Braze + Snowflake
Connect Braze and Snowflake to Run Smarter Customer Campaigns
Put your warehouse data to work in your marketing platform — and get campaign results back where your analysts can actually use them.
Why integrate Braze and Snowflake?
Braze and Snowflake are both doing important jobs, but they weren't built to talk to each other out of the box. Braze runs your cross-channel campaigns — email, push, SMS, in-app. Snowflake holds the customer data your analysts have spent real time cleaning, enriching, and modeling. When they're not connected, marketing teams end up working with stale segments and manual CSV exports while the good data sits untouched in the warehouse. Connecting them through tray.ai automates the flow in both directions: enriched attributes and predictive scores go from Snowflake into Braze, and campaign engagement data comes back into Snowflake for attribution and reporting.
Automate & integrate Braze & Snowflake
Use case
Sync Enriched User Profiles from Snowflake to Braze
Automatically push enriched customer attributes — LTV tiers, product affinity scores, churn risk labels — from Snowflake into Braze user profiles. Your marketing segments in Braze will reflect what your data science or analytics teams have actually computed, not whatever was last manually exported. Campaigns can be triggered and personalized based on these attributes without anyone touching a CSV.
Use case
Write Braze Campaign Engagement Data Back to Snowflake
Automatically export Braze engagement events — opens, clicks, conversions, unsubscribes, attributed revenue — back into Snowflake for centralized reporting. Your data warehouse ends up with the full customer journey from acquisition through engagement, so analysts can build multi-touch attribution models and measure real marketing ROI without digging through Braze dashboards.
Use case
Trigger Braze Campaigns Based on Snowflake Events
Use tray.ai to watch Snowflake tables or streams for business events — a customer hitting a spending threshold, completing onboarding, entering a churn-risk cohort — and automatically trigger the right Braze campaign in response. Your warehouse logic drives your marketing automation, so the right message goes out the moment the data says it should.
Use case
Build and Refresh Braze Audience Segments from Snowflake Cohorts
Automatically build or refresh Braze subscription groups and audience segments from cohort queries run against Snowflake. Your data team defines precise cohorts in SQL — combining CRM, product, and transactional data — and tray.ai pushes the resulting user lists into Braze on a schedule or when triggered. Marketers get sophisticated segmentation without waiting on engineering.
Use case
Sync Braze Custom Events to Snowflake for Predictive Modeling
Stream custom behavioral events tracked in Braze — feature usage, in-app clicks, content interactions — into Snowflake so data science teams can use them in predictive models. Once those models produce scores or labels, the results feed back into Braze to personalize future campaigns. What users do in your product informs what you say to them next.
Use case
Automate Braze User Deletion and Suppression Based on Snowflake Compliance Lists
Automatically suppress or delete user records in Braze when they appear on Snowflake-managed compliance lists — GDPR deletion requests, opt-out registries, legal hold lists. tray.ai watches the relevant Snowflake tables and triggers the appropriate Braze API actions so your engagement platform stays compliant. No one accidentally messages a user who asked to be forgotten.
Use case
Populate Braze Catalogs with Product and Content Data from Snowflake
Keep Braze Catalogs continuously updated with the latest product, inventory, and content metadata from Snowflake. When prices change, new products are added, or inventory shifts in your warehouse, tray.ai reflects those changes in Braze Catalogs automatically — so personalized recommendations are never based on outdated information.
Get started with Braze & Snowflake integration today
Braze & Snowflake Challenges
What challenges are there when working with Braze & Snowflake and how will using Tray.ai help?
Challenge
Handling Large-Volume Snowflake Query Results Without Timeouts
Snowflake queries over large datasets can return millions of rows, making it impractical to load everything into memory at once when syncing cohorts or events to Braze. Straightforward integrations frequently hit memory limits, timeout errors, or Braze API rate limits when attempting bulk transfers.
How Tray.ai Can Help:
tray.ai handles large result sets through built-in pagination and chunked iteration, processing Snowflake query results in configurable batches and respecting Braze API rate limits between each batch. Transfers complete reliably regardless of dataset size, without any custom infrastructure on your end.
Challenge
Keeping Data in Sync Without Full Overwrite on Every Run
Re-syncing entire Snowflake tables to Braze on every workflow run is slow, expensive, and risks overwriting valid data with stale records. Incremental sync logic requires tracking watermarks, detecting changes, and carefully merging updates — which is hard to maintain in hand-built scripts.
How Tray.ai Can Help:
tray.ai workflows support stateful watermark tracking using workflow data storage, so each run only processes new or changed records since the last successful sync. Combined with Snowflake MERGE statements and conditional logic in tray.ai, your data stays fresh without full-table overwrites.
Challenge
Mapping Complex Snowflake Schemas to Braze User Profile Attributes
Snowflake data models often use normalized schemas, nested JSON fields, or domain-specific naming conventions that don't map directly to Braze's flat user profile attribute structure. Manual field mapping is tedious, error-prone, and breaks whenever the underlying Snowflake schema changes.
How Tray.ai Can Help:
tray.ai's visual data mapper and JSONPath transformation tools make it straightforward to reshape complex Snowflake output into the exact structure the Braze /users/track API expects. Transformations live centrally in the workflow, so schema changes are fast to update and easy to audit.
