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

This text represents a breakthrough in the process underlying the design of the increasingly common and important data-driven Web applications.

This invaluable roadmap for startup engineers reveals how to successfully handle web application scalability challenges to meet increasing product and traffic demands. Web Scalability for Startup Engineers shows engineers working at startups and small companies how to plan and implement a comprehensive scalability strategy. It presents broad and holistic view of infrastructure and architecture of a scalable web application. Successful startups often face the challenge of scalability, and the core concepts driving a scalable architecture are language and platform agnostic. The book covers scalability of HTTP-based systems (websites, REST APIs, SaaS, and mobile application backends), starting with a high-level perspective before taking a deep dive into common challenges and issues. This approach builds a holistic view of the problem, helping you see the big picture, and then introduces different technologies and best practices for solving the problem at hand. The book is enriched with the author's real-world experience and expert advice, saving you precious time and effort by learning from others' mistakes and successes. Language-agnostic approach addresses universally challenging concepts in Web development/scalability—does not require knowledge of a particular language Fills the gap for engineers in startups and smaller companies who have limited means for getting to the next level in terms of accomplishing scalability Strategies presented help to decrease time to market and increase the efficiency of web applications

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

The most prominent Web applications in use today are data-intensive. Scores of database management systems across the Internet access and maintain large amounts of structured data for e-commerce, on-line trading, banking, digital libraries, and other high-volume sites. Developing and maintaining these data-intensive applications is an especially complex, multi-disciplinary activity, requiring all the tools and techniques that software engineering can provide. This book represents a breakthrough for Web application developers. Using hundreds of illustrations and an elegant intuitive modeling language, the authors—all internationally-known database researchers—present a methodology that fully exploits the conceptual modeling approach of software engineering, from idea to application. Readers will learn not only how to harness the design technologies of relational databases for use on the Web, but also how to transform their conceptual designs of data-intensive Web applications into effective software components. * A fully self-contained introduction and practitioner's guide suitable for both technical and non-technical members of staff, as well as students. * A methodology, development process, and notation (WebML) based on common practice but optimized for the unique challenges of high-volume Web applications. * Completely platform- and product-independent; even the use of WebML is optional. * Based on well-known industry standards such as UML and the Entity Relationship Model. * Enhanced by its own Web site (http://www.webml.org), containing additional examples, papers, teaching materials, developers' resources, and exercises with solutions.

Architect and design data-intensive applications and, in the process, learn how to collect, process, store, govern, and expose data for a variety of use cases Key Features Integrate the data-intensive approach into your application architecture Create a robust application layout with effective messaging and data querying architecture Enable smooth data flow and make the data of your application intensive and fast Book Description Are you an architect or a developer who looks at your own applications gingerly while browsing through Facebook and applauding it silently for its data-intensive, yet fluent and efficient, behaviour? This book is your gateway to build smart data-intensive systems by incorporating the core data-intensive architectural principles, patterns, and techniques directly into your application architecture. This book starts by taking you through the primary design challenges involved with architecting data-intensive applications. You will learn how to implement data curation and data dissemination, depending on the volume of your data. You will then implement your application architecture one step at a time. You will get to grips with implementing the correct message delivery protocols and creating a data layer that doesn't fail when running high traffic. This book will show you how you can divide your application into layers, each of which adheres to the single responsibility principle. By the end of this book, you will learn to streamline your thoughts and make the right choice in terms of technologies and architectural principles based on the problem at hand. What you will learn Understand how to envision a data-intensive system Identify and compare the non-functional requirements of a data collection component Understand patterns involving data processing, as well as technologies that help to speed up the development of data processing systems Understand how to implement Data Governance policies at design time using various Open Source Tools Recognize the anti-patterns to avoid while designing a data store for applications Understand the different data dissemination technologies available to query the data in an efficient manner Implement a simple data governance policy that can be extended using Apache Falcon Who this book is for This book is for developers and data architects who have to code, test, deploy, and/or maintain large-scale, high data volume applications. It is also useful for system architects who need to understand various non-functional aspects revolving around Data Intensive Systems.