Real-Time Data Processing in Snowflake

Snowflake provides advanced features for data streaming and processing. You don't need to build micro batch logic manually. Snowpipe ingests data automatically and dynamic tables can be used for materialization.

The building blocks

Snowpipe

Snowpipe is continuous file loading. When a new file appears in cloud storage such as Amazon S3, Snowpipe picks it up and loads it within minutes. No need for manua batch cron jobs etc.

Snowpipe Streaming

Snowpipe Streaming skips the files altogether. Applications, IoT gateways, or a Kafka connector write rows directly into Snowflake tables, and the data is queryable within seconds. It is the right choice when the source produces a constant stream of events rather than periodic extracts, and it is priced by volume rather than by compute time. No need to create files in the middle. Use SDK libraries to send messages.

Dynamic Tables

Landing raw events is only half the work. The other half is keeping cleaned, joined, and aggregated tables up to date. Dynamic Tables let you define a target table with a single query and a target freshness, for example "no more than one minute behind". Snowflake then refreshes it incrementally and figures out the dependency order between tables itself. This replaces a large amount of hand-written pipeline orchestration.

Streams and Tasks

When you need full control over the logic, Streams record which rows changed in a table and Tasks run SQL or Python on a schedule or whenever a stream has new data. This is the way if you want to build more traditional ETL jobs without building heavy business logic manually.

Openflow

Openflow is Snowflake's managed data integration service, built on Apache NiFi. You can stream data using data source data capture into snowflake without any custom logic or state management.

What this looks like for a business

• One platform instead of two. Streaming data and historical data live in the same tables, so the same dashboard, model, or application sees both without reconciliation.
• Freshness as a setting, not a project. Moving a table from "updated nightly" to "updated every minute" is a configuration change in a Dynamic Table, not a redesign.
• Pay for what moves. Streaming ingestion is billed on throughput and Dynamic Tables only consume compute when there is something to refresh, so idle periods cost very little.
• Governance carries over. Access policies, masking, and lineage apply to streamed data from the moment it lands.
• Strong SLA. Snowflake takes care of implementation and refresh scheduling. This way there is no or little downtime in the process.