In Databricks you can export more than 1000 rows of a table by doing the following:
And downloading the full results.
On Azure Synapse, you get no such option to download the full results:
It defaults to exporting only the first 1000 rows. How can we download all rows of a table to a csv from Azure Synapse without writing more code? I checked and downloading to json is also only 1000 rows.
By default the display() in Pyspark shows the first 1000 rows only. In databricks it is possible to download more than 1000 rows with display() by re-executing it.
Unfortunately, synapse notebook does not have that feature now. You can raise a feature request for that here.
How can we download all rows of a table to a csv from Azure Synapse
without writing more code?
When we have a limitation like that, we have to do it by code only.
Save the dataframe to blob storage as csv with the help of Blob linked service and download it.
import pandas
pandas_data= df.toPandas()
pandas_data.to_csv("abfss://<container-name>/filename.csv",storage_options = {'linked_service' : '<linkedsservicename>'})
The above code is referred from this link by FrancisRomstad.
Related
I own an azure data lake gen2 with data partitioned by datetime nested folders.
I want to provide delta lake format to my team but I am not sure if I should create a new storage account an copy the data into delta format or if it would be best practice to transform the current azure data lake into a delta lake format.
Could anyone provide any tips on this matter?
AFAIK, Delta format is supported only as inline dataset and only in Data flows, we can have inline datasets.
So, my suggestion is to use Data flows for this.
As you have the data in date time nested folders, I reproduced with sample dates like below. I have uploaded a sample csv file in each folder 10 and 9.
Create a data flow in ADF and in source select inline dataset to give the wild card path we want. Select your data format, here Delimited text for me. give the linked service as well.
Assuming that your nested folder structure is same for all files, give the wild card path like below as per your path level.
Now, create delta format sink like below.
give the linked service as well.
In the sink settings give the folder for your delta files and Update method.
You can see the delta format files were created in the Folder path after execution.
I'm new to using Data Factory and what I want to do is to copy the information from several CSV files (storage accounts) to a SQL Server database to the respective tables already created. If for example I have 4 CSV files there should be 4 tables.
I have been testing some activities, for example the "Copy Data", but that would cause me to create the same amount of datasets and if for example there were 15 tables, that would be too many datasets.
I want to make it dynamic but I can't figure out how to do it.
How do you suggest me to do this, any example please, thanks.
Either, you can use wildcard to read all files together which are under same blob container.
Once, you read that then in mapping you can add identifier to determine which columns belongs to which table through which you can identify and import data into resp tables OR You can use foreach loop to read all files from blob container and import data into resp table based on file name.
I had created below article to copy data from sql database to sql database for beginners, you can refer it for some initial level setting.
Azure Data Factory (ADF) Overview For Beginners
here is my requirement:
I have an excel with few columns in it and few rows with data
I have uploaded this excel in Azure blob storage
Using ADF I need to read this excel and parse the records in it one by one and perform an action of creating dynamic folders in Azure blob.
This needs to be done for each and every record present in the excel.
Each record in the excel has some information that is going to help me create the folders dynamically.
Could someone help me in choosing the right set of activities or data flow in ADF to do this work?
Thanks in advance!
This is my Excel file as a Source.
I have created folders in Blob storage based on Country column.
I have selected DataFlow activity.
As shown in below screenshot, Go to Optimize tab of Sink configuration.
Now select Partition option as Set Partition.
Partition type as Key.
And Unique value per partition as Country column.
Now run Pipeline.
Expected Output:-
Inside these folders you will get files with corresponding data.
I am trying to copy data from Azure Table Storage to Csv file using "Copy Activity" in Azure Data Factory. But few columns are not loading.
In Azure Table Source Dataset Preview I'm not able to see all columns. Those columns have Null data for first 400 rows. If i have data for all fields in first 11 rows then i am able to see and load all fields data.
But in my case for few fields we have null data for few rows so how to load all columns data?
Couple of points
In preview we always show a few of records and not all .
The table storage is not a schema based storage and the null are treated differently here . I think this \
Querying azure table storage for null values
will help you understand more .
I am pretty confident that when you run the copy activity it will copy all the records to the sink , even if you do see few in the preview .
I meet the same problem "Not Loading all columns from Azure Table Storage Source in Azure Data Factory". I think it may be a bug of Azure Data Factory.
I have a ASP.NET WebApp that manages some Records. The Records are kept in a Azure Table Storage tables.
The client gave me an Excel file with some hundred of Records in Excel table format (Fields in Columns).
How can I export that table from Excel to Azure Table? I saw there is a way to import data from Azure Tables into Office 2016 Excel (via Data>DetData>FromAzure) but I'd like to know if there are ways to do it backward(from Excel to Azure), and perhaps apply a custom logic when exporting that data (like manage DateTime or transform enumerations...).
I would like to import at least the string fields that does not need transformations, then I would do the rest manually or by code...
You have several options:
Using the Azure Storage Explorer you can import / export data to and from table storage using CSV files.
Upload the file to blob storage and use Azure Data Factory to transform and import the data.
Write some code to do this.
An addition about the transform part: you might be able to do this in the source excel file as well. In that case option 1 is probably the easiest.
When it comes to option 1, you can choose to use a .typed.csv file or a regular one. Using the latter it will try to distill the type. So importing a .csv file looking like this:
PartitionKey,RowKey,C1,C2
a,a,1,w
a,b,2,ww
a,c,5,www
will result in a table with 4 columns. (Actually, there will be five, the Timestamp column you'll get for free)