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用 Azure Synapse 进行端到端分析

Synapse Analytics
Cosmos DB
数据工厂
Databricks
函数
事件中心

此示例方案演示如何使用广泛的 Azure 数据服务系列来构建能够处理组织中最常见的数据挑战的新式数据平台。This example scenario demonstrates how to use the extensive family of Azure Data Services to build a modern data platform capable of handling the most common data challenges in an organization.

本文中所述的解决方案组合了一系列 Azure 服务,这些服务将从不同的源中引入、存储、处理、充实和提供数据与见解 (结构化、半结构化、非结构化和流式处理) 。The solution described in this article combines a range of Azure services that will ingest, store, process, enrich, and serve data and insights from different sources (structured, semi-structured, unstructured, and streaming).

相关用例Relevant use cases

此方法还可以用来:This approach can also be used to:

  • 建立企业范围的数据中心,其中包含用于结构化数据的数据仓库,以及用于半结构化数据和非结构化数据的 data lake。Establish an enterprise-wide data hub, consisting of a data warehouse for structured data and a data lake for semi-structured and unstructured data. 此数据中心成为报表数据的单一事实来源。This data hub becomes the single source of truth for your reporting data.
  • 使用大数据处理技术将关系数据源与其他非结构化数据集集成。Integrate relational data sources with other unstructured datasets, with the use of big data processing technologies.
  • 使用语义建模和强大的可视化工具来简化数据分析。Use semantic modeling and powerful visualization tools for simpler data analysis.
  • 在组织内或通过受信任的外部合作伙伴共享数据集。Share datasets within the organization or with trusted external partners.

体系结构Architecture

使用 Azure 数据服务的新式数据平台体系结构Architecture for a modern data platform using Azure data services

备注

  • 此体系结构涵盖的服务只是更大的 Azure 服务系列的一个子集。The services covered by this architecture are only a subset of a much larger family of Azure services. 可以通过使用本设计未涵盖的其他服务或功能来实现类似的结果。Similar outcomes can be achieved by using other services or features that are not covered by this design.
  • 分析用例的特定业务要求还可能要求使用不在此设计中考虑的不同服务或功能。Specific business requirements for your analytics use case may also ask for the use of different services or features that are not considered in this design.

分析用例Analytics Use Cases

关系图左侧的不同数据源阐释了体系结构涵盖的分析用例。The analytics use cases covered by the architecture are illustrated by the different data sources on the left-hand side of the diagram. 数据从底部流向解决方案,如下所示:Data flows through the solution from the bottom up as follows:

具有 Cosmos DB 的 Azure 数据服务、云本机 HTAPAzure Data Services, cloud native HTAP with Cosmos DB

  1. 使用适用于 Azure Cosmos DB 的 Azure Synapse 链接,你可以通过使用 azure Synapse 工作区中提供的两个分析引擎,对 Azure Cosmos DB 中的操作数据运行近乎实时的分析: SQL 无服务器Spark 池Azure Synapse Link for Azure Cosmos DB enables you to run near real-time analytics over operational data in Azure Cosmos DB, by using the two analytics engines available from your Azure Synapse workspace: SQL Serverless and Spark Pools.

  2. 使用 SQL 无服务器查询Spark 池笔记本,你可以访问 Cosmos DB 分析存储 ,然后将来自你的近乎实时操作数据的数据集与 data lake 或数据仓库中的数据合并在一起。Using either a SQL Serverless query or a Spark Pool notebook, you can access the Cosmos DB analytical store and then combine datasets from your near real-time operational data with data from your data lake or from your data warehouse.

  3. SQL 无服务器查询生成的数据集可以保存在 data lake 中。The resulting datasets from your SQL Serverless queries can be persisted in your data lake. 如果使用 Spark 笔记本,则生成的数据集可以保存在 data lake 或数据仓库中, (SQL 池) 。If you are using Spark notebooks, the resulting datasets can be persisted in your data lake or data warehouse (SQL pool).

  4. 将 Azure Synapse SQL 池中或 data lake 中的相关数据加载到 Power BI 数据集中 ,以实现数据可视化。Load relevant data from the Azure Synapse SQL pool or data lake into Power BI datasets for data visualization. Power BI 模型 实现了一个语义模型,以简化业务数据和关系的分析。Power BI models implement a semantic model to simplify the analysis of business data and relationships.

