无本地工具时，能否仅用Azure实现Blob文件自动解压并导入Azure SQL？

Absolutely! You can automate this entire workflow using Azure's native services—no local Visual Studio, admin rights, or IT support required. Let me walk you through a practical, doable approach tailored to your setup:

We'll string together Azure's serverless tools to create a fully automated pipeline:
Blob Storage (compressed file upload) → Auto-unzip process → Blob Storage (uncompressed data) → Auto-import to Azure SQL

Step 1: Automatically Unzip Uploaded Compressed Files #

You have two low-effort options here, depending on whether you prefer no-code or light code:

• No-Code: Azure Logic Apps
  • Create a new Logic App in the Azure Portal, start with a When a blob is added or modified (properties only) trigger pointing to your incoming compressed file container.
  • Add the built-in Extract archive to folder action: select the source blob (the uploaded zip/7z file), choose a target Blob Storage container (e.g., uncompressed-data) to store the unzipped files. That's it—no code needed.
• Light-Code: Azure Functions
  • Create a Blob-triggered Function directly in the Azure Portal (use the online code editor, no local tools required).
  • For Python, use libraries like zipfile (for .zip) or py7zr (for .7z) to read the blob content, extract files, and write them to a target container. For C#, use System.IO.Compression.ZipArchive.
  • Assign a Managed Identity to the Function, then grant it Storage Blob Data Contributor access to your storage account—no hardcoded keys needed.

Step 2: Automatically Import Uncompressed Data to Azure SQL #

Again, pick the option that fits your comfort level:

• No-Code: Azure Logic Apps
  • Add another Logic App (or extend your existing one) with a trigger for the uncompressed-data container (when new blobs are added).
  • Use the Azure SQL - Bulk insert from blob action (ideal for CSV/structured files) or Insert row(s) for smaller datasets. Configure the connection to your Azure SQL database, select the target table, and map blob columns to SQL table fields.
• Light-Code: Azure Functions
  • Create another Blob-triggered Function that reads the uncompressed file (e.g., CSV) from blob storage.
  • Use a SQL driver like pyodbc (Python) or System.Data.SqlClient (C#) to bulk insert the data into your Azure SQL table. Again, use Managed Identity to grant the Function access to your SQL database.
• Advanced: Azure Data Factory (ADF)
  • If you anticipate future ETL needs (like data cleaning, scheduling), ADF lets you build a full data pipeline. You can set up a Blob source, use a "Copy Data" activity to load directly into Azure SQL, and monitor runs all from the Portal.

• Permissions Made Easy: Use Azure Managed Identity for all services (Logic Apps/Functions/ADF) instead of connection strings. You can grant access to Storage and SQL directly in the Azure Portal without IT help.
• Error Handling: Add steps to your Logic App/Function to catch failures—for example, move failed files to a failed-files container and send yourself an email alert.
• Cost Efficiency: All these tools are serverless, so you only pay for what you use. For daily small-to-medium file volumes, you'll likely stay within Azure's free tier or pay just a few dollars a month.

内容的提问来源于stack exchange，提问作者David Jerome