I tried to use this article in successfully copying data from one table to another using Dataflows in Data factory. Now my scenario is to handle multiple tables in the DB. the above example is for one of the table.
I tried to follow the next article (link) in same series and have created View and For each loop but now wondering how should I put the input in Data Flow activity.
Any ideas or if any one tried the same thing.
Thanks
You will need to use a parameterized dataset that uses a dataset parameter for the name of the table. Then, pass a string parameter from the Foreach activity that contains the table name into the dataset parameter for that data flow activity. This will all be accomplished from the pipeline.
Related
I am setting up a pipeline in data factory where the first part of the pipeline needs some pre-processing cleaning. I currently have a script set up to query these rows that need to be deleted, and export these results into a csv.
What I am looking for is essentially the opposite of an upsert copy activity. I would like the procedure to delete the rows in my table based on a matching row.
Apologies in advanced if this is an easy solution, I am fairly new to data factory and just need help looking in the right direction.
Assuming the source from which you are initially getting the rows is different from the sink
There are multiple ways to achieve it.
in case if the number of rows is less, we can leverage script activity or lookup activity to delete the records from the destination table
in case of larger dataset, limitations of lookup activity, you can copy the data into a staging table with in destination and leverage a script activity to delete the matching rows
in case if your org supports usage of dataflows, you can use that to achieve it
I am using azure data factory to have a soap API connection data to be transferred to snowflake. I understand that snowflake has to have the data in variant column or csv or we need to have intermediate storage in azure to finally land the data in snowflake. the problem I faced is the data from api is a string within that there is xml data. so when i put the data in blob storage, its a string. how do I avoid this and have the proper columns while putting the data ?
over here, the column is read as string. is there a way to parse it into their respective rows ? I tried to put the collection reference, it still does not recognize individual columns. Any input is highly appreciated.
You need to change to Advanced editor in Mapping section of copy activity. I took the sample data and repro'd this. Below are the steps.
Img:1 Source dataset preview
In mapping section of copy activity,
Click Import Schema
Switch to Advanced editor .
Give the collection reference value.
Img:2 Mapping settings
I’m using a copy activity in azure synapse pipeline to copy and filter data from
containerA/file1.csv to containerB/file2US.csv
Similarly I’m using another copy activity to copy and filter data from containerA/file1.csv to containerB/file2IND.csv
The same process for different regions. In every activity I add a where clause to filter the data and copy it into region specific files.
It feels pretty redundant to do this way. Is there any way where I can conditionally check each row and copy it to a different sink based on the region value?
What I’m trying to achieve is a SINGLE ACTIVITY that can select the correct sink based on a condition each row maps to.
The activity you are looking for is called Data Flows. You will use the Conditional Split transformation with as many sinks as you require to achieve this use case.
I would approach this using a For Each activity which runs in parallel and a parameterised Copy activity. You can use an array parameter to list the regions you want to loop through. Here's an example with continents:
["Africa","Antarctica","Asia","Australia","Europe","North America","South America"]
Set up your pipeline like this:
Use the Query in the Sink and and parameterise it with the Add dynamic content button:
Alternately use a Stored Proc. Parameterise the Sink using a dataset parameter. This will give you control of the output filename and location.
I have a requirement where I need to move data from multiple tables in Oracle to ADLS.
The size of data is around 5TB. These files in ADLS, I might use it in future to connect Power BI.
Is their any easy and efficient way to do this.
Thanks in Advance !
You can do this by using Lookup activity and ForEach in Azure Data Factory.
Create a table or file to store the list of table names which needs to be extracted.
Use Lookup Activity get the tables list.
Pass the list to ForEach activity and by looping each table copy the current item() from oracle to ADLS.
In ForEach, settings->Items, add the following code in the Add Dynamic Content.
#activity('Get-Tables').output.value
Add a Copy activity inside ForEach activity.
In Copy data activity, source > Query and Input the following code:
SELECT * FROM #{item().Table_Name}
Now add the sink dataset(ADLS) and Execute your pipeline.
Please refer Microsoft Documentation to know about the creation of linked services for Oracle.
Please go through this article by Sean Forgatch in MODERN DATA ENGINEERING if you face any issues in the process.
I have a requirement where in I have a source file containing the Table Name(s) in Mapping Data Flow. Based on the Table Name in the file - there needs to be a dynamic query where column metadata, along with some other properties is retrieved from the data dictionary tables and inserted into a different sink table. The table name from the file would be used as a where condition filter.
Since there can be multiple tables listed in the input file (lets assume its a csv with only one column containing the table names), if we decide to use a cache sink for the file :
Is it possible to use the results of that cached sink in the Source transformation query in the same mapping data flow - as a lookup (from where the column metadata is being retrieved) and if Yes, how
What would be the best way to restrict data from the metadata table query based on this table name
Though of alternatively achieving this with a pipeline using For Each passing the table name as parameter to data flow, but in this case if there are 100 tables in the file, there would be 100 iterations and 100 times the cluster would need to be spun up. Please advise if this is wronf or there are better ways to achieve this
You would need to use option 3. Loop through the table names and pass each in as a parameter to the data flow to set the table name in the dataset.
ADF handles the cluster creation and teardown. All you have to worry about is whether you want to execute each sequentially or in parallel and how many. There are concurrency limits in ADF, so you should consider a batch count of 20 if you run in parallel.