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所在平台: Udemy |
课程主页: https://www.udemy.com/course/dbt-data-build-tool-mastery-5-practice-exams-new/
课程评论：没有评论
课程名称：dbt (数据构建工具) 精通：5个练习考试【新】 课程概述：本课程旨在帮助学习者掌握dbt（数据构建工具），并通过5个精心设计的练习考试来评估知识，涵盖500多个独特的问题，结合理论理解与真实场景。本课程将帮助您复习核心dbt概念，加深您对数据转换、建模、测试、文档和部署的理解。无论是为面试做准备还是增强实践技能，这些练习考试都模拟了真实世界中的挑战，考验您在生产环境中进行dbt项目的准备程度。 课程内容包括： 1. **dbt概述**：定义、目的、关键特性与好处；用例：数据转换、建模及数据质量测试；dbt在现代数据堆栈中的角色，以及与Snowflake、BigQuery、Redshift和Databricks的集成。 2. **安装与设置**：安装dbt（CLI和云选项）、初始化和结构化dbt项目、配置个人资料及连接不同数据仓库。 3. **核心概念**： - 模型：定义、SQL转换和物化类型（视图、表、增量、短暂） - 数据源：定义和管理数据源，源鲜度检查 - Seeds：将CSV文件作为种子加载和使用 - 在dbt中使用SQL：利用Jinja进行模板化、变量、宏和过滤器；使用ref和source函数编写查询；处理大数据集的最佳实践和查询优化。 4. **测试与验证**：内置测试（唯一性、非空、接受值）、使用Jinja的自定义SQL测试、数据验证策略及自动测试工作流。 5. **文档**：生成和维护项目文档，基于YAML的元数据管理，文档最佳实践。 6. **宏与重用性**：编写可重用的宏和参数化转换，安装和管理dbt包，如dbt-utils，使用高级模板技术和自定义过滤器。 7. **增量模型与性能**：创建增量模型，使用is_incremental逻辑，分区、聚类和性能调优的最佳实践。 8. **版本控制与协作**：使用Git进行版本控制，分支策略和环境管理，团队合作最佳实践，代码审查，及与CI/CD管道（如GitHub Actions、GitLab CI等）的集成。 9. **部署与调度**：在dbt Cloud中管理作业和调度，结合外部调度器如Airflow、Prefect和Dagster，开发、预发布和生产环境的管理。 10. **监控与调试**：分析日志、工件，使用dbt run/debug调试，跟踪查询性能和模型执行时间。 11. **高级主题**：自定义物化和跨数据库建模，管理多个仓库间的依赖关系，利用dbt模型进行数据应用和分析工作流。 通过这些练习考试，您将自信地复习所有主要dbt功能、技术及最佳实践，确保您为数据库面试和实际数据转换项目做好充分准备。
Master dbt (Data Build Tool) and assess your knowledge with 5 expertly crafted practice exams, covering 500+ unique questions that blend both conceptual understanding and real-world scenarios. This course helps you revise core dbt concepts, solidify your understanding of data transformations, modeling, testing, documentation, and deployment. Whether preparing for interviews or enhancing your practical expertise, these practice exams simulate real-world challenges and test your readiness for dbt projects in production environments.Topics Covered in Practice ExamsOverview of dbtDefinition, purpose, key features, and benefitsUse cases: data transformation, modeling, and data quality testingdbt's role in the modern data stack and integration with Snowflake, BigQuery, Redshift, and DatabricksInstallation and SetupInstalling dbt (CLI and Cloud options)Initializing and structuring a dbt projectConfiguring profiles and connecting to different data warehousesCore ConceptsModels: definitions, SQL transformations, and materialization types (view, table, incremental, ephemeral)Sources: defining and managing sources, source freshness checksSeeds: loading and using CSV files as seedsSQL in dbtUsing Jinja for templating, variables, macros, and filtersWriting queries with ref and source functionsQuery optimization and best practices for handling large datasetsTesting and ValidationBuilt-in tests (unique, not null, accepted values)Custom SQL-based tests using JinjaData validation strategies and automated test workflowsDocumentationGenerating and maintaining project documentationLineage graphs, YAML-based metadata management, and documentation best practicesMacros and ReusabilityWriting reusable macros and parameterized transformationsInstalling and managing dbt packages like dbt-utilsAdvanced templating techniques with custom filters and control flowIncremental Models and PerformanceCreating incremental models and using is_incremental logicPartitioning, clustering, and performance tuning best practicesDebugging and optimizing query execution plansVersion Control and CollaborationUsing Git for version control, branching strategies, and environment managementCollaboration best practices for teams and code reviewsIntegrating dbt with CI/CD pipelines using GitHub Actions, GitLab CI, etc.Deployment and SchedulingManaging jobs and schedules in dbt CloudIntegrating with external orchestrators like Airflow, Prefect, and DagsterEnvironment management for development, staging, and productionMonitoring and DebuggingAnalyzing logs, artifacts, and debugging with dbt run/debugMonitoring query performance and tracking model execution timesRunning and debugging tests within pipelinesAdvanced TopicsCustom materializations and cross-database modelingManaging dependencies across multiple warehousesLeveraging dbt models for data applications and analytics workflowsThese practice exams will help you confidently review all major dbt features, techniques, and best practices - ensuring you are fully prepared to excel in dbt interviews and real-world data transformation projects.
