![]() Loopcheckvariable Activity – it will be defined as zero first and reset of this variable to 1 will happen within ‘until activity’ after the first iteration to avoid the further lookup available in ‘ if condition Activity’.Lookup check – Lookup check variable used to restrict the lookup activity will happen once and set the count & dividend_count, or else it will set the dividend_count variable again to the old value instead of further iteration.Ĭreate a pipeline like below and do all the activities:. ![]() ![]() RAW SQL QueryĪctual query at the time of the second iteration after resetting initialize and dividend_count variable Use lookup activities to trigger the below SQL query and save the count & dividend_count from the table into Azure Data Factory variable. We can achieve the partition of different files using these two methods.įile partition using Azure Data Factory pipeline parameters, variables, and lookup activities will enable the way to extract the data into different sets by triggering the dynamic SQL query in the source.īelow is the SQL query and methods to extract data into the different partitions. We can achieve this by writing custom logic or Azure dataflow. In such cases, we may need to partition the data into different sets to achieve performance and leverage the loading of data into targets limiting performance hits.īy default, we do not have the option to partition the data into different sets of files in ADF. In many cases, we have a scenario of extracting a huge volume of data from the source. In this blog, we are going to explore file partition using Azure Data Factory. By Default, Azure Data Factory supports the extraction of data from different sources and different targets like SQL Server, Azure Data warehouse, etc.
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