The name of the store that hosts the entity for this stream.
Required: No
Default value: User’s default store.
Type: String
Valid values: See list-stores
The name of the Snowflake database that would host the Snowflake #table.
Required: Yes
Default values: None
Type: String
Valid values: Database names available from list-entities.
The name of the Snowflake schema that would host the Snowflake #table.
Required: Yes
Default value: None
Type: String
Valid values: Schema names available from list-entities under the snowflake.db.name database name.
The name of the Snowflake table to use when creating the #table. If the table doesn't exist in the store, a table with the table\_name is created in the corresponding store.
Required: No
Default value: table\_name
Type: String
The amount of time to buffer events with the sink #table before writing the data into the corresponding Snowflake table.
It is important to note that the longer events are buffered, the larger the internal state of the query gets. Depending on the volume and size of the events for the corresponding query, memory limitations may be reached.
Required: No
Default value: 1000
Type: Long
Valid values: (0, ...]
The name of the Databricks catalog that would host the Databricks #table.
Required: Yes
Default values: None
Type: String
Valid values: Database names available from list-entities.
The name of the Databricks schema that would host the Databricks #table.
Required: Yes
Default value: None
Type: String
Valid values: Schema names available from list-entities under the databricks.catalog.name catalog name.
The name of the Databricks table to use when creating the #table. A table with the table\_name is created in the corresponding store. If that table already exists, then an error is reported back to the user.
Required: No
Default value: table\_name
Type: String
The S3 directory location for the Delta-formatted data to be written. This location is either a full S3 path, or a relative path to the data store's Cloud Provider Bucket (see create-store). The credentials for writing to S3 are given during data store creation (see create-store). Note that the S3 bucket from the location specified by this parameter must match the databricks.cloud.s3.bucket property defined in the data store.
Required: Yes
Default value: None
Type: String
Required: Yes
See CDC source parameters for the same parameter name
Required: Yes
See CDC source parameters for the same parameter name
Required: No
See CDC source parameters for the same parameter name
The name of the database (namespace) in the AWS Glue instance (used as the Iceberg Catalog implementation in the Iceberg Store) containing the existing Iceberg table. Required: Yes
Type: String
| | `iceberg.aws.glue.table.name` |The name of the existing Iceberg table in the AWS Glue instance (used as the Iceberg Catalog implementation in the Iceberg Store).
Required: Yes
Type: String
| ## Examples — Snowflake #### Create a copy of a stream in a Snowflake table The following creates a replica of the source [#stream](https://docs.deltastream.io/overview/core-concepts/databases#stream "mention"), `pageviews` in the Snowflake [#table](https://docs.deltastream.io/overview/core-concepts/databases#table "mention"), `PV_TABLE`: ```sql CREATE TABLE "PV_TABLE" WITH ( 'store' = 'sfstore', 'snowflake.db.name' = 'DELTA_STREAMING', 'snowflake.schema.name' = 'PUBLIC' ) AS SELECT * FROM pageviews; ``` #### Create a stream of changes for a changelog in a Snowflake table The following CTAS query creates a new Snowflake [#table](https://docs.deltastream.io/overview/core-concepts/databases#table "mention") to store incremental changes resulting from a grouping aggregation on the `transactions` [#stream](https://docs.deltastream.io/overview/core-concepts/databases#stream "mention"): ```sql CREATE TABLE "CC_TYPE_USAGE" WITH ( 'store' = 'sfstore', 'snowflake.db.name' = 'DELTA_STREAMING', 'snowflake.schema.name' = 'PUBLIC' ) AS SELECT cc_type AS "CREDIT_TYPE", tx_time AS "TRANSACTION_TS", tx_id AS "TRANSACTION_ID" FROM transactions GROUP BY cc_type; ``` This query stores insert and update changes to the grouping column `cc_type` to the sink table `CC_TYPE_USAGE`. ## Examples — PostgreSQL Assumptions for the following 2 examples: * `pageviews` is a stream you have already defined. * `ps_store` is a PostgreSQL data store you have already created. * For details and an example, see [CREATE STORE](https://docs.deltastream.io/reference/sql-syntax/ddl/create-store#create-a-postgresql-store). #### Create a new table in a PostgresSQL data store — example 1 In this example, you create a new table, named `pageviews`, in the given PostgreSQL data store under the public schema. The table has 3 columns: 1. viewtime 2. uid 3. pageid. The query writes its results into this table as its sink. ```sql CREATE TABLE pg_table WITH ( 'store'='ps_store', 'postgresql.db.name'='demo', 'postgresql.schema.name'='public', 'postgresql.table.name'='pageviews') AS SELECT viewtime, userid AS uid, pageid FROM pageviews; ``` #### Create a new table in a PostgresSQL data store — example 2 This query creates a new table, named `pagevisits`, in the given PostgreSQL data store under the public schema. The table has 2 columns: 1. `userid` 2. `cnt` The query writes its results into this table. ```sql CREATE TABLE pagevisits WITH ( 'store'='ps_store', 'postgresql.db.name'='demo', 'postgresql.schema.name'='public') AS SELECT userid, count(*) AS cnt FROM pageviews GROUP BY userid; ``` {% hint style="info" %} **Note** Since the query includes a `GROUP BY` clause, the `userid` column — which is the grouping column — is considered the primary key for the results. The `pagevisits` table in PostgreSQL is created accordingly, with `userid` as its primary column. {% endhint %} ## Examples — Databricks #### Create a copy of a stream in a Databricks table The following creates a replica of the source [#stream](https://docs.deltastream.io/overview/core-concepts/databases#stream "mention"), `pageviews` in the Databricks [#table](https://docs.deltastream.io/overview/core-concepts/databases#table "mention"), `pageviews_db`: ```sql CREATE TABLE pageviews_db WITH ( 'store' = 'databricks_store', 'databricks.catalog.name' = 'catalog1', 'databricks.schema.name' = 'schema1', 'databricks.table.name' = 'pageviews', 'table.data.file.location' = 's3://mybucket/test/0/pageviews' ) AS SELECT * FROM pageviews; ``` Upon issuing this query, a Databricks table is created in `catalog1.schema1.pageviews` that uses `s3://mybucket/test/0/pageviews` as its external location. This query writes the Delta-formatted parquet files and updates the Delta log in that S3 location.