Azure Data Factory functions Azure Data Factory consists of several functions that combine to provide your data engineers with a complete data-analytics platform. Connect and collect The first part of the process is to collect the required data from the appropriate data sources. These sources can ...
本文介绍如何通过使用 Azure 数据工厂和 Synapse Pipelines 门户中的“执行 SSIS 包”活动在 Azure 数据工厂管道中运行 SQL Server Integration Services (SSIS) 包。 先决条件 如果还没有 Azure-SSIS Integration Runtime (IR),请按照以下文章中的分步说明创建 IR:教程:预配 Azure-SSIS IR。 使用“执行 SSIS 包”...
Internal Server ErrorSomething went wrongGo to community home
Internal Server ErrorSomething went wrong
Azure Databricks Microsoft Purview Azure Data Factory Azure Machine Learning Microsoft Fabric HDInsight Azure Data Explorer Azure Data Lake Storage Azure Operator Insights Solutions Featured View all solutions (40+) Azure AI Migrate to innovate in the era of AI Build and modernize...
Power Platform dataflows Data Factory wrangling dataflows What do they have in common? What's the difference? Which user persona is suited to which type of dataflow?Microsoft Power Platform dataflows and Azure Data Factory dataflows are often considered to be doing the same thing: extract...
Navigate to the Copy activity's Sink tab and select the destination dataset. In this example, the destination is Azure Data Lake Storage (Gen2), as a Parquet file.Run the pipelineRun the pipeline to move the data from the Fabric Lakehouse table into the Parquet file in ADLS Gen2....
Based on the selected metric, which is the Failed Activity Runs in the selected Azure Data Factory, you have the option to choose the aggregation method that will be used to display the selected metric, such as Count, Max, Min, Avg or Sum, where the aggregated metric will be drawn direct...
Azure SDK 2.5 - Azure SDK 2.5 for .NET and Visual Studio 2015 Overview C# - How C# 6.0 Simplifies, Clarifies and Condenses Your Code Visual Studio 2013 - Expand Visual Studio 2013 with Extensions Code Downloads Last Word - Connect(); the Past to the Future ...
When developing complex and multi-stage Azure Data Factory pipelines, it becomes harder to test the functionality and the performance of the pipeline as one block. Instead, it is highly recommended to test such pipelines when you develop each stage, so that you can make sure that this stage ...