  5. 业务分析师使用 Power BI 报表和仪表板来分析数据并派生业务见解。Business analysts use Power BI reports and dashboards to analyze data and derive business insights.

  6. 还可以使用 Azure 数据共享将数据安全地共享到其他业务部门或外部可信合作伙伴。Data can also be securely shared to other business units or external trusted partners using Azure Data Share.

关系数据库Relational databases

  1. 使用 Azure Synapse 管道 从本地和云中的各种数据库中提取数据。Use Azure Synapse pipelines to pull data from a wide variety of databases, both on-premises and in the cloud. 管道可以根据预定义的计划来触发,以响应事件,也可以通过 REST Api 进行显式调用。Pipelines can be triggered based on a pre-defined schedule, in response to an event, or can be explicitly called via REST APIs.

  2. 在 Azure Synapse 管道中,使用复制数据活动将从关系数据库复制的数据暂存到Azure Data Lake Store 第2代Data Lake 的原始区域From the Azure Synapse pipeline, use a Copy Data activity to stage the data copied from the relational databases into the Raw zone of your Azure Data Lake Store Gen 2 data lake. 你可以将数据保存为带分隔符的文本格式,或压缩为 Parquet 文件。You can save the data in delimited text format or compressed as Parquet files.

  3. 使用数据流、 SQL 无服务器查询Spark 笔记本验证、转换数据集,并将其移动到 Data lake 中的特选区域。Use either Data Flows, SQL Serverless queries, or Spark notebooks to validate, transform, and move the datasets into your Curated zone in your data lake.

    1. 作为数据转换的一部分,你可以 使用标准的 t-sql 或 Spark 笔记本从 SQL 池中调用机器学习模型。As part of your data transformations, you can invoke machine learning models from your SQL pools using standard T-SQL or Spark notebooks. 可以使用这些 ML 模型来丰富数据集,并生成更多的业务见解。These ML models can be used to enrich your datasets and generate further business insights. 可从 Azure 认知服务azure ml 中的自定义 ML 模型使用这些机器学习模型。These machine learning models can be consumed from Azure Cognitive Services or custom ML models from Azure ML.
  4. 可以直接从 data lake 特选区域提供最终的数据集,也可以使用复制数据活动将最终的数据集导入到 SQL 池表中,使用 复制命令 快速引入。You can serve your final dataset directly from the data lake Curated zone or you can use Copy Data activity to ingest the final dataset into your SQL pool tables using the COPY command for fast ingestion.

  5. 将 Azure Synapse SQL 池中或 data lake 中的相关数据加载到 Power BI 数据集中 ,以实现数据可视化。Load relevant data from the Azure Synapse SQL pool or data lake into Power BI datasets for data visualization. Power BI 模型 实现了一个语义模型,以简化业务数据和关系的分析。Power BI models implement a semantic model to simplify the analysis of business data and relationships.

  6. 业务分析师使用 Power BI 报表和仪表板来分析数据并派生业务见解。Business analysts use Power BI reports and dashboards to analyze data and derive business insights.

  7. 还可以使用 Azure 数据共享将数据安全地共享到其他业务部门或外部可信合作伙伴。Data can also be securely shared to other business units or external trusted partners using Azure Data Share.

半结构化数据源Semi-structured data sources

  1. 使用 Azure Synapse 管道 从本地和云中的各种半结构化数据源中提取数据。Use Azure Synapse pipelines to pull data from a wide variety of semi-structured data sources, both on-premises and in the cloud. 例如:For example:

    • 从包含 CSV 或 JSON 文件的基于文件的源引入数据。Ingest data from file-based sources containing CSV or JSON files.
    • 连接到非 SQL 数据库(如 Cosmos DB 或 Mongo DB)。Connect to No-SQL databases such as Cosmos DB or Mongo DB.
    • 调用 SaaS 应用程序提供的用于管道的数据源的 REST Api。Call REST APIs provided by SaaS applications that will function as your data source for the pipeline.
  2. 在 Azure Synapse 管道中,使用复制数据活动将从半结构化数据源复制的数据暂存到Azure Data Lake Store 第2代data Lake 的原始区域From the Azure Synapse pipeline, use a Copy Data activity to stage the data copied from the semi-structured data sources into the Raw zone of your Azure Data Lake Store Gen 2 data lake. 应保存数据,保留从数据源获取的原始格式。You should save data preserving the original format as acquired from the data sources.

