What is streaming analytics?
Streaming analytics is data analytics that delivers real-time or near real-time data via stream processing that can be analyzed quickly as long as the analytical tasks are not too complex.
Streaming analytics is often used in industries that require real-time data access to perform ongoing regular tasks or monitor systems performance. Examples include the monitoring of IoT device data in transportation, manufacturing, and home security; rapid transaction processing in financial services; or patient monitoring in healthcare.
Streaming processing, the backbone of streaming analytics, differs from batch processing in that batch processing can process very large data volumes but only in pre-set time spans. Streaming processing is continuous but processes smaller data volumes at any given time.
Snowflake and Stream Processing
Snowflake can ingest streaming data through the Snowflake Connector for Kafka. In addition, Snowflake Snowpipe can help organizations seamlessly load continuously generated data into Snowflake. It’s an automated service that utilizes a REST API to asynchronously listen for new data as it arrives in an S3 staging environment, and load it into Snowflake as it arrives, whenever it arrives.