Real-time
Two-way sync
Changes in Actian Vector or Databricks instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Actian Vector to Databricks integration — real-time, two-way sync
Keep Actian Vector and Databricks 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
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Actian Vector and Databricks
Keep tables consistent across Actian Vector and Databricks, for a migration, a multi-warehouse stack, or a dataset two platforms both need.
Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.
Stacksync syncs tables between Actian Vector and Databricks continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.
Common use cases
- 01 Keep reporting tables in Vector refreshed from production databases on a schedule.
- 02 Publish aggregates computed in Vector back to operational tools that business teams use.
- 03 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- 04 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
Common sync patterns
Consolidation after M&A
Bring the acquired company's warehouse data across continuously instead of through one-off dumps.
Migration without a big bang
When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
Serve tools that only connect to one platform
Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.
What you can sync between Actian Vector and Databricks
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.
| Actian Vector objects | Databricks objects | How this pairing syncs | |
|---|---|---|---|
| Schemas Namespaces used to organize synced tables. | Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Views SQL views readable as query-backed sync sources. | Views Curated read-only projections used as sync sources for downstream tools. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Users and Roles Database principals used to grant the sync connection least-privilege access. | Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Users and Roles is specific to Actian Vector and Delta Tables to Databricks — each maps to any object or custom field on the other side. | |
| Databases Top-level containers targeted by a sync connection. | Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Databases is specific to Actian Vector and Materialized Views to Databricks — each maps to any object or custom field on the other side. | |
| Tables Columnar tables that serve as sync sources or destinations. | Volumes Unity Catalog file storage used for staging bulk loads. | Tables is specific to Actian Vector and Volumes to Databricks — each maps to any object or custom field on the other side. | |
| Columns Typed columns mapped field-by-field during schema mapping. | SQL Warehouses The compute endpoint a sync connects to for query execution. | Columns is specific to Actian Vector and SQL Warehouses to Databricks — each maps to any object or custom field on the other side. |
How changes propagate between Actian Vector and Databricks
Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.
Actian Vector Databricks Interval-based propagation
DetectionStacksync polls Actian Vector for changes on an incremental schedule, reading only records changed since the previous pass. Polling on timestamp or key columns.
DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.
Databricks Actian Vector Sub-second propagation
DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.
DeliveryEach detected change is applied to Actian Vector as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Actian Vector: No API rate limits in the SaaS sense; throughput depends on cluster resources and query concurrency.
- Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Actian Vector ⇄ Databricks
Connect Actian Vector and Databricks for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Actian Vector–Databricks connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Actian Vector or Databricks 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 Actian Vector or Databricks record.
Observability
Monitoring
Track your Actian Vector ⇄ Databricks sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Actian Vector and Databricks.
How the Actian Vector and Databricks connectors work
Actian Vector
- Integration surface
- SQL over JDBC/ODBC
- Authentication
- Database credentials
- Change detection
- Polling on timestamp or key columns; no log-based CDC interface is generally exposed to external consumers
- Capabilities
- read · write
- Rate limits
- No API rate limits in the SaaS sense; throughput depends on cluster resources and query concurrency
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
How it works
How to connect Actian Vector to Databricks — 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 Actian Vector and Databricks with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Actian Vector
connected
Databricks
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Actian Vector and Databricks 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 · Actian Vector ⇄ DatabricksCustomers 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.
Actian Vector DatabricksCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Actian Vector and Databricks integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Actian Vector and Databricks: authenticate both systems, choose the objects to sync (such as Actian Vector's Schemas and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Actian Vector: SQL over JDBC/ODBC. Authentication: Database credentials. 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. Stacksync manages authentication, retries, and rate limits on both sides.
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Actian Vector: Actian Vector is a columnar analytics database with vectorized query execution, so it is used as an analytical destination rather than a transactional source. Databricks: Unity Catalog imposes a three-level namespace (catalog.schema.table) that governs access across workspaces. Stacksync's field mapping accounts for these differences between Actian Vector and Databricks 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 Actian Vector and Databricks records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Actian Vector and Databricks connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Actian Vector–Databricks integration in-house.
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Yes — Stacksync ships production-grade connectors for both Actian Vector and Databricks. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
SECURITY
Security teams trust 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
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
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