Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load

near real-time data by using the messaging services provided by a cloud provider. Snowflake

Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka

topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously

added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage

Snowflake’s micro-partitioning and columnar storage to optimize data ingestion and query

performance. Snowflake streams and Spark are not ingestion methods, but rather components of the

Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by

tracking data changes in a table. Spark is a distributed computing framework that can be used to

process large-scale data and write it to Snowflake using the Snowflake Spark Connector. Reference:

Snowflake Connector for Kafka

Snowpipe

Snowflake Streams

Snowflake Spark Connector