  3. 使用数据流、 SQL 无服务器查询Spark 笔记本验证、转换数据集,并将其移动到 Data lake 中的特选区域。Use either Data Flows, SQL Serverless queries or Spark notebooks to validate, transform and move the your datasets into your Curated zone in your data lake. SQL 无服务器查询将基础 CSVParquetJSON 文件公开为外部表,以便可以使用 t-sql 查询它们。SQL Serverless queries expose underlying CSV, Parquet or JSON files as external tables so they can be queried using T-SQL.

    1. 作为数据转换的一部分,你可以 使用标准的 t-sql 或 Spark 笔记本从 SQL 池中调用机器学习模型。As part of your data transformations, you can invoke machine learning models from your SQL pools using standard T-SQL or Spark notebooks. 可以使用这些 ML 模型来丰富数据集,并生成更多的业务见解。These ML models can be used to enrich your datasets and generate further business insights. 可从 Azure 认知服务azure ml 中的自定义 ML 模型使用这些机器学习模型。These machine learning models can be consumed from Azure Cognitive Services or custom ML models from Azure ML.
  4. 可以直接从 data lake 特选区域提供最终的数据集,也可以使用复制数据活动将最终的数据集导入到 SQL 池表中,使用 复制命令 快速引入。You can serve your final dataset directly from the data lake Curated zone or you can use Copy Data activity to ingest the final dataset into your SQL pool tables using the COPY command for fast ingestion.

  5. 将 Azure Synapse SQL 池中或 data lake 中的相关数据加载到 Power BI 数据集中 ,以实现数据可视化。Load relevant data from the Azure Synapse SQL pool or data lake into Power BI datasets for data visualization. Power BI 模型 实现了一个语义模型,以简化业务数据和关系的分析。Power BI models implement a semantic model to simplify the analysis of business data and relationships.

  6. 业务分析师使用 Power BI 报表和仪表板来分析数据并派生业务见解。Business analysts use Power BI reports and dashboards to analyze data and derive business insights.

  7. 还可以使用 Azure 数据共享将数据安全地共享到其他业务部门或外部可信合作伙伴。Data can also be securely shared to other business units or external trusted partners using Azure Data Share.

非结构化数据源Non-structured data sources

  1. 使用 Azure Synapse 管道 从本地和云中的各种非结构化数据源中提取数据。Use Azure Synapse pipelines to pull data from a wide variety of non-structured data sources, both on-premises and in the cloud. 例如:For example:

    • 从包含源文件的基于文件的源中引入视频、图像、音频或任意文本。Ingest video, image, audio, or free text from file-based sources containing the source files.
    • 调用 SaaS 应用程序提供的用于管道的数据源的 REST Api。Call REST APIs provided by SaaS applications that will function as your data source for the pipeline.
  2. 在 Azure Synapse 管道中,使用复制数据活动将从非结构化数据源复制的数据暂存到Azure Data Lake Store 第2代Data Lake 的原始区域From the Azure Synapse pipeline, use a Copy Data activity to stage the data copied from the non-structured data sources into the Raw zone of your Azure Data Lake Store Gen 2 data lake. 应保存数据,保留从数据源获取的原始格式。You should save data preserving the original format as acquired from the data sources.

  3. 使用 Spark 笔记本 验证、转换和将数据集导入到 data lake 中的特选区域。Use Spark notebooks to validate, transform, enrich and move the your datasets into your Curated zone in your data lake.

    1. 作为数据转换的一部分,你可以 使用标准的 t-sql 或 Spark 笔记本从 SQL 池中调用机器学习模型。As part of your data transformations, you can invoke machine learning models from your SQL pools using standard T-SQL or Spark notebooks. 可以使用这些 ML 模型来丰富数据集,并生成更多的业务见解。These ML models can be used to enrich your datasets and generate further business insights. 可从 Azure 认知服务azure ml 中的自定义 ML 模型使用这些机器学习模型。These machine learning models can be consumed from Azure Cognitive Services or custom ML models from Azure ML.
  4. 你可以直接从 data lake 特选区域提供最终的数据集,也可以使用复制数据活动将最终的数据集引入到数据仓库表中,使用 复制命令 快速引入。You can serve your final dataset directly from the data lake Curated zone or you can use Copy Data activity to ingest the final dataset into your data warehouse tables using the COPY command for fast ingestion.

