Data Mesh In Action

Revolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. In Data Mesh in Action you will learn how to: Implement a data mesh in your organization Turn data into a data product Move from your current data architecture to a data mesh Identify data domains, and decompose an organization into smaller, manageable domains Set up the central governance and local governance levels over data Balance responsibilities between the two levels of governance Establish a platform that allows efficient connection of distributed data products and automated governance Data Mesh in Action reveals how this groundbreaking architecture looks for both small startups and large enterprises. You won’t need any new technology—this book shows you how to start implementing a data mesh with flexible processes and organizational change. You’ll explore both an extended case study and multiple real-world examples. As you go, you’ll be expertly guided through discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-product system. Plus, dozens of workshop techniques for both in-person and remote meetings help you onboard colleagues and drive a successful transition. About the technology Business increasingly relies on efficiently storing and accessing large volumes of data. The data mesh is a new way to decentralize data management that radically improves security and discoverability. A well-designed data mesh simplifies self-service data consumption and reduces the bottlenecks created by monolithic data architectures. About the book Data Mesh in Action teaches you pragmatic ways to decentralize your data and organize it into an effective data mesh. You’ll start by building a minimum viable data product, which you’ll expand into a self-service data platform, chapter-by-chapter. You’ll love the book’s unique “sliders” that adjust the mesh to meet your specific needs. You’ll also learn processes and leadership techniques that will change the way you and your colleagues think about data. What's inside Decompose an organization into manageable domains Turn data into a data product Set up central and local governance levels Build a fit-for-purpose data platform Improve management, initiation, and support techniques About the reader For data professionals. Requires no specific programming stack or data platform. About the author Jacek Majchrzak is a hands-on lead data architect. Dr. Sven Balnojan manages data products and teams. Dr. Marian Siwiak is a data scientist and a management consultant for IT, scientific, and technical projects. Table of Contents PART 1 FOUNDATIONS 1 The what and why of the data mesh 2 Is a data mesh right for you? 3 Kickstart your data mesh MVP in a month PART 2 THE FOUR PRINCIPLES IN PRACTICE 4 Domain ownership 5 Data as a product 6 Federated computational governance 7 The self-serve data platform PART 3 INFRASTRUCTURE AND TECHNICAL ARCHITECTURE 8 Comparing self-serve data platforms 9 Solution architecture design

Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh

Solve difficult service-to-service communication challenges around security, observability, routing, and resilience with an Istio-based service mesh. Istio allows you to define these traffic policies as configuration and enforce them consistently without needing any service-code changes. In Istio in Action you will learn: Why and when to use a service mesh Envoy's role in Istio's service mesh Allowing "North-South" traffic into a mesh Fine-grained traffic routing Make your services robust to network failures Gain observability over your system with telemetry "golden signals" How Istio makes your services secure by default Integrate cloud-native applications with legacy workloads such as in VMs Reduce the operational complexity of your microservices with an Istio-powered service mesh! Istio in Action shows you how to implement this powerful new architecture and move your application-networking concerns to a dedicated infrastructure layer. Non-functional concerns stay separate from your application, so your code is easier to understand, maintain, and adapt regardless of programming language. In this practical guide, you'll go hands-on with the full-featured Istio service mesh to manage microservices communication. Helpful diagrams, example configuration, and examples make it easy to understand how to control routing, secure container applications, and monitor network traffic. Foreword by Eric Brewer. About the technology Offload complex microservice communication layer challenges to Istio! The industry-standard Istio service mesh radically simplifies security, routing, observability, and other service-to-service communication challenges. With Istio, you use a straightforward declarative configuration style to establish application-level network policies. By separating communication from business logic, your services are easier to write, maintain, and modify. About the book Istio in Action teaches you how to implement an Istio-based service mesh that can handle complex routing scenarios, traffic encryption, authorization, and other common network-related tasks. You'll start by defining a basic service mesh and exploring the data plane with Istio’s service proxy, Envoy. Then, you'll dive into core topics like traffic routing and visualization and service-to-service authentication, as you expand your service mesh to workloads on multiple clusters and legacy VMs. What's inside Comprehensive coverage of Istio resources Practical examples to showcase service mesh capabilities Implementation of multi-cluster service meshes How to extend Istio with WebAssembly Traffic routing and observability VM integration into the mesh About the reader For developers, architects, and operations engineers. About the author Christian Posta is a well-known architect, speaker, and contributor. Rinor Maloku is an engineer at Solo.io working on application networking solutions. ToC PART 1 UNDERSTANDING ISTIO 1 Introducing the Istio service mesh 2 First steps with Istio 3 Istio's data plane: The Envoy proxy PART 2 SECURING, OBSERVING, AND CONTROLLING YOUR SERVICE’S NETWORK TRAFFIC 4 Istio gateways: Getting traffic into a cluster 5 Traffic control: Fine-grained traffic routing 6 Resilience: Solving application networking challenges 7 Observability: Understanding the behavior of your services 8 Observability: Visualizing network behavior with Grafana, Jaeger, and Kiali 9 Securing microservice communication PART 3 ISTIO DAY-2 OPERATIONS 10 Troubleshooting the data plane 11 Performance-tuning the control plane PART 4 ISTIO IN YOUR ORGANIZATION 12 Scaling Istio in your organization 13 Incorporating virtual machine workloads into the mesh 14 Extending Istio on the request path

Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patterns Key FeaturesUnderstand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner. What you will learnUnderstand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.

Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, and treats data as a product. This book shows you why and how. Examine the current landscape of data architectures, their underlying characteristics, and failure modes Learn how to divide data (and its supporting technology stacks and architecture) into operational data and analytical data Get a complete introduction to data mesh principles and logical architecture Create a foundation for gaining value from analytical data and historical facts at scale Move beyond a monolithic data lake to a distributed data mesh

Summary Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort. Foreword by Neha Narkhede, Cocreator of Apache Kafka Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Not all stream-based applications require a dedicated processing cluster. The lightweight Kafka Streams library provides exactly the power and simplicity you need for message handling in microservices and real-time event processing. With the Kafka Streams API, you filter and transform data streams with just Kafka and your application. About the Book Kafka Streams in Action teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you'll explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You'll even dive into streaming SQL with KSQL! Practical to the very end, it finishes with testing and operational aspects, such as monitoring and debugging. What's inside Using the KStreams API Filtering, transforming, and splitting data Working with the Processor API Integrating with external systems About the Reader Assumes some experience with distributed systems. No knowledge of Kafka or streaming applications required. About the Author Bill Bejeck is a Kafka Streams contributor and Confluent engineer with over 15 years of software development experience. Table of Contents PART 1 - GETTING STARTED WITH KAFKA STREAMS Welcome to Kafka Streams Kafka quicklyPART 2 - KAFKA STREAMS DEVELOPMENT Developing Kafka Streams Streams and state The KTable API The Processor APIPART 3 - ADMINISTERING KAFKA STREAMS Monitoring and performance Testing a Kafka Streams applicationPART 4 - ADVANCED CONCEPTS WITH KAFKA STREAMS Advanced applications with Kafka StreamsAPPENDIXES Appendix A - Additional configuration information Appendix B - Exactly once semantics

Data governance is broken. Its time we fix it. Why is data governance so ineffective? The truth is data governance programs arent designed for the way we run our data teams, they arent even designed for a modern organization at all. They were designed when reports still came through inter-office mail. The flow of data into, within, and out of todays organizations is a tsunami breaking through rigid data governance methods. Yet our programs still rely on that command and control approach. Have you ever tried to control a tsunami? Every organization that uses data knows that they need a data governance program. Data literacy efforts and legislation like GDPR have become the bellwethers for our governance functions. But we still sit in data governance meetings without enough people and too many questions to move things forward. Theres no agility to the program because we imply a degree of frailty to the data that doesnt exist. We continue to insist on archaic methods that bring no value to our organizations. Achieving deep insights from data cant happen without good governance practices. All indicators point to the need to create a resilient and responsive data governance function. Where we go from here, and how we achieve success in data governance requires a radically different way. The hard truth: its time to challenge everything we know about data governance. Laura Madsen shows you how to redefine governance for the modern age. With a casual, witty style Madsen taps on her decades of experience, shares interviews with other best-in-field experts and grounds her perspective in research. Witness where it all fell apart, challenge long-held beliefs, and commit to a fundamental shiftthat governance is not about stopping or preventing usage but about supporting the usage of data. Be able to bring back trust and value to our data governance functions, and learn the: People-driven approach to governance; Processes that support the tsunami of data; Cutting edge technology thats enabling data governance.