theory of design and the practice of design. The book explains theory in a way that practitioners should be able to understand, and it explains why that theory is of considerable practical importance. Reading this book provides you with an important theoretical grounding on which to do the practical work of database design. Reading the book also helps you in going to and understanding the more academic texts as you build your base of knowledge and expertise. Anyone with a professional interest in database design can benefit from using this book as a stepping-stone toward a more rigorous design approach and more lasting database models.
What You Will Learn
Understand what design theory is and is not
Be aware of the two different goals of normalization
Know which normal forms are truly significant
Apply design theory in practice
Be familiar with techniques for dealing with redundancy
Understand what consistency is and why it is crucially important

In Designing Cloud Data Platforms, you’ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you’ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you’ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more.
what's inside
The tools of different public cloud for implementing data platforms
Best practices for managing structured and unstructured data sets
Machine learning tools that can be used on top of the cloud
Cost optimization techniques

Fully updated and upgrade for the latest version of Spring, Spring Microservices in Action, Second Edition is an expanded revision of a Manning bestseller. In it, you’ll learn how to build microservice-based applications using Java and the Spring platform and how to efficiently monitor and log your microservices operations. Throughout the book, carefully selected real-life examples expose microservice-based patterns for configuring, routing, scaling, and deploying your services. You'll see how Spring's intuitive tooling can help augment and refactor existing applications with microservices and how Spring Cloud Gateway makes it easy to handle multiple APIs with a single tool. You’ll also pick up best practices for using Spring with modern deployment platforms based on Kubernetes and Istio.
what's inside
Core microservice design principles
Microservices best practices
Using docker containers to run microservices
Managing configuration with Spring Cloud Config and Hashicorp Vault for sensitive information
Client-side resiliency with Hystrix, and Ribbon
Managing application metrics with Prometheus and Grafana
Intelligent routing using Spring Cloud Gateway
Distributed tracing with Spring Cloud Sleuth, Zipkin and ELK Stack
Deploying Spring Cloud applications with Kubernetes and Istio