  5. 将 Azure Synapse SQL 池中或 data lake 中的相关数据加载到 Power BI 数据集中 ,以实现数据可视化。Load relevant data from the Azure Synapse SQL pool or data lake into Power BI datasets for data visualization. Power BI 模型 实现了一个语义模型,以简化业务数据和关系的分析。Power BI models implement a semantic model to simplify the analysis of business data and relationships.

  6. 业务分析师使用 Power BI 报表和仪表板来分析数据并派生业务见解。Business analysts use Power BI reports and dashboards to analyze data and derive business insights.

  7. 还可以使用 Azure 数据共享将数据安全地共享到其他业务部门或外部可信合作伙伴。Data can also be securely shared to other business units or external trusted partners using Azure Data Share.

流式处理Streaming

  1. 使用 Azure 事件中心或 Azure IoT 中心 引入客户端应用程序或 IoT 设备生成的数据流。Use Azure Event Hubs or Azure IoT Hubs to ingest data streams generated by client applications or IoT devices. 事件中心或 IoT 中心随后会引入并存储流数据,从而保留接收的事件的顺序。Event Hub or IoT Hub will then ingest and store streaming data preserving the sequence of events received. 然后,使用者可以连接到事件中心或 IoT 中心终结点,并检索用于处理的消息。Consumers can then connect to Event Hub or IoT Hub endpoints and retrieve messages for processing.

  2. 配置事件中心捕获IoT 中心存储终结点,将事件副本保存到Azure Data Lake Store 第2代Data Lake 的原始区域Configure Event Hub Capture or IoT Hub Storage Endpoints to save a copy of the events into the Raw zone of your Azure Data Lake Store Gen 2 data lake. 此功能实现了 Lambda 体系结构模式 的 "冷路径",允许你使用 SQL 无服务器查询或使用 SQL 无服务器查询Spark 笔记本 按照上述半结构化数据源的模式对保存在 data lake 中的流数据执行历史分析和趋势分析。This feature implements the "Cold Path" of the Lambda architecture pattern and allows you to perform historical and trend analysis on the stream data saved in your data lake using SQL Serverless queries or Spark notebooks following the pattern for semi-structured data sources described above.

  3. 使用 流分析作业 来实现 Lambda 体系结构模式 的 "热路径",并从传输中的流数据派生见解。Use a Stream Analytics job to implement the "Hot Path" of the Lambda architecture pattern and derive insights from the stream data in transit. 为来自事件中心或 IoT 中心的数据流定义至少一个输入,一个查询用于处理输入数据流,另一个 Power BI 输出发送到查询结果将发送到的位置。Define at least one input for the data stream coming from your Event Hub or IoT Hub, one query to process the input data stream and one Power BI output to where the query results will be sent to.

    1. 在通过流分析进行数据处理的过程中,您可以调用机器学习模型来丰富您的流数据集,并根据生成的预测来推动业务决策。As part of your data processing with Stream Analytics, you can invoke machine learning models to enrich your stream datasets and drive business decisions based on the predictions generated. 可从 Azure 认知服务或 Azure 机器学习中的自定义 ML 模型使用这些机器学习模型。These machine learning models can be consumed from Azure Cognitive Services or from custom ML models in Azure Machine learning.
  4. 然后,业务分析师使用 Power BI 的实时数据集和仪表板 功能,直观显示流分析查询生成的快速变化见解。Business analysts then use Power BI real-time datasets and dashboard capabilities for to visualize the fast changing insights generated by your Stream Analytics query.

发现和控制Discover and Govern

在大型企业环境中,数据监管是一项常见挑战。Data governance is a common challenge in large enterprise environments. 一方面,业务分析人员需要能够发现和理解可帮助他们解决业务问题的数据资产。On one hand, business analysts need to be able to discover and understand data assets that can help them solve business problems. 另一方面,首席数据官需要了解业务数据的隐私和安全性。On the other hand, Chief Data Officers want insights on privacy and security of business data.

Azure PurviewAzure Purview

  1. 使用Azure 监控范围发现数据资产的数据发现和管理见解、数据分类敏感信息,涵盖整个组织数据布局。Use Azure Purview for data discovery and governance insights on your data assets, data classification and sensitivity covering the entire organizational data landscape.

