Are there any limitations where Snowpipe might not be the ideal choice for data loading?

Yes, there are some limitations or scenarios where Snowpipe might not be the ideal choice for data loading. These include:

• Latency: Snowpipe can have some latency, as the data is first loaded into a staging area before being loaded into the final table. This latency can be a few minutes, or even longer for large datasets.
• Throughput limits: Snowpipe has throughput limits, which means that the amount of data that can be loaded per second is limited. This can be a problem for very high-volume data loads.
• Cost: Snowpipe can be more expensive than other data loading methods, such as COPY INTO. This is because Snowpipe uses a serverless compute model, which means that you are charged for the resources that you use.
• Data transformation: Snowpipe is not designed for data transformation. If you need to perform any data transformation, such as filtering or joining data, you will need to do this after the data has been loaded into Snowflake.
• Support for specific data formats: Snowpipe does not support all data formats. If you are using a data format that is not supported by Snowpipe, you will need to convert the data to a supported format before loading it.

Overall, Snowpipe is a powerful and versatile data loading tool. However, it is important to be aware of its limitations before choosing it for your data loading needs.

Here are some additional scenarios where Snowpipe might not be the ideal choice:

• When you need to load data in real time.
• When you need to load very large datasets.
• When you need to perform complex data transformation.
• When you are on a tight budget.

If you are not sure whether Snowpipe is the right choice for your data loading needs, you should consult with a Snowflake expert.