Did Snowflake Dynamic Tables Kill dbt? | Modern Data Pipelines

[@premradhakrishnanssw8724]

Thanks Adam. Great overview. Keep em coming please.. and while I am asking things for Xmas.. Could you do a comparison on dbt vs Coalesce as well please?

[@nashdashflash]

Coalesce is the name of dbt's annual conference.

[@danscott6434]

@@nashdashflash Coalesce is the name of a much better transformations tool DBT. It's also the name of their confernce, but I bet they change that soon @Adam, this would be a good topic.

[@roberbonox]

Hi, i create a dynamic table and have task that validates data on tables (standard tables) that are used in the dynamic table. The thing is when i finish the validations i need, i put an ALTER DYNAMIC TABLE IF EXISTS xxx REFRESH; inside the task, and when the task is executed the refresh is not starting. If i execute manually the command it works but is like it doesn't when is inside the task, is this a limitation right now??

[@naveenyalamala]

What should i learn AZF or DBT ?

[@davdumas]

How does dbt address data summarization? As you know you can summarize data in dyn tables for increased performance

[@sdhilip]

Hi Adam, very nice. Could you please explain what is the primary difference between Dynamic Tables vs Views vs Material Views?

[@mastering_snowflake]

Thanks for the question: Views: Snowflake provides what would be considered a traditional database view. In general, a view allows the result of a query to be accessed as if it were a table, including linking (or in database parlance, joining) two or more tables or other views into a single logical view. Once defined, views can be queried like any other table. Materialized views: Materialized views differ from traditional views by providing the ability to pre-compute the dataset based on materialized view query. Because the result is pre-computed, querying a materialized view is faster than executing a query against the base table of the view. This performance difference can be significant when a query is run frequently or is sufficiently complex. As a result, materialized views can speed up expensive aggregation, projection, and selection operations, especially those that run frequently and that run on large data sets. Dynamic Tables: Dynamic tables materialize the results of a specified query. Instead of creating a separate target table and writing code to transform and update the data in that table, you can define the target table as a dynamic table, and you can specify the SQL statement that performs the transformation. Background automation then keeps the dynamic table up to date based on the refresh criteria that you specify. Reference: docs.snowflake.com/en/user-guide/overview-view-mview-dts

[@vrmotovlog4333]

Very good topic