This book is about nonlinear observability. It provides a modern theory of observability based on a new paradigm borrowed from theoretical physics and the mathematical foundation of that paradigm. In the case of observability, this framework takes into account the group of invariance that is inherent to the concept of observability, allowing the reader to reach an intuitive derivation of significant results in the literature of control theory. The book provides a complete theory of observability and, consequently, the analytical solution of some open problems in control theory. Notably, it presents the first general analytic solution of the nonlinear unknown input observability (nonlinear UIO), a very complex open problem studied in the 1960s. Based on this solution, the book provides examples with important applications for neuroscience, including a deep study of the integration of multiple sensory cues from the visual and vestibular systems for self-motion perception. Observability: A New Theory Based on the Group of Invariance is the only book focused solely on observability. It provides readers with many applications, mostly in robotics and autonomous navigation, as well as complex examples in the framework of vision-aided inertial navigation for aerial vehicles. For these applications, it also includes all the derivations needed to separate the observable part of the system from the unobservable, an analysis with practical importance for obtaining the basic equations for implementing any estimation scheme or for achieving a closed-form solution to the problem. This book is intended for researchers in robotics and automation, both in academia and in industry. Researchers in other engineering disciplines, such as information theory and mechanics, will also find the book useful.

Securing, observing, and troubleshooting containerized workloads on Kubernetes can be daunting. It requires a range of considerations, from infrastructure choices and cluster configuration to deployment controls and runtime and network security. With this practical book, you'll learn how to adopt a holistic security and observability strategy for building and securing cloud native applications running on Kubernetes. Whether you're already working on cloud native applications or are in the process of migrating to its architecture, this guide introduces key security and observability concepts and best practices to help you unleash the power of cloud native applications. Authors Brendan Creane and Amit Gupta from Tigera take you through the full breadth of new cloud native approaches for establishing security and observability for applications running on Kubernetes. Learn why you need a security and observability strategy for cloud native applications and determine your scope of coverage Understand key concepts behind the book's security and observability approach Explore the technology choices available to support this strategy Discover how to share security responsibilities across multiple teams or roles Learn how to architect Kubernetes security and observability for multicloud and hybrid environments

Use BPF Tools to Optimize Performance, Fix Problems, and See Inside Running Systems BPF-based performance tools give you unprecedented visibility into systems and applications, so you can optimize performance, troubleshoot code, strengthen security, and reduce costs. BPF Performance Tools: Linux System and Application Observability is the definitive guide to using these tools for observability. Pioneering BPF expert Brendan Gregg presents more than 150 ready-to-run analysis and debugging tools, expert guidance on applying them, and step-by-step tutorials on developing your own. You’ll learn how to analyze CPUs, memory, disks, file systems, networking, languages, applications, containers, hypervisors, security, and the kernel. Gregg guides you from basic to advanced tools, helping you generate deeper, more useful technical insights for improving virtually any Linux system or application. • Learn essential tracing concepts and both core BPF front-ends: BCC and bpftrace • Master 150+ powerful BPF tools, including dozens created just for this book, and available for download • Discover practical strategies, tips, and tricks for more effective analysis • Analyze compiled, JIT-compiled, and interpreted code in multiple languages: C, Java, bash shell, and more • Generate metrics, stack traces, and custom latency histograms • Use complementary tools when they offer quick, easy wins • Explore advanced tools built on BPF: PCP and Grafana for remote monitoring, eBPF Exporter, and kubectl-trace for tracing Kubernetes • Foreword by Alexei Starovoitov, creator of the new BPF BPF Performance Tools will be an indispensable resource for all administrators, developers, support staff, and other IT professionals working with any recent Linux distribution in any enterprise or cloud environment.

Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Learn the core principles and benefits of data observability Use data observability to detect, troubleshoot, and prevent data issues Follow the book's recipes to implement observability in your data projects Use data observability to create a trustworthy communication framework with data consumers Learn how to educate your peers about the benefits of data observability

Observability is critical for building, changing, and understanding the software that powers complex modern systems. Teams that adopt observability are much better equipped to ship code swiftly and confidently, identify outliers and aberrant behaviors, and understand the experience of each and every user. This practical book explains the value of observable systems and shows you how to practice observability-driven development. Authors Charity Majors, Liz Fong-Jones, and George Miranda from Honeycomb explain what constitutes good observability, show you how to improve upon what youâ??re doing today, and provide practical dos and don'ts for migrating from legacy tooling, such as metrics monitoring and log management. Youâ??ll also learn the impact observability has on organizational culture (and vice versa). You'll explore: How the concept of observability applies to managing software systems The value of practicing observability when delivering and managing complex cloud native applications and systems The impact observability has across the entire software development lifecycle How and why different functional teams use observability with service-level objectives (SLOs) How to instrument your code to help future engineers understand the code you wrote today How to produce quality code for context-aware system debugging and maintenance How data-rich analytics can help you debug elusive issues quickly

The concept of observability of entities in physical science is typically analyzed in terms of the nature and significance of a dichotomy between observables and unobservables. In this book, however, this categorization is resisted and observability is analyzed in a descriptive way in terms of the information which one can receive through interaction with objects in the world. The account of interaction and the transfer of information is done using applicable scientific theories. In this way the question of observability of scientific entities is put to science itself. Several examples are presented which show how this interaction-information account of observability is done. It is demonstrated that observability has many dimensions which are in general orthogonal. The epistemic significance of these dimensions is explained. This study is intended primarily as a method for understanding problems of observability rather than as a solution to those problems. The important issue of scientific realism and its relation to observability, however, demands attention. Hence, the implication of the interaction-information account for realism is drawn in terms of the epistemic significance of the dimensions of observability. This amounts to specifying what it is about good observations that make them objective evidence for scientific theories.

Unlock your organizational potential and scale your business using observability with this information packed guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn the principles of identifying stakeholders, tools, and processes necessary for implementing observability Develop strategies to self-sustain the observability journey in the long run Learn with real-life case studies to set up observability for your enterprise Book Description Observability can be implemented in multiple ways within an organization based on the organization's needs. So, it's crucial for organizations to decide whether they need observability and to what extent, what skills and tools will suit them, and how long will it take to implement it. Implementing Enterprise Observability for Success provides a step-by-step approach to help you create an observability strategy, understand the principles behind the creation of the strategy, and logical steps to plan and execute the implementation. You'll learn about observability fundamentals and challenges, the importance of data and analytics along with different tools. Further, you'll discover the various layers from which data should be collected for setting up observability. Through real- life examples distilled from the author's experience in implementing observability at an enterprise level, you'll uncover some of the non-technical & technical drivers of observability like the culture of the organization, the hierarchy of stakeholders, tools at disposal and the willingness to invest. By the end of this book, you'll be well-equipped to plan the observability journey, identify different stakeholders, spot the technology stack required, and lay out an effective plan for organization-wide adoption. What you will learn Understand observability fundamentals from scratch Get to grips with common challenges in implementing observability Segregate organizations according to their size for implementing observability Leverage analytics to better understand the value of collected data Explore best practices from case studies to smoothen your observability journey Understand the Observability Maturity Model and the essential people skills necessary to attain each maturity level Who this book is for This book is for technology leaders, architects, and initiative leads looking to enhance monitoring and/or implementing observability. Engineers, developers, and professionals already working on monitoring and analytics or are responsible for scaling the observability implementation across multiple teams or at an organizational level can also benefit from this book. A deep understanding of monitoring concepts, general knowledge of IT systems and processes along with familiarity with working across various stakeholders is required.