支持ALTER TABLE ADD COLUMN,以便能够使用允许 NULL 值的新列扩展现有表。 查询见解更新 发货(2024 年第 3 季度) 将通过 Query Insights 提供已关闭会话的历史视图。 此外,它还有助于分析 DW 的流量、负载和使用情况。 仓库编辑器中的就地还原 发货(2024 年第 2 季度) 现在,可以使用仓库编辑器体验轻松创建还原...
type 数据集的 type 属性必须设置为 LakehouseTable。 是 表 表的名称。 是 示例: JSON 复制 { "name": "LakehouseTableDataset", "properties": { "type": "LakehouseTable", "linkedServiceName": { "referenceName": "<Microsoft Fabric Lakehouse linked service name>", "type":...
BUG::Lakehouse table view UI 02-15-2024 12:38 AM Issue: can't resize lakehouse columns to view column names in full.STEPS:0__create a lakehouse. 1__ingest some data into the lakehouse; make sure you use long column names.
destination_lakehouse str Required; The lakehouse where the shortcut will be created. destination_workspace str Optional; The workspace in which the shortcut will be created. Defaults to the 'sourceWorkspaceName' parameter value. shortcut_name str Optional; The name of the shortcut 'table' to ...
When a Direct Lake semantic model is deployed, it doesn't automatically bind to items in the target stage. For example, if a LakeHouse is a source for a DirectLake semantic model and they're both deployed to the next stage, the DirectLake semantic model in the target stage will still be...
Column mapping when your destination is Lakehouse Table with the Copy assistant. Let’s take a look at another example when copying data to Data Warehouse/SQL data stores. Here, we have anid column in our source that is an int type and we can cha...
Column mapping when your destination is Lakehouse Table with the Copy assistant. Let’s take a look at another example when copying data to Data Warehouse/SQL data stores. Here, we have anid column in our source that is an int type and we can c...
Incremental refresh is a feature that allows you to load only new or updated data into your data destination, such as a lakehouse. This can improve the performance and efficiency of your data pipelines. you can use a watermark column to slice the new or updated records for every run, and ...
[]}) for t in tableList: tName = t['name'] tType = t['type'] tLocation = t['location'] tFormat = t['format'] new_data = {'Workspace Name': wName, 'Lakehouse Name': lakehouseName, 'Table Name': tName, 'Type': tType, 'Location': tLocation, 'Format': tFormat} df = ...
不管目标是Lakehouse Table、Data Warehouse还是SQL data stores,都可以方便地调整。比如,可以将源中PersonID列的int类型改成string类型。这样,数据复制更灵活,数据处理更简单。想了解更多?点击链接,探索更多精彩内容!Edit the Destination Table Column Type when Copying Data to Lakehouse Ta...