 Hey guys, welcome to SSUN attacks. So, we'll decide and today we are going to see about the validation schema in the data flow. So, validation schema is basically going to use when your source and your sync are not going to same. Then we don't want to execute our pipeline. So, on that scenario, this will help us. So, as per the Microsoft, it is used to validate the source schema if there is any changes in the source schema. But then it would fail the pipeline as I told you. So, go to the Azure data factory and we'll try to implement this in practice. So, here we are having this file which is the employee data name file and it has total four columns here and this is under the input folder. So, what is our requirement? We just want to load the data and this file from the input folder to output folder of this blob storage. So, go to on the Azure data factory and here let me try to add a new data flow here. So, in the new data flow, we can see add source. So, we can click on that. For the source, we have already created the data set. So, let me try to use that one and go to the open. So, we can validate that. So, this is under the input folder of the employee data new.csu file. So, this is the same. Now, let me try to add quickly sync here. So, this is the sync we have added. So, go to the source side and here we can see the options. So, under the options, we can see the allow schema drift. Then we can see the infer drifting column type. Then we can see the validate schema. So, for first these two will be going to cover in the next video. In this video, I am very concerned to discuss about the validate schema. So, let me select this checkbox here. So, what it will be going to do? It will be going to check the source schema and the sync schema both. And if data is not going to match, if any new column is added or any existing column has dropped, then your pipeline will be fed. Like this data flow will not be executed successfully. So, here we can select that one. So, it will be going to check the schema first. Now, we can go under the projection and in this the projection tab, we can see few options like the define default format, then detect data type. So, let me try to use the detect data type because the detect data type will be going to by default it will be going to have the string columns here. Like all the columns will be going to have the string data type. But in case of the detect data type, it will be going to detect what is the actual data type of that column. So, it will be going to detect like that. Now, we can go here like reset schema. We can reset the schema. Let me click on that. So, by default it was a string. So, it has a string. Let me detect data type again. So, it will be having string values. So, as we can see it got changed. Now, we can go directly in the data preview and we will try to see the data. So, it should have all the data here with all the columns. So, that we can see that. Now, let me go in the sync side and under the sync, let me try to create a new data set and here output should be on the Azure Blob Stories. Let me click on continue, delimited text continue. Here, this is the data set for the sync of the employee. Now, let me go in the link service and here SSU testing. First row as header, we can select this checkbox. Let me go here. Let me select this output folder and click on OK. Here the file that should be employee.csv file. Now, everything is OK. So, we can click on OK. So, here it got failed because it is not able to import the schema. So, let me try to import schema option as none and then try to click on OK. Now, let me go in the data preview and try to see the data here. So, it should have all the columns with all the data. Now, let me try to publish this. So, this will be going to publish. So, we just want to execute this by using the pipeline. So, we can directly go here try to create a new pipeline and for executing of any data flow, we are required to use the data flow activity. We can drag and drop here and after that go to the settings and in the settings, we can see the data flow. So, we can select the data flow here. Now, if you are going to execute this, so your file will not be in the output folder now. So, that should be there. So, as of now we can see only this one file which is the filter one. This is in progress. So, we can wait. So, it got executed. Let me go back here and try to refresh this folder. So, this would have the file. So, as we can see this is the file. So, let me try to open this and here let me go in the edit. Under that we can see all the data with all the columns. Now, let me try to go in the source again and now we are required to do some changes here. So, for example, let me go in edit and here let me try to remove the department column from here. So, it will not have the department column now. So, it should be going to fail the execution of pipeline because your schema is not going to match and we have selected the option for the validate schema. Let me save this. So, it has only three columns at the source. Now, let me try to debug it. So, this time this should be fail just because of validate schema option. So, we can wait. So, here as we can see this got failed. Let me check the error message here. So, error message is saying job failed due to the reason the column in the source configuration cannot be found in the source dataset schema. So, this is the same error. It is returning like your source schema is not going to match. So, this is all about the validate schema. So, thank you so much for watching this video. If you have still any doubt, then you can comment your questions in the comment box. See you in the next video.