什么是 Azure Synapse Analytics(以前称为 SQL DW)?What is Azure Synapse Analytics (formerly SQL DW)?

Azure Synapse 是一种无限制的分析服务,它将企业数据仓库和大数据分析结合在一起。Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. 它让你可以根据自己的条件自由查询数据,无论是使用无服务器的按需资源还是使用大规模预配的资源。It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources-at scale. Azure Synapse 将这两个领域结合在一起,提供统一的体验来引入、准备、管理和处理数据,以满足即时 BI 需求Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI needs

Azure Synapse 包含四个组件:Azure Synapse has four components:

  • SQL Analytics:基于 T-SQL 的完整分析 - 正式版SQL Analytics: Complete T-SQL based analytics - Generally Available
    • SQL 池(按预配的 DWU 付费)SQL pool (pay per DWU provisioned)
    • SQL 随选(按处理的 TB 付费)-(预览)SQL on-demand (pay per TB processed) - (Preview)
  • Spark:深度集成的 Apache Spark(预览)Spark: Deeply integrated Apache Spark (Preview)
  • 数据集成:混合数据集成(预览)Data Integration: Hybrid data integration (Preview)
  • 工作室:统一的用户体验。Studio: Unified user experience. (预览版)(Preview)

Azure Synapse 中的 SQL Analytics 和 SQL 池SQL Analytics and SQL pool in Azure Synapse

SQL Analytics 是指 Azure Synapse 中正式发布的企业数据仓库功能。SQL Analytics refers to the enterprise data warehousing features that are generally available in Azure Synapse.

SQL 池表示使用 SQL Analytics 时预配的分析资源集合。SQL pool represents a collection of analytic resources that are being provisioned when using SQL Analytics. SQL 池的大小由数据仓库单位 (DWU) 决定。The size of SQL pool is determined by Data Warehousing Units (DWU).

使用简单的 PolyBase T-SQL 查询导入大数据,然后利用 MPP 的功能运行高性能分析。Import big data with simple PolyBase T-SQL queries, and then use the power of MPP to run high-performance analytics. 进行集成和分析时,SQL Analytics 是企业为了获取更快且更可靠见解能够依赖的唯一事实来源。As you integrate and analyze, SQL Analytics will become the single version of truth your business can count on for faster and more robust insights.

大数据解决方案的关键组件Key component of a big data solution

数据仓库是基于云的端到端大数据解决方案的关键组件。Data warehousing is a key component of a cloud-based, end-to-end big data solution.

数据仓库解决方案

在云数据解决方案中,可从各种源将数据引入大数据存储中。In a cloud data solution, data is ingested into big data stores from a variety of sources. 将数据置于大数据存储中以后,Hadoop、Spark 和机器学习算法就可以准备和训练数据。Once in a big data store, Hadoop, Spark, and machine learning algorithms prepare and train the data. 当数据准备就绪可以进行复杂的分析时,SQL Analytics 就会使用 PolyBase 来查询大数据存储。When the data is ready for complex analysis, SQL Analytics uses PolyBase to query the big data stores. PolyBase 使用标准 T-SQL 查询将数据引入 SQL Analytics 表中。PolyBase uses standard T-SQL queries to bring the data into SQL Analytics tables.

SQL Analytics 通过按列存储将数据存储到关系表中。SQL Analytics stores data in relational tables with columnar storage. 此格式可显著降低数据存储费用,改进查询性能。This format significantly reduces the data storage costs, and improves query performance. 存储数据后,即可大规模地运行分析。Once data is stored, you can run analytics at massive scale. 与传统数据库系统相比,数分钟的分析查询只需数秒即可完成,数天的查询只需数小时。Compared to traditional database systems, analysis queries finish in seconds instead of minutes, or hours instead of days.

分析结果可以传输到世界各地的报告数据库或应用程序。The analysis results can go to worldwide reporting databases or applications. 然后即可通过业务分析获得进行明智的业务决策所需的见解。Business analysts can then gain insights to make well-informed business decisions.

后续步骤Next steps