  2. Azure 监控范围可帮助你维护具有特定业务术语的 业务术语表 ,用户需要这些术语来了解数据集的含义,以及如何在组织中使用它们。Azure Purview can help you maintain a business glossary with the specific business terminology required for users to understand the semantics of what datasets mean and how they are meant to be used across the organization.

  3. 你可以 注册所有数据源 并设置 常规扫描 ,以自动目录和更新有关组织中的数据资产的相关元数据。You can register all your data sources and setup regular scans to automatically catalog and update relevant metadata about data assets in the organization. Azure 监控范围还可以根据 Azure 数据工厂或 Azure Synapse 管道中的信息自动添加 数据沿袭 信息。Azure Purview can also automatically add data lineage information based on information from Azure Data Factory or Azure Synapse pipelines.

  4. 数据分类数据敏感度 标签可根据常规扫描期间应用的预配置或海关规则自动添加到数据资产。Data Classification and Data Sensitivity labels can be added automatically to your data assets based on pre-configured or customs rules applied during the regular scans.

  5. 数据管理人员可以使用 Azure 监控范围生成的报告和见解来控制整个数据布局,并保护组织免受任何安全和隐私问题。Data governance professionals can use the reports and insights generated by Azure Purview to keep control over the entire data landscape and protect the organization against any security and privacy issues.

平台服务Platform Services

若要提高 Azure 解决方案的质量,请遵循 azure Well-Architected 框架 中定义的建议和指导原则优秀的体系结构:成本优化、卓越运营、性能效率、可靠性和安全性。In order to improve the quality of your Azure solutions, follow the recommendations and guidelines defined in the Azure Well-Architected Framework five pillars of architecture excellence: Cost Optimization, Operational Excellence, Performance Efficiency, Reliability, and Security.

按照这些建议,应将下面的服务视为设计的一部分:Following these recommendations, the services below should be considered as part of the design:

  1. Azure Active Directory:标识服务、跨 Azure 工作负荷的单一登录和多重身份验证。Azure Active Directory: identity services, single sign-on and multi-factor authentication across Azure workloads.
  2. Azure 成本管理:通过 azure 工作负荷进行金融管理。Azure Cost Management: financial governance over your Azure workloads.
  3. Azure Key Vault:保护凭据和证书管理。Azure Key Vault: secure credential and certificate management. 例如, Azure Synapse 管道Azure Synapse Spark 池azure ML 可以从 Azure Key Vault 检索用于安全访问数据存储的凭据和证书。For example, Azure Synapse Pipelines, Azure Synapse Spark Pools and Azure ML can retrieve credentials and certificates from Azure Key Vault used to securely access data stores.
  4. Azure Monitor:收集、分析和处理 Azure 资源的遥测信息,以主动识别问题并最大限度地提高性能和可靠性。Azure Monitor: collect, analyze, and act on telemetry information of your Azure resources to proactively identify problems and maximize performance and reliability.
  5. Azure 安全中心:增强和监视 azure 工作负荷的安全状况。Azure Security Center: strengthen and monitor the security posture of your Azure workloads.
  6. Azure DevOps & GitHub:实现 DevOps 做法,以将自动化和符合性强制用于 Azure Synapse 和 azure ML 的工作负载开发和部署管道。Azure DevOps & GitHub: implement DevOps practices to enforce automation and compliance to your workload development and deployment pipelines for Azure Synapse and Azure ML.
  7. Azure 策略:实现组织标准和监管,实现资源一致性、法规遵从性、安全性、成本和管理。Azure Policy: implement organizational standards and governance for resource consistency, regulatory compliance, security, cost, and management.

