![]() ![]() But if you run a Mac or Linux, then you need a tool that runs on those platforms, and Azure Data Studio does just that. If you live in Windows all day exclusively, then that’s fine. When I started at Microsoft in 2006, that’s the product I worked on building. SQL Server Management Studio is an amazing tool. And if you don’t find what you need, you can make more. In Azure Data Studio, you can connect to multiple data systems, not just SQL Server, like Apache Hadoop HDFS, Apache Spark ™, and others. And those still work just fine – but Azure Data Studio goes further than those tools. You may be thinking wait – don’t we already have a lot of those? Isn’t that the SQL Server Management Studio (SSMS), or “Data Dude” (SQL Server Tools for Visual Studio) or even Visual Studio Code with the add-in for SQL Server? Azure Data Studio is a new tool that you can use to work with SQL Server. This time I’ve learned about a great new tool the team has put together. I’ll still be blogging at my regular location, and from time to time I’ll chime in here on the SQL Server blog. I’ve rejoined the SQL Server team to help with the inclusion of Apache Spark ™, Kubernetes, and the Machine Learning and AI features. After that I spent some time in Microsoft Consulting Services, then over to the Machine Learning team in Microsoft Research, and then the Machine Learning and AI team. I started on the SQL Server team, and then helped ship Microsoft Azure. # use FTP's STOR command to upload the dataįtp.storbinary(f"STOR ).This blog entry comes from Buck Woody, who recently rejoined the SQL Server team from the Machine Learning and AI team.įor those of you who haven’t met me or read any of my books or blog entries, it’s great to meet you! I’ve been a data professional for over 35 years, worked at a variety of places like NASA, various consulting firms, and here at Microsoft since 2006. With ftplib.FTP(FTP_HOST, FTP_USER, FTP_PASS, encoding="utf-8") as ftp: # get FTP connection details from app settings Product_csv = product_list.to_csv(index=False)ĭatatosend = io.BytesIO(product_csv.encode('utf-8')) Product_list = pandas.DataFrame(products) # convert the SQL data to comma separated text ('Python HTTP trigger function processed a request.') To write data to an FTP server, we can use the built-in library ftplib in Python.ĭef main(everyDayAt5AM: func.TimerRequest, products: func.SqlRowList) -> func.HttpResponse: Manually invoke the timer triggers using the built-in HTTP endpoint.Start the function locally by pressing F5 in Visual Studio Code or the Run and Debug icon in the left-hand side Activity bar. ![]() Copy to and update the values for SqlConnectionString, FTP_HOST, FTP_USER, and FTP_PASS with your own values.Clone this repository to your local machine.If you don't have a SQL database, either run SQL Server in a container or create an Azure SQL Database.Complete the Configure your environment steps to setup your local development environment for Azure Functions.Python dependencies for the Azure Function, including azure-functions, pandas, and requests Scenario 2: Take data from the Azure SQL Database and send the data to an API endpointĪzure Function that sends data from SQL to an API endpoint, Scenario 2Īzure Function that sends data from SQL to an FTP server, Scenario 1Īzure Functions app settings file, used for local development - update and copy to.txt files from data currently stored in Azure SQL Database and send the files to an FTP server How do I transfer data from Azure SQL Database in Python every day? ![]()
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