Real-time
Two-way sync
Changes in Databricks or Klaviyo instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Business productivity
Databricks to Klaviyo integration — real-time, two-way sync
Keep Databricks and Klaviyo in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from Mulesoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
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Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Databricks and Klaviyo
Get the data locked inside Klaviyo into Databricks as live tables, and send results back where Klaviyo can use them, without writing a pipeline.
Whatever Klaviyo is used for, it accumulates data the rest of the company wants to analyze, and that data usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay for it.
Stacksync syncs Campaigns, Flows, Templates, Catalog Items from Klaviyo into tables in Databricks continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in Databricks can also be written back into fields in Klaviyo where the tool can use them.
Common use cases
- Mirror campaign and flow engagement events into a warehouse for revenue attribution alongside order data
- Keep suppression and consent status consistent between Klaviyo and other email or SMS tools
- Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
Analytics on Klaviyo's data
Records and events from Klaviyo land in Databricks as queryable tables, current within seconds and ready to join with the rest of the warehouse.
Cross-tool reporting
Combine Klaviyo's data with data from every other synced system to answer questions no single tool can.
Where Klaviyo accepts updates: operational write-back
Segments, scores, or reference values computed in Databricks sync back onto records in Klaviyo, putting analysis where the work happens.
What you can sync between Databricks and Klaviyo
Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.
| Databricks objects | Klaviyo objects | |
|---|---|---|
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Metrics Event type definitions organize the activity stream used in segmentation and attribution. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Campaigns Email and SMS sends expose performance data for warehouse-based reporting. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Flows Automation definitions and their engagement data sync outward for attribution. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Templates Message templates are accessible via API for content management workflows. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Catalog Items Product catalog records synced from an ERP or PIM power product blocks and back-in-stock triggers. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Tags Organizational labels on campaigns, flows, and lists support reporting rollups. |
What ships with Databricks ⇄ Klaviyo
Connect Databricks and Klaviyo for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Klaviyo connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Klaviyo data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
At scale
Event queues
Handle millions of events per minute without losing a single Databricks or Klaviyo record.
Observability
Monitoring
Track your Databricks ⇄ Klaviyo sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Databricks and Klaviyo.
How the Databricks and Klaviyo connectors work
Databricks
- Integration surface
- SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
- Authentication
- Personal access tokens or OAuth machine-to-machine credentials for service principals
- Change detection
- Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
- Capabilities
- read · write · CDC
- Rate limits
- Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
Klaviyo
- Integration surface
- REST API compliant with the JSON:API specification
- Authentication
- OAuth 2.0 for apps or private API keys
- Change detection
- Polling on updated timestamps; outbound webhooks exist for event topics (availability depends on plan or app-partner status) and as flow webhook actions
- Capabilities
- read · write · webhooks
- Rate limits
- Per-endpoint burst and steady rate limits apply
How it works
How to connect Databricks to Klaviyo — three steps, no code
Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.
- 01
Connect your apps
Authenticate Databricks and Klaviyo with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Databricks
connected
Klaviyo
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Klaviyo objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.
objects · Databricks ⇄ KlaviyoCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Databricks KlaviyoCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Klaviyo integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Klaviyo: authenticate both systems, choose the objects to sync (such as Databricks's Catalogs and Schemas), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Databricks and Klaviyo: Analytics on Klaviyo's data; Cross-tool reporting; Where Klaviyo accepts updates: operational write-back. Records and events from Klaviyo land in Databricks as queryable tables, current within seconds and ready to join with the rest of the warehouse.
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Databricks: SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution. Authentication: Personal access tokens or OAuth machine-to-machine credentials for service principals. Klaviyo: REST API compliant with the JSON:API specification. Authentication: OAuth 2.0 for apps or private API keys. Stacksync manages authentication, retries, and rate limits on both sides.
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Klaviyo: Klaviyo's current APIs follow the JSON:API specification, so payloads use typed resource objects with relationships rather than flat JSON. Databricks: Delta Lake's Change Data Feed records row-level inserts, updates, and deletes, enabling incremental sync without full scans. Stacksync's field mapping accounts for these differences between Databricks and Klaviyo without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Databricks and Klaviyo records are not retained after a sync operation.
SECURITY
Security teams love Stacksync
As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS
SSO & SCIM
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Alerts
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Secure connection options
Securely connects to your systems with:
Related integrations
Every pair below is a real-time, two-way sync. Search all 386 integrations available for Databricks and Klaviyo.
Popular · 8 of 386
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Databricks ⇄ Twilio -
Databricks ⇄ GitHub -
Databricks ⇄ Atlassian -
Databricks ⇄ Braze -
Klaviyo ⇄ Teradata Vantage -
Klaviyo ⇄ Tinybird -
Klaviyo ⇄ Vertica -
Klaviyo ⇄ Yellowbrick
No integrations match .