Challenge
Enforcing Data Governance Across the Integration
Syncing customer data between Snowflake and Braze raises real data governance questions — making sure only consented users get messaged, that PII isn't replicated unnecessarily, and that deletion requests reach both systems promptly. These requirements are genuinely hard to enforce consistently in manual or script-based integrations.
How Tray.ai Can Help:
tray.ai lets you build governance controls directly into integration workflows — consent-flag filtering before data goes to Braze, automated deletion propagation from Snowflake compliance tables, and field-level masking to avoid transmitting PII you don't need to send. Every workflow run is logged for audit purposes.
Challenge
Managing Authentication and Credential Rotation Across Both Platforms
Braze uses REST API keys scoped to specific endpoints; Snowflake supports key-pair authentication, OAuth, and role-based access control. Hardcoded or poorly managed credentials in integration scripts create security risks and cause outages the moment anything rotates.
How Tray.ai Can Help:
tray.ai stores all Braze and Snowflake credentials in an encrypted, centralized vault with role-based access controls. When credentials need to be rotated, you update them once and every dependent workflow picks up the change instantly — no hardcoded secrets, no credential-related outages.
Start using our pre-built Braze & Snowflake templates today
Start from scratch or use one of our pre-built Braze & Snowflake templates to quickly solve your most common use cases.
Braze & Snowflake Templates
Find pre-built Braze & Snowflake solutions for common use cases
Template
Snowflake Cohort to Braze Segment Sync
On a set schedule, this template runs a SQL query against Snowflake to fetch a target user cohort, then creates or updates the corresponding audience segment in Braze — so your marketing segments always reflect the latest warehouse data.
Steps:
- Schedule the workflow to run at a defined frequency (e.g., hourly or daily)
- Execute a parameterized SQL query in Snowflake to retrieve the target user cohort
- Paginate through query results and batch-format user identifiers
- Call the Braze API to create or update the target audience segment with the cohort user list
- Log sync results and send an alert if record counts fall outside expected thresholds
Connectors Used: Snowflake, Braze
Template
Braze Engagement Events to Snowflake Pipeline
This template continuously exports Braze campaign and Canvas engagement events — sends, opens, clicks, and conversions — into a structured Snowflake table, giving your team a reliable data pipeline for attribution reporting and BI analysis.
Steps:
- Poll the Braze Export API or listen for Braze Currents webhook events at a set interval
- Transform and normalize raw Braze event payloads into a structured schema
- Deduplicate events using Snowflake merge logic to prevent double-counting
- Insert cleaned records into the designated Snowflake events table
- Trigger a downstream dbt or Snowflake task to refresh attribution models
Connectors Used: Braze, Snowflake
Template
Snowflake Churn Risk Score Sync to Braze User Attributes
Automatically reads churn risk scores from your data science models in Snowflake and writes them to Braze user profiles as custom attributes — so marketers can build retention campaigns targeting high-risk users without any manual data transfer.
Steps:
- Query the Snowflake model output table for users with updated churn risk scores
- Filter to only users whose scores have changed since the last sync run
- Map Snowflake fields to Braze custom attribute names and data types
- Call the Braze /users/track endpoint in batches to update user profiles
- Write sync status and record counts back to a Snowflake audit log table
Connectors Used: Snowflake, Braze
Template
GDPR Deletion Request Compliance Sync
Watches a Snowflake table for newly logged GDPR or CCPA deletion requests and automatically triggers user deletion or suppression in Braze — so compliance timelines are met and every deletion has an auditable end-to-end record.
Steps:
- Poll the Snowflake deletion request table for new unprocessed records
- Extract external user IDs or email addresses flagged for deletion
- Call the Braze /users/delete endpoint for each identified user
- Update the Snowflake deletion request record with a processed timestamp and status
- Generate a compliance audit report and route it to the designated data privacy channel
Connectors Used: Snowflake, Braze
Template
Snowflake Product Catalog to Braze Catalog Refresh
Keeps Braze Catalogs in sync with product, pricing, and inventory data from Snowflake by running a scheduled comparison and pushing only changed or new records to Braze — so personalized campaign content stays accurate.
Steps:
- Query Snowflake for all active product records modified since the last sync timestamp
- Compare records against the current Braze Catalog state to identify additions and updates
- Format product records to match the Braze Catalog item schema
- Batch-upload new or updated items to the Braze Catalog via the Catalogs API
- Archive discontinued products in Braze and log all changes to a Snowflake sync audit table
Connectors Used: Snowflake, Braze
Template
Braze Custom Event Stream to Snowflake for ML Feature Engineering
Streams granular Braze custom behavioral events into Snowflake in near real-time, making them available for data science teams to use as features in machine learning models that feed predictions back into Braze.
Steps:
- Receive Braze Currents events via webhook or poll the Braze Export API for custom events
- Parse and validate the event payload schema, flagging malformed records for review
- Transform events into a flat, analytics-ready structure with consistent field naming
- Stream records into the Snowflake raw events table using COPY INTO or Snowpipe
- Trigger a notification to the data science team when a new event type is detected for schema review
Connectors Used: Braze, Snowflake