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Read and write data from Snowflake

important

The legacy query federation documentation has been retired and might not be updated. The products, services, or technologies mentioned in this content are not officially endorsed or tested by Databricks. See What is Lakehouse Federation? instead.

Databricks provides a Snowflake connector in the Databricks Runtime to support reading and writing data from Snowflake.

Experimental

The configurations described in this article are Experimental. Experimental features are provided as-is and are not supported by Databricks through customer technical support. To get full query federation support, you should instead use Lakehouse Federation, which enables your Databricks users to take advantage of Unity Catalog syntax and data governance tools.

Query a Snowflake table in Databricks​

You can configure a connection to Snowflake and then query data. Before you begin, check which version of Databricks Runtime your cluster runs on. The following code provides example syntax in Python, SQL, and Scala.

Python

# The following example applies to Databricks Runtime 11.3 LTS and above.

snowflake_table = (spark.read
  .format("snowflake")
  .option("host", "hostname")
  .option("port", "port") # Optional - will use default port 443 if not specified.
  .option("user", "username")
  .option("password", "password")
  .option("sfWarehouse", "warehouse_name")
  .option("database", "database_name")
  .option("schema", "schema_name") # Optional - will use default schema "public" if not specified.
  .option("dbtable", "table_name")
  .load()
)

# The following example applies to Databricks Runtime 10.4 and below.

snowflake_table = (spark.read
  .format("snowflake")
  .option("dbtable", table_name)
  .option("sfUrl", database_host_url)
  .option("sfUser", username)
  .option("sfPassword", password)
  .option("sfDatabase", database_name)
  .option("sfSchema", schema_name)
  .option("sfWarehouse", warehouse_name)
  .load()
)

Notebook example: Snowflake Connector for Spark​

The following notebooks provide simple examples of how to write data to and read data from Snowflake. See Snowflake Connector for Spark for more details.

tip

Avoid exposing your Snowflake username and password in notebooks by using Secrets, which are demonstrated in the notebooks.

Snowflake Python notebook

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Notebook example: Save model training results to Snowflake​

The following notebook walks through best practices for using the Snowflake Connector for Spark. It writes data to Snowflake, uses Snowflake for some basic data manipulation, trains a machine learning model in Databricks, and writes the results back to Snowflake.

Store ML training results in Snowflake notebook

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Frequently asked questions (FAQ)​

Why don’t my Spark DataFrame columns appear in the same order in Snowflake?​

The Snowflake Connector for Spark doesn’t respect the order of the columns in the table being written to; you must explicitly specify the mapping between DataFrame and Snowflake columns. To specify this mapping, use the columnmap parameter.

Why is INTEGER data written to Snowflake read back as DECIMAL?​

Snowflake represents all INTEGER types as NUMBER, which can cause a change in data type when you write data to and read data from Snowflake. For example, INTEGER data can be converted to DECIMAL when writing to Snowflake, because INTEGER and DECIMAL are semantically equivalent in Snowflake (see Snowflake Numeric Data Types).

Why are the fields in my Snowflake table schema always uppercase?​

Snowflake uses uppercase fields by default, which means that the table schema is converted to uppercase.

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