book

Implementing Data Mesh

by Jean-Georges Perrin, Eric Broda

September 2024

Intermediate to advanced

268 pages

7h 13m

English

O'Reilly Media, Inc.

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Who Is This Book For? Overview of the Parts and ChaptersWhat This Book Isn’t Conventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
Making Data AgileLocal Autonomy + Speed = AgilitySolving Today’s Data ChallengesBridging Data SilosShifting Toward Higher Quality DataTransforming Data GovernanceData Volume, Variety, and VariabilityTurning Principles into PracticeSummary
Data Mesh PrinciplesData as a ProductDecentralized Domain OwnershipSelf-Serve Data PlatformFederated Computational GovernanceDefining a “Good” Data ProductDefining a Principled Data ProductDefining a FAIR Data ProductDefining an Enterprise-Grade Data ProductDefining a Valuable Data ProductDefining a Balanced Data ProductDefining a Modern Data Product—More than Just “Regular” DataDefining a Practical Data Product LifecycleDefining a Practical Data Mesh RoadmapA “Good” Data Product Has an Empowered Data Product OwnerIdentifying a Data ProductSummary
Making Climate Data Easier to Find, Consume, Share, and TrustIntroducing Climate Quantum Inc.Climate Quantum Data LandscapeApplying Climate Quantum Inc. to Your EnterpriseSummary
Data Product ArchitectureData Product ArtifactsDevelopment Architecture ComponentsRuntime Architecture ComponentsOperations Architecture ComponentsData Mesh ArchitectureData Mesh Marketplace and RegistryData Mesh Backbone ServicesClimate Quantum Use Case ConsiderationsSummary
Bringing Value Through TrustNavigating the Data ContractGoing Through the TheoryStacking Up Good InformationIt’s All About Proper VersioningKeeping It Simple and SemanticWalking Through an Example: Complementing Tribal KnowledgeWhat is Data QoS and Why Is It Critical?RepresentationWhy Does It Matter?Why Data Quality Is Not EnoughService Levels Complement QualityApplying Data QoS to the Data ContractChecking Conformity of MeasurementsCompletenessAccuracyEngaging Service LevelsSummary
Anatomy of a Data ProductThe Data Contract to the RescueConnecting Your Data SourcesEnsuring Higher Data Quality with ObservabilityGetting Faster Data DiscoveryEnabling Operability and ControlBuilding and PackagingSummary

The Three Planes The Infrastructure Plane Remains KeyThe Data Product Experience Plane Is IndependentThe Mesh Experience Plane Is About SynergyBuilding a Capability Model for Each PlaneCapabilities of the Infrastructure Experience PlaneCapabilities of the Data Product Experience PlaneCapabilities of the Mesh Experience PlaneGathering Key Metrics Through Feedback LoopsSummary
Registering Your Data ProductConnecting to Your Products in the MeshMeshing Data Products TogetherDescribing Your Data LineageNotification Through the MeshSummary
Making Data Products Discoverable, Observable, and SecureData Product Discovery InterfaceData Product Observability InterfaceData Product Control InterfaceData Product SecurityClimate Quantum Use Case ConsiderationsToward Dynamic Data ProductsThe Power of Dynamic Data ProductsSummary
Challenges with Traditional Data CatalogsThe Data Mesh MarketplaceA Window into Data MeshThe Consumer User JourneyThe Producer User JourneySelf-Publishing of Data ProductsThe Power of DiscoverabilityBootstrapping Your MarketplaceClimate Quantum’s Data Mesh MarketplaceSummary
Data GovernanceData Product CertificationFederated Data Product CertificationImplementing Data Product Certification Data Contracts and CertificationClimate Quantum’s Certification ApproachSummary
The Modern Manufacturing Supply ChainThe Modern Software FactoryThe Data Product FactoryThe Data Product Supply ChainClimate Quantum’s Data Product Supply ChainSummary
Generative AI BackgroundLarge Language Models EmbeddingsVector DatabasesPromptsChallengesData Mesh and Generative AIImprove Data Quality with Generative AIElevate Models as Primary ArtifactsConsume Models Inside Data ProductsGenerate Data Product CodeComplement Data Product CapabilitiesClimate Quantum and Generative AISimplifying Climate Data SearchAnalyzing and Summarizing Climate Data Creating Disclosure ReportsGenerating Code to Simplify Climate Data ProcessingSummary
Team Topologies in Data MeshThe Data Product TeamPlatform TeamsEnabling TeamsClimate Quantum TeamsSummary
Characteristics of an Operating ModelThe Operating Model ContinuumCentralized OrganizationsMatrixed OrganizationsFederated OrganizationsDistributed OrganizationsData Mesh Operating ModelData Mesh Maturity ModelClimate Quantum’s Data Mesh Operating ModelSummary
Roadmap StructureStrategy and Roadmap StreamTechnology StreamFactory StreamOperating Model StreamSocialization StreamRollout StreamClimate Quantum’s Data Mesh RoadmapSummary

Content preview from Implementing Data Mesh

Chapter 3. Our Case Study: Climate Quantum Inc.

In this chapter, we will introduce our case study—Climate Quantum Inc.—where we will apply Data Mesh capabilities to an important and pressing need: climate change.

But first some background.

Climate change permeates every aspect of our global society. According to the United Nations, “Since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil and gas.” The article continues:

Climate change can affect our health, ability to grow food, housing, safety and work. Some of us are already more vulnerable to climate impacts, such as people living in small island nations and other developing countries. Conditions like sea-level rise and saltwater intrusion have advanced to the point where whole communities have had to relocate, and protracted droughts are putting people at risk of famine. In the future, the number of people displaced by weather-related events is expected to rise.

As businesses grapple with the implications of climate change, they find themselves confronting a new frontier: the complex and constantly changing world of climate data. While the data holds invaluable insights, navigating its intricacies poses significant challenges.

First, climate data is an ever-evolving landscape. Climate data isn’t static; it constantly morphs, influenced by countless variables ranging from anthropogenic activities to natural phenomena. Pinpointing the relevant ...