Software maintenance work is often considered a dauntingly rigid activity - this book proves the opposite: it demands high levels of creativity and thinking outside the box. Highlighting the creative aspects of software maintenance and combining analytical and systems thinking in a holistic manner, the book motivates readers not to blithely follow the beaten tracks of "technical rationality". It delivers the content in a pragmatic fashion using case studies which are woven into long running story lines. The book is organized in four parts, which can be read in any order, except for the first chapter, which introduces software maintenance and evolution and presents a number of case studies of software failures. The "Introduction to Key Concepts" briefly introduces the major elements of software maintenance by highlighting various core concepts that are vital in order to see the forest for the trees. Each such concept is illustrated with a worked example. Next, the "Forward Engineering" part debunks the myth that being fast and successful during initial development is all that matters. To this end, two categories of forward engineering are considered: an inept initial project with a multitude of hard evolutionary phases and an effective initial project with multiple straightforward future increments. "Reengineering and Reverse Engineering" shows the difficulties of dealing with a typical legacy system, and tackles tasks such as retrofitting tests, documenting a system, restructuring a system to make it amenable for further improvements, etc. Lastly, the "DevOps" section focuses on the importance and benefits of crossing the development versus operation chasm and demonstrates how the DevOps paradigm can turn a loosely coupled design into a loosely deployable solution. The book is a valuable resource for readers familiar with the Java programming language, and with a basic understanding and/or experience of software construction and testing. Packed with examples for every elaborated concept, it offers complementary material for existing courses and is useful for students and professionals alike.

Software maintenance work is often considered a dauntingly rigid activity - this book proves the opposite: it demands high levels of creativity and thinking outside the box. Highlighting the creative aspects of software maintenance and combining analytical and systems thinking in a holistic manner, the book motivates readers not to blithely follow the beaten tracks of "technical rationality". It delivers the content in a pragmatic fashion using case studies which are woven into long running story lines. The book is organized in four parts, which can be read in any order, except for the first chapter, which introduces software maintenance and evolution and presents a number of case studies of software failures. The "Introduction to Key Concepts" briefly introduces the major elements of software maintenance by highlighting various core concepts that are vital in order to see the forest for the trees. Each such concept is illustrated with a worked example. Next, the "Forward Engineering" part debunks the myth that being fast and successful during initial development is all that matters. To this end, two categories of forward engineering are considered: an inept initial project with a multitude of hard evolutionary phases and an effective initial project with multiple straightforward future increments. "Reengineering and Reverse Engineering" shows the difficulties of dealing with a typical legacy system, and tackles tasks such as retrofitting tests, documenting a system, restructuring a system to make it amenable for further improvements, etc. Lastly, the "DevOps" section focuses on the importance and benefits of crossing the development versus operation chasm and demonstrates how the DevOps paradigm can turn a loosely coupled design into a loosely deployable solution. The book is a valuable resource for readers familiar with the Java programming language, and with a basic understanding and/or experience of software construction and testing. Packed with examples for every elaborated concept, it offers complementary material for existing courses and is useful for students and professionals alike.

Build straightforward and maintainable APIs to create services that are usable and maintainable. Although this book focuses on distributed services, it also emphasizes how the core principles apply even to pure OOD and OOP constructs. The overall context of Creating Maintainable APIs is to classify the topics into four main areas: classes and interfaces, HTTP REST APIs, messaging APIs, and message payloads (XML, JSON and JSON API as well as Apache Avro). What You Will Learn Use object-oriented design constructs and their APIs Create and manage HTTP REST APIs Build and manage maintainable messaging APIs, including the use of Apache Kafka as a principal messaging hub Handle message payloads via JSON Who This Book Is For Any level software engineers and very experienced programmers.

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors. What You'll Learn Play the role of a data scientist when completing increasingly challenging exercises using Python 3 Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data Apply theory of probability, statistical inference, and algebra to understand the data science practices Who This Book Is For Anyone who would like to embark into the realm of data science using Python 3.