Managing Cloud Native Data on Kubernetes
Is Kubernetes ready for stateful workloads? This open source system has become the primary platform for deploying and managing cloud native applications. But because it was originally designed for stateless workloads, working with data on Kubernetes has been challenging. If you want to avoid the inefficiencies and duplicative costs of having separate infrastructure for applications and data, this practical guide can help.
Using Kubernetes as your platform, you'll learn open source technologies that are designed and built for the cloud. Authors Jeff Carpenter and Patrick McFadin provide case studies to help you explore new use cases and avoid the pitfalls others have faced. You'll get an insider's view of what's coming from innovators who are creating next-generation architectures and infrastructure.
- Learn how to use basic Kubernetes resources to compose data infrastructure
- Automate the deployment and operations of data infrastructure on Kubernetes using tools like Helm and o ...
Data Management at Scale, 2nd Edition
As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization.
Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.
Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric; Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data produc ...
Resilient Oracle PL/SQL
As legacy and other critical systems continue to migrate online, the need for continuous operation is imperative. Code has to handle data issues as well as hard external problems today, including outages of networks, storage systems, power, and ancillary systems. This practical guide provides system administrators, DevSecOps engineers, and cloud architects with a concise yet comprehensive overview on how to use PL/SQL to develop resilient database solutions.
Integration specialist Stephen B Morris helps you understand the language, build a PL/SQL toolkit, and collect a suite of reusable components and patterns. You'll dive into the benefits of synthesizing the toolkit with a requirements-driven, feature-oriented approach and learn how to produce resilient solutions by synthesizing the PL/SQL toolkit in conjunction with a scale of resilience.
Build solid PL/SQL solutions while avoiding common PL/SQL antipatterns; Learn why embedding complex business logic in SQL is often a brittle ...
Practical Linux System Administration
This essential guide covers all aspects of Linux system administration, from user maintenance, backups, filesystem housekeeping, storage management, and network setup to hardware and software troubleshooting and some application management. It's both a practical daily reference manual for sysadmins and IT pros and a handy study guide for those taking Linux certification exams.
You'll turn to it frequently, not only because of the sheer volume of valuable information it provides but because of the real-world examples within and the clear, useful way the information is presented. With this book at your side, you'll be able to: Install Linux and perform initial setup duties, such as connecting to a network; Navigate the Linux filesystem via the command line; Install software from repositories and source and satisfy dependencies; Set permissions on files and directories; Create, modify, and remove user accounts; Set up networking; Format and mount filesystems; Perform basic troubleshoot ...
Practical Data Privacy
Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.
Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and method ...
Streaming Data Mesh
Data lakes and warehouses have become increasingly fragile, costly, and difficult to maintain as data gets bigger and moves faster. Data meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services.
Authors Hubert Dulay and Stephen Mooney examine the vast differences between streaming and batch data meshes. Data engineers, architects, data product owners, and those in DevOps and MLOps roles will learn steps for implementing a streaming data mesh, from defining a data domain to building a good data product. Through the course of the book, you'll create a complete self-service data platform and devise a data governance system that enables your mesh to work seamlessly.
With this book, you will: Design a streaming data mesh using Kafka; Learn how to identify a domain; Build your first data product using self-service to ...
Machine Learning for High-Risk Applications
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.
This book describes approaches to responsible AI - a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security; Lea ...
Generative Deep Learning, 2nd Edition
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.
The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.
Discover how VAEs can change facial expressions in photos; Train GANs to generate images based on your own dataset; Build diffusion models to produce new varieties of flowers; Train your own GPT for text generation; Learn how large language models like ChatGPT are trained; Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN; Compose polyphonic music using Transformers and Mu ...
Kubernetes Patterns, 2nd Edition
The way developers design, build, and run software has changed significantly with the evolution of microservices and containers. These modern architectures offer new distributed primitives that require a different set of practices than many developers, tech leads, and architects are accustomed to. With this focused guide, Bilgin Ibryam and Roland Huss provide common reusable patterns and principles for designing and implementing cloud native applications on Kubernetes.
Each pattern includes a description of the problem and a Kubernetes-specific solution. All patterns are backed by and demonstrated with concrete code examples. This updated edition is ideal for developers and architects familiar with basic Kubernetes concepts who want to learn how to solve common cloud native challenges with proven design patterns.
You'll explore: Foundational patterns covering core principles and practices for building and running container-based cloud native applications; Behavioral patterns that ...
Docker: Up & Running, 3rd Edition
Docker and Linux containers have fundamentally changed the way that organizations develop, deliver, and run software at scale. But understanding why these tools are important and how they can be successfully integrated into your organization's ecosystem can be challenging. This fully updated guide provides developers, operators, architects, and technical managers with a thorough understanding of the Docker tool set and how containers can improve almost every aspect of modern software delivery and management.
This edition includes significant updates to the examples and explanations that reflect the substantial changes that have occurred since Docker was first released almost a decade ago. Sean Kane and Karl Matthias have updated the text to reflect best practices and to provide additional coverage of new features like BuildKit, multi-architecture image support, rootless containers, and much more.
Learn how Docker and Linux containers integrate with cloud services and Kubernetes; ...