Forum Discussion
How to Convert String time column to timestamp or DATE DATATYPE using
Hi,
I am peforming the data load from lake house table to Silver table where one of the date column in the existing table.
As Silver tables not support to load string type date column values to DATE/TIMESTAMP in silver layer table, is there any conversion function to do this convertion? If you can provide one example by taking one date (string type column) to convert into DATE/TIMESTAMP will appriciate.
Thanks,
Sri.
If you're working in Fabric using Spark or a notebook, you can convert a string column to either a DATE or TIMESTAMP using the built-in conversion functions.
For example, if your source column contains values such as '2026-07-02':
SQL
SELECT
TO_DATE(date_string_col, 'yyyy-MM-dd') AS converted_date
FROM source_table;
Show more lines
If the source value includes time, such as '2026-07-02 14:30:00', use:
SQL
SELECT
TO_TIMESTAMP(date_string_col, 'yyyy-MM-dd HH:mm:ss') AS converted_timestamp
FROM source_table;
Show more lines
In PySpark:
Python
from pyspark.sql.functions import to_date, to_timestamp
df = df.withColumn(
"DateColumn",
to_date("StringDateColumn", "yyyy-MM-dd")
)
df = df.withColumn(
"TimestampColumn",
to_timestamp("StringTimestampColumn", "yyyy-MM-dd HH:mm:ss")
)
Show more lines
Make sure the format string matches the actual values in your source column. If the source data contains formats such as MM/dd/yyyy or dd-MM-yyyy, adjust the pattern accordingly. You can then write the transformed data into your Silver table with the target column defined as DATE or TIMESTAMP.
Hi Koritala,
You can't directly load a string into a DATE/TIMESTAMP column — you need to cast it during the SELECT/transform step before writing to Silver. Just make sure the source string format matches the pattern you pass to the conversion function.
Spark SQL
SELECT to_date(order_date_str, 'yyyy-MM-dd') AS order_date, to_timestamp(order_ts_str, 'yyyy-MM-dd HH:mm:ss') AS order_ts FROM bronze.ordersPySpark
from pyspark.sql.functions import to_date, to_timestamp silver_df = ( bronze_df .withColumn("order_date", to_date("order_date_str", "yyyy-MM-dd")) .withColumn("order_ts", to_timestamp("order_ts_str", "yyyy-MM-dd HH:mm:ss")) ) silver_df.write.mode("append").saveAsTable("silver.orders")
If your source format is different (e.g., dd/MM/yyyy or MM-dd-yyyy HH:mm), adjust the pattern accordingly — otherwise you'll get NULLs. Exact syntax can vary slightly by platform (Fabric / Databricks / Synapse), so if you share your source string format and target platform, I can tailor it further.Hope this helps!
If this got you what you needed, a Kudos and an Accepted Solution mark would be great — it helps others searching for the same thing find the answer quicker.
Disclosure: My earlier response above was drafted with the assistance of an AI tool (Microsoft Copilot) and cross-checked against Microsoft Learn documentation. Sharing this note for transparency as per the Community AI Usage PolicyHi Koritala
We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet. And, if you have any further query do let us know.
Thank you.
6 Replies
If you're working in Fabric using Spark or a notebook, you can convert a string column to either a DATE or TIMESTAMP using the built-in conversion functions.
For example, if your source column contains values such as '2026-07-02':
SQL
SELECT
TO_DATE(date_string_col, 'yyyy-MM-dd') AS converted_date
FROM source_table;
Show more lines
If the source value includes time, such as '2026-07-02 14:30:00', use:
SQL
SELECT
TO_TIMESTAMP(date_string_col, 'yyyy-MM-dd HH:mm:ss') AS converted_timestamp
FROM source_table;
Show more lines
In PySpark:
Python
from pyspark.sql.functions import to_date, to_timestamp
df = df.withColumn(
"DateColumn",
to_date("StringDateColumn", "yyyy-MM-dd")
)
df = df.withColumn(
"TimestampColumn",
to_timestamp("StringTimestampColumn", "yyyy-MM-dd HH:mm:ss")
)
Show more lines
Make sure the format string matches the actual values in your source column. If the source data contains formats such as MM/dd/yyyy or dd-MM-yyyy, adjust the pattern accordingly. You can then write the transformed data into your Silver table with the target column defined as DATE or TIMESTAMP.
Hi Koritala,
You can't directly load a string into a DATE/TIMESTAMP column — you need to cast it during the SELECT/transform step before writing to Silver. Just make sure the source string format matches the pattern you pass to the conversion function.
Spark SQL
SELECT to_date(order_date_str, 'yyyy-MM-dd') AS order_date, to_timestamp(order_ts_str, 'yyyy-MM-dd HH:mm:ss') AS order_ts FROM bronze.ordersPySpark
from pyspark.sql.functions import to_date, to_timestamp silver_df = ( bronze_df .withColumn("order_date", to_date("order_date_str", "yyyy-MM-dd")) .withColumn("order_ts", to_timestamp("order_ts_str", "yyyy-MM-dd HH:mm:ss")) ) silver_df.write.mode("append").saveAsTable("silver.orders")
If your source format is different (e.g., dd/MM/yyyy or MM-dd-yyyy HH:mm), adjust the pattern accordingly — otherwise you'll get NULLs. Exact syntax can vary slightly by platform (Fabric / Databricks / Synapse), so if you share your source string format and target platform, I can tailor it further.Hope this helps!
If this got you what you needed, a Kudos and an Accepted Solution mark would be great — it helps others searching for the same thing find the answer quicker.
Disclosure: My earlier response above was drafted with the assistance of an AI tool (Microsoft Copilot) and cross-checked against Microsoft Learn documentation. Sharing this note for transparency as per the Community AI Usage PolicyHi Koritala
We would like to inquire whether have you got the chance to check the solutions provided by Dev_Dholakia sannavajjala to resolve the issue. We hope the information provided helps to clear the query. Should you have any further queries, kindly feel free to contact the Microsoft Fabric community
Thanks,
Srikanth CheriCommunity Support Team
Hi Koritala
We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet. And, if you have any further query do let us know.
Thank you.Hi! What are you using to do the transformation? Notebook or Gen2DataFlow?
Notebook
