I'm seeing a lot of buzz about Dynamic Tables in Snowflake recently. We currently use a combination of Streams and Tasks to manage our incremental data processing and CDC. Is it worth refactoring our pipelines to use Dynamic Tables instead? What are the trade-offs regarding latency, complexity, and credit consumption when choosing between these two approaches?
3 answers
Dynamic Tables move Snowflake toward a declarative data modeling approach, similar to dbt. Instead of writing complex logic to track offsets in Streams and scheduling Tasks, you just define the target state with a SELECT statement and set a "Target Lag." Snowflake handles the refresh logic automatically. The trade-off is control; Streams and Tasks allow for complex conditional logic and lower latency for specific rows. Dynamic Tables are simpler to manage but can be slightly more expensive if the refresh interval is set very aggressively
If we have a pipeline that requires multiple joins across several large tables, do Dynamic Tables perform efficiently, or do they struggle with complex dependency graphs?
We switched to Dynamic Tables for our silver-to-gold layer transformations and it reduced our codebase by 40%. The "Target Lag" feature makes it very easy to manage SLAs.
That's a huge reduction in maintenance! I think for most teams, the ease of use with Dynamic Tables far outweighs the minor increase in credit spend.
William, they actually handle dependencies quite well. Snowflake builds a directed acyclic graph (DAG) to manage the refresh order. However, for extremely complex joins, you might see higher credit usage compared to a finely-tuned manual Stream/Task setup. It’s a classic trade-off between engineering time and compute cost. I suggest testing a small subset of your data first to see the performance.