![]() ![]() For Redshift, ability to CLEAR PATH to overwrite existing partitions in the lake.use a defined set of columns to create partitions in the lake) Control over the partitioning logic (e.g.Specifying the output format (csv, json, parquet) and compression where applicable.Specifying a location to output partitioned results generated by a model's sql.The idea is to create a new materialization (potentially "external" would work) option which would handle persisting query results and enable a few capabilties: Note: query tags are set at the session level.When using Redshift Spectrum, dbt is able to stage external tables stored in S3 and do useful things such as declaring the source data format (csv, json, parquet, etc.). At the start of each model materialization, if the model has a custom query_tag configured, dbt will run alter session set query_tag to set the new value. As such, build failures midway through a materialization may result in subsequent queries running with an incorrect tag.Īt the end of the materialization, dbt will run another alter statement to reset the tag to its default value. The incremental_strategy config controls how dbt builds incremental models. By default, dbt will use a merge statement on Snowflake to refresh incremental tables. Snowflake's merge statement fails with a "nondeterministic merge" error if the unique_key specified in your model config is not actually unique. If you encounter this error, you can instruct dbt to use a two-step incremental approach by setting the incremental_strategy config for your model to delete+insert. Configuring table clustering ĭbt supports table clustering on Snowflake. It will add the specified clustering keys to the target tableīy using the specified cluster_by fields to order the table, dbt minimizes the amount of work required by Snowflake's automatic clustering functionality.It will implicitly order the table results by the specified cluster_by fields.When this configuration is applied, dbt will do two things: To control clustering for a table or incremental model, use the cluster_by config. ![]() If an incremental model is configured to use table clustering, then dbt will also order the staged dataset before merging it into the destination table. As such, the dbt-managed table should always be in a mostly clustered state. The cluster_by config accepts either a string, or a list of strings to use as clustering keys. The following example will create a sessions table that is clustered by the session_start column.When using Redshift Spectrum, dbt is able to stage external tables stored in S3 and do useful things such as declaring the source data format (csv, json, parquet, etc.). Specifying a location to output partitioned results generated by a model's sql.Specifying the output format (csv, json, parquet) and compression where applicable.use a defined set of columns to create partitions in the lake).For Redshift, ability to CLEAR PATH to overwrite existing partitions in the lake.The implementation of create_external_table here accomplishes this when triggered by a run-operation. The goal here is to make that logic a materialization so that it can become part of the dbt run pipeline. Additional contextīelieve this is relevant for any of the databases currently supported in the external tables package:ĭbt Users who have existing infrastructure that leverages a more data lake centric approach for managing persistence will benefit from this. They can use dbt and the warehouse as an ephemeral compute / transform layer, and then persist the data to a file store, which enables other tools (e.g. Are you interested in contributing this feature? AWS Glue / Athena) to query the results using existing analytical patterns. Thanks for opening my view, there's a crucial distinction here between "read-only" and "write-read" external tables-sources and sinks, if you will. I believe Redshift/Spectrum is unique in its support of create external table.
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