Snowflake

Data Warehouse

Cloud data platform for the enterprise.

About Snowflake

Snowflake is the modern data warehouse. It separates storage from compute, allowing infinite scale. However, it is an island without bridges. Integrating Snowflake involves two pipelines: ETL (getting data IN from Salesforce/Shopify) and Reverse ETL (getting insights OUT to Slack/Email), making it the single source of truth for the organization.

Integration Capabilities

Snowflake has 6 native integrations with leading business platforms. Connect via direct API, webhooks, or third-party automation tools for unlimited possibilities.

Browse Snowflake integrations below. Green integrations are native and free. Yellow integrations require an automation platform like Make ↗ to sync data.

Common Integration Patterns

→ 360 Customer View: Combining Salesforce data (Sales), Stripe data (Finance), and Segment data (Product) into one table for analysis.
→ Reverse ETL: Pushing a 'Health Score' calculated in Snowflake back into the Salesforce Account record for CSMs to see.
→ Alerting: Using middleware to query Snowflake for anomalies (e.g., 'Revenue dropped 20%') and posting to Slack.

Integration Challenges

⚠ Latency: Business users expect 'Real-Time', but Snowflake pipelines usually run in batches (hourly/daily). Managing expectations is key.
⚠ Cost Control: Poorly written integration queries that scan full tables frequently can skyrocket compute credits.
⚠ Schema Drift: If a field changes in Salesforce, the ETL pipeline often breaks, causing the Snowflake table to become stale.

Before You Integrate

1. Use Incremental Sync: Ensure your ETL tool uses 'Incremental' replication keys (Last Modified Date) instead of 'Full Load'.
2. Separate Warehouses: Create a dedicated Warehouse (compute cluster) for integration queries so they don't slow down BI analysts.
3. Mask PII: Configure Dynamic Data Masking on PII columns before granting access to integration users.