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Building Modern Data Applications Using Databricks Lakehouse

Name: Building Modern Data Applications Using Databricks Lakehouse
Author: Will Girten
ISBN: 9781801073233

by Will Girten

October 2024

Intermediate to advanced

246 pages

6h 22m

English

Packt Publishing

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Building Modern Data Applications Using Databricks Lakehouse
Contributors
About the author
About the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1:Near-Real-Time Data Pipelines for the Lakehouse
Chapter 1: An Introduction to Delta Live Tables
Technical requirementsThe emergence of the lakehouseThe Lambda architectural patternIntroducing the medallion architectureThe Databricks lakehouseThe maintenance predicament of a streaming applicationWhat is the DLT framework?How is DLT related to Delta Lake?Introducing DLT conceptsStreaming tablesMaterialized viewsViewsPipelinePipeline triggersWorkflowTypes of Databricks computeDatabricks RuntimeUnity CatalogA quick Delta Lake primerThe architecture of a Delta tableThe contents of a transaction commitSupporting concurrent table reads and writesTombstoned data filesCalculating Delta table stateTime travelTracking table changes using change data feedA hands-on example – creating your first Delta Live Tables pipelineSummary
Chapter 2: Applying Data Transformations Using Delta Live Tables
Technical requirementsIngesting data from input sourcesIngesting data using Databricks Auto LoaderScalability challenge in structured streamingUsing Auto Loader with DLTApplying changes to downstream tablesAPPLY CHANGES commandThe DLT reconciliation processPublishing datasets to Unity CatalogWhy store datasets in Unity Catalog?Creating a new catalogAssigning catalog permissionsData pipeline settingsThe DLT product editionPipeline execution modeDatabricks runtimePipeline cluster typesA serverless compute versus a traditional computeLoading external dependenciesData pipeline processing modesHands-on exercise – applying SCD Type 2 changesSummary
Chapter 3: Managing Data Quality Using Delta Live Tables
Technical requirementsDefining data constraints in Delta LakeUsing temporary datasets to validate data processingAn introduction to expectationsExpectation compositionHands-on exercise – writing your first data quality expectationActing on failed expectationsHands-on example – failing a pipeline run due to poor data qualityApplying multiple data quality expectationsDecoupling expectations from a DLT pipelineHands-on exercise – quarantining bad data for correctionSummary
Chapter 4: Scaling DLT Pipelines
Technical requirementsScaling compute to handle demandHands-on example – setting autoscaling properties using the Databricks REST APIAutomated table maintenance tasksWhy auto compaction is importantVacuuming obsolete table filesMoving compute closer to the dataOptimizing table layouts for faster table updatesRewriting table files during updatesData skipping using table partitioningDelta Lake Z-ordering on MERGE columnsImproving write performance using deletion vectorsServerless DLT pipelinesIntroducing Enzyme, a performance optimization layerSummary
Part 2:Securing the Lakehouse Using the Unity Catalog

Chapter 5: Mastering Data Governance in the Lakehouse with Unity Catalog
Technical requirementsUnderstanding data governance in a lakehouseIntroducing the Databricks Unity CatalogA problem worth solvingAn overview of the Unity Catalog architectureUnity Catalog-enabled cluster typesUnity Catalog object modelEnabling Unity Catalog on an existing Databricks workspaceIdentity federation in Unity CatalogData discovery and catalogingTracking dataset relationships using lineageObservability with system tablesTracing the lineage of other assetsFine-grained data accessHands-on example – data masking healthcare datasetsSummary
Chapter 6: Managing Data Locations in Unity Catalog
Technical requirementsCreating and managing data catalogs in Unity CatalogManaged data versus external dataSaving data to storage volumes in Unity CatalogSetting default locations for data within Unity CatalogIsolating catalogs to specific workspacesCreating and managing external storage locations in Unity CatalogStoring cloud service authentication using storage credentialsQuerying external systems using Lakehouse FederationHands-on lab – extracting document text for a generative AI pipelineGenerating mock documentsDefining helper functionsChoosing a file format randomlyCreating/assembling the DLT pipelineSummary
Chapter 7: Viewing Data Lineage Using Unity Catalog
Technical requirementsIntroducing data lineage in Unity CatalogTracing data origins using the Data Lineage REST APIVisualizing upstream and downstream transformationsIdentifying dependencies and impactsHands-on lab – documenting data lineage across an organizationSummary
Part 3:Continuous Integration, Continuous Deployment, and Continuous Monitoring
Chapter 8: Deploying, Maintaining, and Administrating DLT Pipelines Using Terraform
Technical requirementsIntroducing the Databricks provider for TerraformSetting up a local Terraform environmentImporting the Databricks Terraform providerConfiguring workspace authenticationDefining a DLT pipeline source notebookApplying workspace changesConfiguring DLT pipelines using TerraformnamenotificationchanneldevelopmentcontinuouseditionphotonconfigurationlibraryclustercatalogtargetstorageAutomating DLT pipeline deploymentHands-on exercise – deploying a DLT pipeline using VS CodeSetting up VS CodeCreating a new Terraform projectDefining the Terraform resourcesDeploying the Terraform projectSummary
Chapter 9: Leveraging Databricks Asset Bundles to Streamline Data Pipeline Deployment
Technical requirementsIntroduction to Databricks Asset BundlesElements of a DAB configuration fileSpecifying a deployment modeDatabricks Asset Bundles in actionUser-to-machine authenticationMachine-to-machine authenticationInitializing an asset bundle using templatesHands-on exercise – deploying your first DABHands-on exercise – simplifying cross-team collaboration with GitHub ActionsSetting up the environmentConfiguring the GitHub ActionTesting the workflowVersioning and maintenanceSummary
Chapter 10: Monitoring Data Pipelines in Production
Technical requirementsIntroduction to data pipeline monitoringExploring ways to monitor data pipelinesUsing DBSQL Alerts to notify data validityPipeline health and performance monitoringHands-on exercise – querying data quality events for a datasetData quality monitoringIntroducing Lakehouse MonitoringHands-on exercise – creating a lakehouse monitorBest practices for production failure resolutionHandling pipeline update failuresRecovering from table transaction failureHands-on exercise – setting up a webhook alert when a job runs longer than expectedSummary
Index
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Content preview from Building Modern Data Applications Using Databricks Lakehouse

8 Deploying, Maintaining, and Administrating DLT Pipelines Using Terraform

In this chapter, we’re going to explore how an automation tool such as Terraform can be used to express data pipelines as code, commonly referred to as Infrastructure as Code (IAC), in Databricks. We’ll look at how to set up a local Terraform development environment using popular code editors such as VS Code so that we can experiment with deploying different resources to a Databricks workspace. Next, we’ll dive into how to represent data pipelines using Terraform and how to configure different aspects of a Delta Live Tables (DLT) pipeline. We’ll also look at how we can automate the validation and deployment of IaC to different Databricks workspaces, including a production ...

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Building Modern Data Applications Using Databricks Lakehouse

by Will Girten

8

Deploying, Maintaining, and Administrating DLT Pipelines Using Terraform

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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