Connecting Snowflake to Streamline – Streamline

Connecting a Snowflake database to your project is a key step in leveraging the powerful capabilities of Streamline. This guide walks you through integrating a Snowflake data source and creating datasets for use in your workflows.

Note: This article is specific to Snowflake. If you're connecting a different data source, check our Data Source Library for platform-specific instructions.

Prerequisites

Before you begin, ensure the following:

You have network access to your Snowflake account (e.g., correct region URL and access permissions).
Your Snowflake user account has appropriate roles and privileges (e.g., USAGE and SELECT on the database, schema, and tables).
Required firewall or network configurations are in place to allow secure outbound HTTPS traffic to Snowflake.

If unsure, check with your database administrator or Snowflake account admin.

Part 1: Connect to a Snowflake Data Source

Step 1: Go to the Integrations Page

From your dashboard, navigate to the Integrations tab on the left.
Click + New Connection at the top of the page or the centre.

Step 2: Select Snowflake as Your Integration Type

In the pop-up window, choose Snowflake from the list of supported databases.

Give your connection a recognizable name & description (e.g., Snowflake - Prod Warehouse, Snowflake Reporting).

Tip: Use names that help your team quickly understand the connection’s purpose or environment.

Step 3: Enter Snowflake Connection Details

Provide the following details:

Host
Warehouse
Database (target Snowflake database)
Username
Private Key

Click Next to test the connection.

Step 4: Choose Your Database and Schema

Once authenticated, you'll be prompted to select the database and schema you want to connect to.

Step 5: Finalize the Connection

Click Connect to complete the integration. Your Snowflake connection will now appear in the Integrations list.

Step 6: Explore Your Snowflake Data

Once connected:

Navigate to the Data Catalog to browse tables and fields in your Snowflake schema.
Use search or filters to find specific entities.
The Entity View displays tables; the Data Field View shows individual columns with metadata (data type, nullability, etc.).

Part 2: Create and Manage a Dataset from Snowflake

Once your Snowflake connection is active, you can organize your data into reusable datasets for your projects and workflows.

Step 1: Access the Datasets Section

Go to Datasets via the left-hand panel.
Click + Create New Dataset.

Step 2: Define Dataset Basics

Give your dataset a clear and intuitive name.
Select your Snowflake data source.
Choose a Primary Entity (table) from your schema.
Add a label to make database terms more user-friendly.
Example: Use “Sales Orders” instead of STG_ORDERS_VW.

Step 3: Configure Field Access

Review available fields (columns).
Configure field visibility and write access where applicable.
Click Create to save the dataset.

Optional: Create a Dataset Directly from the Data Catalog

From the Data Catalog, select a table.
Click New Dataset from Entity.
Customize the dataset by choosing fields or editing labels.

Optional: Add Related Entities

To enrich your dataset:

During dataset creation, click Add Related Entity.
The platform will automatically detect foreign key relationships where available (if constraints are defined in Snowflake).
You can manually add joins as well if needed.
Add as many related tables as needed before finalizing.

Optional: Edit an Existing Dataset

To update an existing dataset:

Go to Datasets and locate the dataset.
Click Edit to:
- Add/remove related entities
- Change field names or labels
- Adjust data access settings
Click Next & Update to save your changes.

Troubleshooting Tips

Encountering issues? Here are some common solutions:

Connection fails: Check account URL and warehouse/database names.
Authentication errors: Ensure your Snowflake credentials are valid and that your user has the correct role.
Schema access issues: Confirm your role has USAGE and SELECT on the database and schema.
Firewall/network problems: Ensure outbound HTTPS traffic to Snowflake is allowed.

Contact your Snowflake admin or IT support if issues persist.

Performance Tips

Choose only necessary columns to keep datasets efficient.
Use filters or SQL WHERE clauses for targeted data retrieval.
Regularly audit and update datasets to remove unused or redundant data.
Consider using materialized views or external functions for complex transformations.

Connecting: Best Practices and Requirements

Currently, only private key authorization is supported for connecting to Snowflake.

The private key must be in unencrypted PKCS#8 format.

Generate Your Key

Use the following command to generate a private RSA key:

openssl genpkey -algorithm RSA -out rsa_key.p8 -pkeyopt rsa_keygen_bits:2048

Extract the Public Key

Then extract the corresponding public key:

openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub

Register Public Key in Snowflake

Open a SQL Worksheet in Snowflake and run the following command, replacing your_user with your username and ...with the actual public key content:

alter user your_user set rsa_public_key='...';

Note: If you do not have the required permissions, you may need assistance from your Snowflake ACCOUNTADMIN to run this command.

Create Streamline Integration

When creating the integration in Streamline, upload your rsa_key.p8 file from your local machine to authenticate the connection.

Limitations

Supported Data Types

Streamline currently supports a subset of Snowflake's data types. The following are supported (including their synonyms):

VARCHAR
TIMESTAMPTZ
TIMESTAMPNTZ
TIMESTAMPLTZ
DATE
DOUBLE
BOOLEAN
TIME
NUMBER
BINARY

For full reference, see the official Snowflake Data Types documentation.

Summary

By connecting your Snowflake environment and building datasets, you ensure your data is clean, accessible, and workflow-ready. Whether you're building datasets from scratch or using the Data Catalog, Streamline provides control over your Snowflake data.

Need help? Check our FAQs/articles or contact Support.