book

Data Engineering with dbt

by Roberto Zagni

June 2023

Intermediate to advanced

578 pages

15h 37m

English

Packt Publishing

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ContributorsAbout the authorAbout the reviewers
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
Technical requirementsIntroducing SQLSQL basics – core concepts and commandsSQL core conceptsUnderstanding the categories of SQL commandsSetting up a Snowflake database with users and rolesCreating your Snowflake accountSetting up initial users, roles, and a database in SnowflakeCreating and granting your first roleQuerying data in SQL – syntax and operatorsSnowflake query syntaxSQL operatorsCombining data in SQL – the JOIN clauseCombining orders and customersJOIN typesVisual representation of join typesAdvanced – introducing window functionsWindow definitionWindow frame definitionSummaryFurther reading
Technical requirementsSetting up your GitHub accountIntroducing Version ControlCreating your GitHub accountSetting up your first repository for dbtSetting up your dbt Cloud accountSigning up for a dbt Cloud accountSetting up your first dbt Cloud projectAdding the default project to an empty repositoryComparing dbt Core and dbt Cloud workflowsdbt Core workflowsdbt Cloud workflowsExperimenting with SQL in dbt CloudExploring the dbt Cloud IDEExecuting SQL from the dbt IDEIntroducing the source and ref dbt functionsExploring the dbt default modelUsing ref and source to connect modelsRunning your first modelsTesting your first modelsEditing your first modelSummaryFurther reading
Technical requirementsWhat is and why do we need data modeling?Understanding dataWhat is data modeling?Why we need data modelingComplementing a visual data modelConceptual, logical, and physical data modelsConceptual data modelLogical data modelPhysical data modelTools to draw data modelsEntity-Relationship modelingMain notationCardinalityTime perspectiveAn example of an E-R model at different levels of detailGeneralization and specializationModeling use cases and patternsHeader-detail use caseHierarchical relationshipsForecasts and actualsLibraries of standard data modelsCommon problems in data modelsFan trapChasm trapModeling styles and architecturesKimball method or dimensional modeling or star schemaUnified Star SchemaInmon design styleData VaultData meshOur approach, the Pragmatic Data Platform - PDPSummaryFurther reading
Technical requirementsThe data life cycle and its evolutionUnderstanding the data flowData creationData movement and storageData transformationBusiness reportingFeeding back to the source systemsUnderstanding the modern data stackThe traditional data stackThe modern data stackDefining analytics engineeringThe roles in the modern data stackThe analytics engineerDataOps – software engineering best practices for dataVersion controlQuality assuranceThe modularity of the code baseDevelopment environmentsDesigning for maintainabilitySummaryFurther reading
Technical requirementsThe dbt Core workflow for ingesting and transforming dataIntroducing our stock tracking projectThe initial data model and glossarySetting up the project in dbt, Snowflake, and GitHubDefining data sources and providing reference dataDefining data sources in dbtLoading the first data for the portfolio projectHow to write and test transformationsWriting the first dbt modelReal-time lineage and project navigationDeploying the first dbt modelCommitting the first dbt modelConfiguring our project and where we store dataRe-deploying our environment to the desired schemaConfiguring the layers for our architectureEnsuring data quality with testsGenerating the documentationSummary
Technical requirementsWriting code for humansRefactoring our initial model to be human-readableCreating the architectural layersCreating the Staging layerGoals and contents of the staging modelsConnecting the REF model to the STGGoals and contents of the refined layerCreating the first data martSaving history is crucialSaving history with dbtSaving history using snapshotsConnecting the REF layer with the snapshotSummary

Adding dimensional dataCreating clear data models for the refined and data mart layersLoading the data of the first dimensionCreating and loading a CSV as a seedConfiguring the seeds and loading themAdding data types and a load timestamp to your seedBuilding the STG model for the first dimensionDefining the external data source for seedsCreating an STG model for the security dimensionAdding the default record to the STGSaving history for the dimensional dataSaving the history with a snapshotBuilding the REF layer with the dimensional dataAdding the dimensional data to the data martExercise – adding a few more hand-maintained dimensionsSummary
Technical requirementsKeeping consistency by reusing code – macrosRepetition is inherent in data projectsWhy copy and paste kills your future selfHow to write a macroRefactoring the “current” CTE into a macroFixing data loaded from our CSV fileThe basics of macro writingBuilding on the shoulders of giants – dbt packagesCreating dbt packagesHow to import a package in dbtBrowsing through noteworthy packages for dbtAdding the dbt-utils package to our projectSummary
Testing to provide reliabilityTypes of testsSingular testsGeneric testsDefining a generic testTesting the right things in the right placesWhat do we test?Where to test what?Testing our models to ensure good qualitySummary
Technical requirementsAgile development and collaborationDefining agile developmentApplying agile to data engineeringStarting a project in an agile wayOrganizing work the agile wayManaging the backlog in an agile wayBuilding reports in an agile wayS1 – designing a light data model for the data martS2 – designing a light data model for the REF layerS3.x – developing with dbt models the pipeline for the XYZ tableS4 – an acceptance test of the data produced in the data martS5 – development and verification of the report in the BI applicationSummary
Enabling collaborationCore collaboration practicesCollaboration with dbt CloudWorking with branches and PRsWorking with Git in dbt CloudThe dbt Cloud Git processKeeping your development environment healthySuggested Git branch namingAdopting frequent releasesMaking your first PRSummaryFurther reading
Technical requirementsDesigning your deployment automationWorking with dbt environmentsCreating our QA and PROD environmentsDeciding where to deployCreating jobsDesigning the architecture of your data platformNotificationsAdvanced automation – hooks and run-operationsHooksRun-operationsTable migrationsDocumentationLineage graphdbt-generated documentationSource freshness reportExposuresMarkdown documentationSummary
Technical requirementsBuilding for modularityModularity in the storage layerModularity in the refined layerModularity in the delivery layerManaging identityIdentity and semantics – defining your conceptsDifferent types of keysMain uses of keysMaster Data managementData for Master Data managementA light MDM approach with DBTSaving history at scaleUnderstanding the save_history macroUnderstanding the current_from_history macroSummary
Technical requirementsRefactoring and evolving modelsDealing with technical debtImplementing real-world code and business rulesReplacing snapshots with HIST tablesRenaming the REF_ABC_BANK_SECURITY_INFO modelHandling orphans in factsCalculating closed positionsCalculating transactionsPublishing dependable datasetsManaging data marts like APIsWhat shape should you use for your data mart?Self-completing dimensionsHistory in reports – that is, slowly changing dimensions type twoSummaryFurther reading
Technical requirementsIngestion patternsBasic setup for ingestionLoading data from filesExternal tablesLanding tablesHistory patternsStoring history with deletions – full loadStoring history with deletion – deletion listStoring history with multiple versions in the inputStoring history with PII and GDPR complianceHistory and schema evolutionSummaryFurther reading
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Content preview from Data Engineering with dbt

12 Deployment, Execution, and Documentation Automation

In this chapter, you will learn how to automate the operation of your data platform by setting up environments and jobs that automate the release and execution of your code following your deployment design.

You will learn about the notification system to alert the relevant people when a job is canceled or fails, either because of an error or a test not passing and providing an error.

You will also learn about the documentation, lineage graph, and source data freshness dashboard, which is automatically generated from the metadata provided in the YAML files of the project.

In this chapter, you will learn about the following topics: