Thirty seconds for three hundred rows is not normal, and it is not something you should accept. This user's experience, moving from an on-premise SQL Server to Azure SQL and watching a previously snappy data load balloon into a half-minute wait, points to a specific, solvable problem, not a limitation of cloud databases. The bottleneck here almost certainly isn't bandwidth or the size of the data set; 300 rows across 30 columns is trivial. With a 1.5 Gbps symmetrical pipe, the issue lies in how the query is being executed and how the data is being pulled into Excel.
What this means in practical terms is that the default method for connecting Excel to Azure SQL is often the culprit. Many users, especially those accustomed to on-premise environments, rely on Excel's native data import tools, which frequently create a cursor-based, row-by-row retrieval pattern. That approach works fine against a local server where latency is measured in microseconds, but it becomes painfully slow when every round trip to Azure SQL incurs network latency, even if that latency is just 10-20 milliseconds per row. Multiply that by 300 rows, and you've got your 30 seconds. The fix is straightforward: use a bulk fetch approach, such as an ODBC or OLEDB connection with a larger fetch buffer, or pull the data via a stored procedure that returns the entire result set in one go. The cloud isn't slow; the handshake is.
This situation is a reminder that moving to a cloud database requires adjusting your tooling habits, not just your server location. The user's frustration is understandable, they expected the same speed with a better pipe. But the real story is that Azure SQL can deliver those 300 rows in under a second if the connection is tuned for it. The lesson here is to test your data retrieval method, not just your network speed. If you're migrating and encounter this lag, start by checking whether Excel is fetching rows one at a time. Change that, and you'll likely find your load time drops from thirty seconds to a couple of seconds, if not less. The cloud is ready for your data. Make sure your tools are ready for the cloud.