体系结构组件Architecture components

以下 Azure 服务已在体系结构中使用:The following Azure services have been used in the architecture:

  • Azure Synapse AnalyticsAzure Synapse Analytics
  • Azure Data Lake Gen2Azure Data Lake Gen2
  • Azure Cosmos DBAzure Cosmos DB
  • Azure 认知服务Azure Cognitive Services
  • Azure 机器学习Azure Machine Learning
  • Azure 事件中心Azure Event Hubs
  • Azure IoT 中心Azure IoT Hub
  • Azure 流分析Azure Stream Analytics
  • Azure PurviewAzure Purview
  • Azure Data ShareAzure Data Share
  • Microsoft Power BIMicrosoft Power BI
  • Azure Active DirectoryAzure Active Directory
  • Azure 成本管理Azure Cost Management
  • Azure Key VaultAzure Key Vault
  • Azure MonitorAzure Monitor
  • Azure 安全中心Azure Security Center
  • Azure DevOpsAzure DevOps
  • Azure PolicyAzure Policy
  • GitHubGitHub

备选方法Alternatives

注意事项Considerations

选择此体系结构中的技术是因为每个技术都提供必要的功能来处理组织中最常见的数据挑战。The technologies in this architecture were chosen because each of them provides the necessary functionality to handle the most common data challenges in an organization. 这些服务满足可伸缩性和可用性要求,同时帮助他们控制成本。These services meet the requirements for scalability and availability, while helping them control costs. 此体系结构涵盖的服务只是更大的 Azure 服务系列的一个子集。The services covered by this architecture are only a subset of a much larger family of Azure services. 可以通过使用本设计未涵盖的其他服务或功能来实现类似的结果。Similar outcomes can be achieved by using other services or features not covered by this design.

分析用例的特定业务要求还可能要求使用不在此设计中考虑的不同服务或功能。Specific business requirements for your analytics use cases may also ask for the use of different services or features not considered in this design.

也可以为可在其中开发和测试工作负荷的预生产环境实现类似的体系结构。Similar architecture can also be implemented for pre-production environments where you can develop and test your workloads. 请考虑工作负荷的具体要求和每个服务的功能,以实现经济高效的预生产环境。Consider the specific requirements for your workloads and the capabilities of each service for a cost-effective pre-production environment.

定价Pricing

通常,使用 Azure 定价计算器 来估算成本。In general, use the Azure pricing calculator to estimate costs. 理想的单个定价层和体系结构中包含的每个服务的总总成本取决于要处理和存储的数据量,以及预期的可接受的性能级别。The ideal individual pricing tier and the total overall cost of each service included in the architecture is dependent on the amount of data to be processed and stored and the acceptable performance level expected. 使用下面的指南来了解有关每个服务的定价方式的详细信息:Use the guide below to learn more about how each service is priced:

  • Azure Synapse Analytics 无服务器体系结构允许单独缩放计算和存储级别。Azure Synapse Analytics serverless architecture allows you to scale your compute and storage levels independently. 计算资源根据使用情况收费,你可以根据需要缩放或暂停这些资源。Compute resources are charged based on usage, and you can scale or pause these resources on demand. 存储资源按 TB 计费,因此,引入的数据越多,费用就越高。Storage resources are billed per terabyte, so your costs will increase as you ingest more data.

  • Azure Data Lake 第2代 根据存储的数据量和读取和写入数据的事务数进行收费。Azure Data Lake Gen 2 is charged based on the amount of data stored and based on the number of transactions to read and write data.

  • Azure 事件中心azure IoT 中心 根据处理消息流所需的计算资源量进行收费。Azure Event Hubs and Azure IoT Hubs are charged based on the amount of compute resources required to process your message streams.

  • Azure 机器学习 费用来自用于训练和部署机器学习模型的计算资源量。Azure Machine Learning charges come from the amount of compute resources used to train and deploy your machine learning models.

  • 认知服务 的收费依据是对服务 api 进行的调用数。Cognitive Services is charged based on the number of call you make to the service APIs.

  • Azure 监控范围 基于目录中的数据资产数量和扫描所需的计算能力。Azure Purview is priced based on the number of data assets in the catalog and the amount of compute power required to scan them.

  • Azure 流分析 按照处理流查询所需的计算能力来收费。Azure Stream Analytics is charged based on the amount of compute power required to process your stream queries.

  • Power BI 提供不同的产品选项来满足不同的要求。Power BI has different product options for different requirements. Power BI Embedded 提供基于 Azure 的选项用于在应用程序中嵌入 Power BI 功能。Power BI Embedded provides an Azure-based option for embedding Power BI functionality inside your applications. 上述定价示例包括 Power BI Embedded 实例。A Power BI Embedded instance is included in the pricing sample above.

  • Azure CosmosDB 基于数据库所需的存储量和计算资源。Azure CosmosDB is priced based on the amount of storage and compute resources required by your databases.

后续步骤Next steps