更新存档
Azure 数据资源管理器: 十一月 2021 每月更新
General availability: Copy data to/from Azure Data Explorer using Azure Data Factory or Synapse Analytics
Mapping Data Flows provides scale-out data transformation in the cloud in Azure Data Factory and Azure Synapse Analytics. With these additional connectors, you can build ETL patterns at Spark scale in a code-free design environment without ever touching the Spark compute. Azure Integration Runtimes allow you to define the Spark environment and provide a serverless Spark compute for your data transformation pipelines.
General availability: Azure Data Explorer cache policy hot windows
Azure Data Explorer always supported to cache the latest ingested data for best performance. It now supports selectively caching older data which is ideal when auditing a given time period.
Public preview: Azure Data Explorer is now supported as an output for Azure Stream Analytics job
We are announcing that Azure Stream Analytics can directly output data to Azure Data Explorer, simplifying architecture where you need both hot and warm path analytics on streaming data. This feature will be added to all Stream Analytics regions progressively.
General availability: New Azure Data Explorer output plugin for Telegraf
This new output plugin allows you to write telemetry collected by any of the input plugins of Telegraf directly into Azure Data Explorer e.g. it makes SQL monitoring at scale better and affordable.
Public preview: Near real-time analytics for telemetry, time series, and log data on Azure Synapse
Azure Synapse data explorer is optimized for efficient log analytics, using powerful indexing technology to automatically index structured, semi-structured, and free-text data commonly found in telemetry data.