Snowflake

Connection requirements

To connect to Snowflake you need to install the ODBC connector:

https://docs.snowflake.net/manuals/user-guide/odbc-download.html

Connecting to Snowflake

To connect to Snowflake, create new documentation by clicking Add documentation and choosing Database connection.

On the connection screen, choose Snowflake as DBMS.

Select authentication mode:

• Password - you will need to provide a username and password.
• SSO (browser) - Dataedo will open a web browser to let you authenticate with Okta.
• JWT (private key) - authentication with a private key. Learn more

Provide database connection details:

• Host - provide a host name or address where the database is located. E.g. server17, server17.ourdomain.com or 192.168.0.37.
• Port - change the default port of the Amazon Redshift instance if required.
• User and password - provide your username and password.
• Database - type in the schema name.

Here is a comparison with connection details in DBeaver.

Saving password

You can save the password for later connections by checking the Save password option. Passwords are saved in the repository database.

Importing schema

When the connection is successful, Dataedo will read objects and show a list of objects found. You can choose which objects to import. You can also use advanced filter to narrow down the list of objects.

Confirm the list of objects to import by clicking Next.

The next screen will allow you to change the default name of the documentation under which your schema will be visible in the Dataedo repository.

Click Import to start the import.

When done, close the import window with the Finish button.

Your database schema has been imported to new documentation in the repository.

Importing changes

To sync any changes in the schema in Snowflake and reimport any technical metadata, simply choose the Import changes option. You will be asked to connect to Snowflake again and changes will be synced from the source.

Specification

Imported metadata

	Imported	Editable
Tables, Transient tables	✅	✅
Columns	✅	✅
Data types	✅	✅
Nullability	✅
Column comments	✅	✅
Table comments	✅	✅
Foreign keys	✅	✅
Primary keys	✅	✅
Unique keys	✅	✅
External tables, Iceberg tables	✅	✅
Location	✅	✅
Script	✅	✅
Columns	✅	✅
Data types	✅	✅
Nullability	✅
Column comments	✅	✅
Table comments	✅	✅
Foreign keys	✅	✅
Primary keys	✅	✅
Views, Materialized Views	✅	✅
Script	✅	✅
Columns	✅	✅
Data types	✅	✅
Column comments	✅	✅
View comments	✅	✅
Dynamic tables	✅	✅
Script	✅	✅
Columns	✅	✅
Data types	✅	✅
Column comments	✅	✅
Table comments	✅	✅
Stored procedures	✅	✅
Script	✅	✅
Parameters	✅	✅
Procedure comments	✅	✅
Tasks	✅	✅
Script	✅	✅
Task comments	✅	✅
User-defined Functions	✅	✅
Script	✅	✅
Input arguments (all as a single field)	✅	✅
Output results (as a single field)	✅	✅
Function comments	✅	✅
Stages	✅	✅
Location	✅	✅
Stage comments	✅	✅
Pipelines	✅	✅
Script	✅	✅
Pipeline comments	✅	✅
Sequences	✅	✅
Streams	✅	✅
Columns	✅	✅
Script	✅	✅
Shared metadata
Dependencies	✅	✅
Created time	✅
Last updated time	✅

Comments

Dataedo reads comments from the following Snowflake objects:

Object	Read	Write back
Tables, Transient tables	✅
Column comments	✅
External tables, Iceberg tables	✅
Column comments	✅
Views, Materialized Views	✅
Columns	✅
Dynamic tables	✅
Columns	✅
Stored procedures	✅
Tasks	✅
Function	✅
Stages	✅
Pipelines	✅

Data profiling

Dataedo supports the following data profiling in Snowflake:

Profile	Support
Table row count	✅
Table sample data	✅
Column distribution (unique, non-unique, null, empty values)	✅
Min, max values	✅
Average	✅
Variance	✅
Standard deviation	✅
Min-max span	✅
Number of distinct values	✅
Top 10/100/1000 values	✅
10 random values	✅

Data Lineage

Source	Method	Status
Views - object level	From dependencies	✅
Views - object level	From SQL parsing	✅
Views - column level	From SQL parsing	✅
Dynamic tables - object level	From INFORMATION_SCHEMA	✅
Dynamic tables - column level	From SQL parsing	✅
External storages (Azure, AWS) -> Stages	From INFORMATION_SCHEMA	✅
Stages -> External tables (Azure, AWS)	From INFORMATION_SCHEMA	✅
Stages -> Pipelines	From SQL parsing	✅
Pipelines -> Tables	From SQL parsing	✅
Stages -> Tables (COPY INTO)	FROM ACCOUNT_USAGE	✅
Table/View -> Streams	FROM ACCOUNT_USAGE	✅
Tables - from Kafka	TBD	TBD
dbt	dbt connector	✅

Column-level data lineage is retrieved using Dataedo SQL parser. Read more about the capabilities of Snowflake SQL parser.

Read more about Dataedo Snowflake data lineage).

Known limitations

• Snowflake’s INFORMATION_SCHEMA currently doesn’t contain data on object dependencies or usage of COPY INTO statements. Information about dependencies and COPY INTO history is retrieved from the SNOWFLAKE.ACCOUNT_USAGE.OBJECT_DEPENDENCIES and SNOWFLAKE.ACCOUNT_USAGE.COPY_HISTORY views. By default, the SNOWFLAKE database is available only to the ACCOUNTADMIN role. Read more about Enabling Snowflake Database Usage for Other Roles.

Due to Snowflake Connector for .NET limitations:

• Username and password
  • Cannot contain ; and = characters
• Database, role and warehouse name
  • Cannot contain ; and ‘ or \ characters

To import stream columns, Dataedo requires access to SNOWFLAKE.ACCOUNT_USAGE.COLUMNS.

If one schema contains more than:

• 5000 foreign keys,
• or 6000 unique keys,
• or 10 000 primary keys,

then Dataedo will read those keys per object, not per schema - this will result in longer import time.

Learn more

Connect to Snowflake

Data Lineage from Snowflake

Snowflake Enterprise Connector