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使用 Databricks Lakeflow 声明式管道、Protobuf 和 BSR 统一人工数据提取和实时更新.pdf

上传人: Fl****zo 编号:718752 2025-06-22 38页 2.46MB

1、Copyright 2025 by Clinician NexusCopyright 2025 by Clinician NexusHuman-Curated Data Ingestion with Databricks DLT,Protobuf,and the BufSchema RegistryDwight Whitlock|Clinician NexusJune 10,2025Copyright 2025 by Clinician NexusForward-looking StatementThis presentation has been prepared for informati

2、onal purposes only.The information set forth herein does not purport to be complete or contain all relevant information.Statements contained herein are made as of the date of this presentation unless stated otherwise.This presentation and the accompanying oral commentary may contain forward-looking

3、statements.In some cases,forward-looking statements can be identified by terms such as“may”,“will”,“should”,“expects”,“plans”,“anticipates”,“could”,“intends”,“projects”,“believes”,“estimates”,“predicts”,or“continue”,or the negative of these words or other similar terms or expressions that concern Da

4、tabricks expectations,strategy,plans,or intentions.Forward-looking statements are based on information available at the time those statements are made and are inherently subject to risks and uncertainties that could cause actual results to differ materially from those expressed in or suggested by th

5、e forward-looking statements.Forward-looking statements should not be read as a guarantee of future performance or outcomes.Except as required by law,Databricks does not undertake any obligation to publicly update or revise any forward-looking statement,whether as a result of new information,future

6、developments or otherwise.3Copyright 2025 by Clinician NexusPersonal disclaimerRed Stapler=movie referenceTPS Report=fictional schemaBaseline knowledge junior data engineerI hope youve seen Office SpaceCopyright 2025 by Clinician NexusData Clinician NexusCopyright 2025 by Clinician NexusBackgroundWe

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本文介绍了如何使用Databricks DLT、Protocol Buffers(Protobuf)和Buf Schema Registry处理和治理医疗保健组织的薪酬、绩效和职位分类数据。关键点如下: 1. **数据治理挑战**:面临不断的数据模式演变和多种数据消费者,需要一致的数据验证和治理。 2. **Protobuf的应用**:采用Protobuf作为跨平台、高效且易于管理的schema格式,支持强类型和配置,广泛被行业接受。 3. **Buf Schema Registry**:介绍Buf Schema Registry的特性和优势,如提前检测破坏性变更,实现更好的依赖管理和API文档。 4. **数据清洗**:通过DLT实现数据清洗的迭代过程,使用Slowly Changing Dimensions(SCD)架构追踪行随时间的变化。 5. **业务价值**:DLT提供事件驱动、无批处理等待、按需计算、无服务器计算的可扩展性,并保持所有历史数据。 6. **性能与成本效率**:优化计算时间和成本,通过Bufstream和Kubernetes实现计算与存储分离。 7. **实施挑战**:面临HIPAA工作区内的预览功能、服务器less计算、IAM权限等挑战。 文章强调了通过以上技术和方法,企业能够高效、可扩展且成本效益地处理数据,同时保持数据质量和服务水平。
"如何应对数据类型变化?" "Buf Schema注册表有何优势?" "DLT如何提升数据处理效率?"
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