I've tried to use SSMS, but it requires a temporary location for the BacPac file which is local. But i don't want to download it to local, would like to export a single table directly to Azure Blob storage.
Per my experience, we could import table data from the csv file stored in blob storage. But didn't find a way to export the table data to Blob Storage as csv file directly.
You could think about using Data Factory.
It can achieve that , please reference bellow tutorials:
Copy and transform data in Azure SQL Database by using Azure Data
Factory
Copy and transform data in Azure Blob storage by using Azure Data
Factory
Using Azure SQL database as the Source, and choose the table as dataset:
Source dataset:
Source:
Using Blob storage as Sink, and choose the DelimitedText as the sink format file:
Sink dataset:
Sink:
Run the pipeline and you will get the csv file in Blob Storage.
Also thanks for the tutorial #Hemant Halwai provided for us.
Related
I have a pipeline that creates a dataset from a stored procedure on Azure SQL Server.
I want to then manipulate it in a power query step within the factory, but it fails to load in the power query editor with this error.
It opens up the JSON file (to correct it, I assume) but I can't see anything wrong with it.
If I download the extract from blob and upload it again as a .csv then it works fine.
The only difference I can find is that if I upload a blob direct to storage then the file information for the blob looks like this:
If I just let ADF create the .csv in blob storage the file info looks like this:
So my assumption is that somewhere in the process in ADF that creates the .csv file it's getting something wrong, and the Power Query module can't recognise it as a valid file.
All the other parts of the pipeline (Data Flows, other datasets) recognise it fine, and the 'preview data' brings it up correctly. It's just PQ that won't read it.
Any thooughts? TIA
I reproduced the same. When data is copied from SQL database to BLOB as csv file, Power query is unable to read. Also, Power query doesn't support json file. But when I tried to download the csv file and reupload, it worked.
Below are the steps to overcome this issue.
When I tried to upload the file in Blob and create the dataset for that file in power query, Schema is imported from connection/Store. It forces us to import schema either from connection/store or from sample file. There is no option as none here.
When data is copied from sql db to Azure blob, and dataset which uses the blob storage didn't have schema imported by default.
Once imported the schema, power query activity ran successfully.
Output before importing schema in dataset
After importing schema in dataset
I need to store xml file from blob to azure sql db. Can I put it in single column in my db?
To insert xml file data from blob storage to SQL database, follow below steps
Step1: Upload XML file to blob storage
Step2: Create table in SQL database.
Step3: Create copy activity in Azure Data Factory
Step4: Add Blob storage as a Source
Step5: Add SQL database as a Sink
Step6: Do the mappings
Step7: Now you can run copy activity and get required output as shown in below image.
I am loading 50GB CSV file From Azure Blob to Azure SQL DB using OPENROWSET.
It takes 7 hours to load this file.
Can you please help me with possible ways to reduce this time?
The easiest option IMHO is just use BULK INSERT. Move the csv file into a Blob Store and the import it directly using BULK INSERT from Azure SQL. Make sure Azure Blob storage and Azure SQL are in the same Azure region.
To make it as fast as possible:
split the CSV in more than one file (for example using something like a CSV splitter. This looks nice https://www.erdconcepts.com/dbtoolbox.html. Never tried and just came up with a simple search, but looks good)
run more BULK INSERT in parallel using TABLOCK option. (https://learn.microsoft.com/en-us/sql/t-sql/statements/bulk-insert-transact-sql?view=sql-server-2017#arguments). This, if the target table is empty, will allow multiple concurrent bulk operations in parallel.
make sure you are using an higher SKU for the duration of the operation. Depending on the SLO (Service Level Objective) you're using (S4? P1, vCore?) you will get a different amount of log throughput, up to close 100 MB/Sec. That's the maximum speed you can actually achieve. (https://learn.microsoft.com/en-us/azure/sql-database/sql-database-resource-limits-database-server)
Please try using Azure Data Factory.
First create the destination table on Azure SQL Database, let's call it USDJPY. After that upload the CSV to an Azure Storage Account. Now create your Azure Data Factory instance and choose Copy Data.
Next, choose "Run once now" to copy your CSV files.
Choose "Azure Blob Storage" as your "source data store", specify your Azure Storage which you stored CSV files.
Provide information about Azure Storage account.
Choose your CSV files from your Azure Storage.
Choose "Comma" as your CSV files delimiter and input "Skip line count" number if your CSV file has headers
Choose "Azure SQL Database" as your "destination data store".
Type your Azure SQL Database information.
Select your table from your SQL Database instance.
Verify the data mapping.
Execute data copy from CSV files to SQL Database just confirming next wizards.
From what I understand SQL Managed Instance cannot access local or external file share.
We are trying to load data from a file to SQL Managed Instance, wanted recommendations on the best approach for that.
Use BULK INSERT from Azure blob storage https://techcommunity.microsoft.com/t5/Azure-SQL-Database/Loading-files-from-Azure-Blob-Storage-into-Azure-SQL-Database/ba-p/386133
Upload your files to azure storage
Create external data source to that location (put SAS token if it is not public):
CREATE EXTERNAL DATA SOURCE MyAzureBlobStorage
WITH ( TYPE = BLOB_STORAGE, LOCATION = 'https://myazureblobstorage.blob.core.windows.net'); ```
Load files from this account.
BULK INSERT Product
FROM 'data/product.dat'
WITH ( DATA_SOURCE = 'MyAzureBlobStorageAccount');
I'm creating a structured streaming job that stores its data in a databricks delta database. I'm confronted with the option of storing the checkpoint location and data from the delta database in either ...
1. a normal dbfs location like "/delta/mycheckpointlocation" and "delta/mydatabase"
2. a mounted directory from a data lake like "/mnt/mydatalake/delta/mycheckpointlocation" and "/mnt/mydatalake/delta/mydatabase"
If I understand correctly the data in nr1 will be persisted in a blob storage while the data in nr2 would be stored in the data lake (assuming it's mounted on /mnt/mydatalake)
What considerations are there to decide to store stuff like the checkpoint location and the delta database in either 1 or 2?
The DBFS location is a part of your workspace. So if you drop the workspace you lose it.
The lake is shared so many things can connect to it, including other Databricks workspaces, or other services (like ADF).
There is no right or wrong to this - pure preference.