From f46c6a10ac4a84e76f4ea9f9716fb95b04b633d6 Mon Sep 17 00:00:00 2001 From: Alena Date: Fri, 21 Jun 2024 12:36:31 +0200 Subject: [PATCH] fix snippet type --- .../docs/dlt-ecosystem/verified-sources/sql_database.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database.md index f8c7ac0dc0..1721b0d44b 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database.md @@ -402,12 +402,12 @@ You can also override the sql type by passing a `type_adapter_callback` function This function takes an `sqlalchemy` data type and returns a new type (or `None` to force the column to be skipped/inferred depending on backend). This is useful for example when: -a) You're loading a data type which is not supported by the destination (e.g. you need JSON type columns to be coerced to string) -b) You're using an sqlalchemy dialect which uses custom types that don't inherit from standard sqlalchemy types. +- You're loading a data type which is not supported by the destination (e.g. you need JSON type columns to be coerced to string) +- You're using an sqlalchemy dialect which uses custom types that don't inherit from standard sqlalchemy types. Example, when loading timestamps from Snowflake you can make sure they translate to `timestamp` columns in the result schema: -```python +```py import dlt from snowflake.sqlalchemy import TIMESTAMP_NTZ import sqlalchemy as sa