book

Foundations for Architecting Data Solutions

Name: Foundations for Architecting Data Solutions
ISBN: 9781492038740

by Ted Malaska, Jonathan Seidman

September 2018

Beginner to intermediate

187 pages

4h 59m

English

O'Reilly Media, Inc.

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Preface
Who This Book Is ForNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly SafariHow to Contact UsAcknowledgments
1. Key Data Project Types and Considerations
Major Data Project TypesData Pipelines and Data StagingPrimary Considerations and Risk ManagementPipeline and Staging Team MakeupData Processing and AnalysisPrimary Considerations and Risk ManagementData Processing and Analytics Team MakeupApplication DevelopmentPrimary Considerations and Risk ManagementApplication Development Team MakeupSummary
2. Evaluating and Selecting Data Management Solutions
Stages of Open Source ProjectsPrivate Incubation StageRelease Stage“Curing Cancer” StageBroken Promises StageHardening StageEnterprise StageDecline and Slow Death StageCommon Life Cycles for Open Source ProjectsOpen Sourcing a Dead ProductThe FollowerEvaluating BenchmarksConsiderations for Technology SelectionUnderstanding the Building BlocksLooking to a Guide for AdviceUsing AnalystsLooking to Market TrendsSummary
3. Managing Risk in Data Projects
Categories of RiskTechnology RiskTeam RiskRequirements RiskManaging RiskCategorizing Risk in Your ArchitectureTechnology RiskStrength of the TeamOther TeamsRequirements RiskTying This All TogetherUsing Prototypes and Proofs of ConceptBuild Two to Three WaysBuild PoCs and Then Throw Them AwayDeployment ConsiderationsUsing InterfacesStart Building EarlyTest Often and Keep RecordsMonitoring and AlertingCommunicating RiskCollaborate and Gain Buy-InShare the RiskUsing Risk as a Negotiation ToolSummary
4. Interface Design
The Human BodyThe Human Body Versus a Data ArchitectureDecouplingDecoupling ConsiderationsSpecializationWhat Makes a Good Interface DesignThe ContractThe AbstractionVersioningBeing DefensiveDocumentation and Naming for InterfacesNonfunctional ConsiderationsAvailabilityResponse-Time GuaranteesLoad CapacityUsing Testing to Determine SLAsCommon Interface ExamplesPublish–SubscribeRequest–Response Asynchronous ExampleRequest–Response Synchronous ExampleSummary
5. Distributed Storage Systems
Attributes of Distributed Storage SystemsStorage System GenealogyPartitioningMutation OptionsRead PathsAvailability Versus ConsistencyPrimary Use CasesStorage System BreakdownHDFSS3 and Object StoresApache HBaseApache CassandraElasticsearch and Apache SolrNewcomers: Apache Kudu and CockroachDBIn-Memory Storage SystemsSummary
6. The Meta of Enterprise Data
Reasons to Care About MetadataVisibilityRelationshipsRegulationTypes of Metadata in a Data ArchitectureData at RestData in MotionMetadata for Source DataMetadata About Data ProcessingReports and DashboardsMetadata CollectionDeclarative Metadata CollectionDiscovery of MetadataMetadata Management in PracticeSummary
7. Ensuring Data Integrity
Examples of Building Data Pipelines to Ensure Data IntegrityPredefined Data PipelinesValidation of Data PipelinesRow CountsDistinct CountFull-Byte ComparisonChecksum ComparisonSummary
8. Data Processing
Attributes of Processing EnginesDAG ManagementCompute IsolationPerformanceFault ToleranceInteraction ModelBatch and/or StreamingData Processing over TimeSummary
Index

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Foundations for Architecting Data Solutions

by Ted Malaska, Jonathan Seidman

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