Snowflake’s Snowpipe serverless data loading tool enables enterprises to load huge volumes of data into Snowflake in a timely, cost-effective, and infrastructure-free manner. It’s compatible with a wide variety of data stores, such as Amazon S3 and Redshift, and with popular RDBMSs like MySQL and Postgres RDS. This blog post offers best practices for optimizing the performance of Snowpipe data loads using Snowflake Query Accelerator (SPA), which you can read more about here.
What Is Snowpipe? For continuous data loading into cloud-based tables, Snowflake offers a serverless data ingestion utility known as Snowpipe. Snowpipe is optimized and scalable, but sometimes it may experience performance issues if not properly configured. We recommend using Snowpipe when you have high throughput workloads, as well as large loads of data or any other scenario where you’re looking for high performance.
FTP and SFTP are not intended for high-volume data transfers. They can be slow, unreliable, and hard to manage. File Transfer Protocol and Secure File Transfer Protocol are both susceptible to attacks that could compromise data integrity or deletion. Some suggestions for decreasing the data flow through Snowpipe: Make sure that the column names in your CSV files match those in your destination table (s). Combine several datasets into a single file for each table. Based on the size of your dataset, select the appropriate amount of rows per transaction. Create multiple files when needed. Snowpipe will consume memory on your host system, so make sure you have enough RAM. Make sure there is enough room on the hard drive you intend to save your Snowpipe dump file on.
Snowpipe performance is affected by a variety of factors, including CPU speed, operating system, and network quality, among others. Even if they are all obtained from identical PCs running identical FTP/SFTP clients, these components might cause significant variances in transfer speeds. This can be due to many factors, including network interruptions between your system and that of CloudPressor or latency built up from having several systems sending files at once, or other unforeseen issues with either your own or our equipment, which we would need to address with specific upgrades for that situation if necessary.
Tuning indexes is an effective strategy for reducing data load. Indexes are used by the Snowpipe loader when loading data, and can have a significant impact on performance. For example, if an index is needlessly filtering out data, this would result in longer loading times since additional queries must be conducted throughout the load process. Snowflake tables provide the load and add methods for importing data. Each time you use load, a new row is added to the table, and each time you use append, you append to the existing table.