Real-time
Two-way sync
Changes in Actian Vector or Snowflake instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Actian Vector to Snowflake integration — real-time, two-way sync
Keep Actian Vector and Snowflake 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 Snowflake
Keep tables consistent across Actian Vector and Snowflake, 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 Snowflake 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 Publish aggregates computed in Vector back to operational tools that business teams use.
- 02 Consolidate data from several source systems into one Vector database for BI dashboards.
- 03 Keep a customer 360 table aligned with its source systems in both directions instead of one-way reverse ETL
- 04 Push product usage aggregates from Snowflake into sales and success tools for account prioritization
Common sync patterns
Shared datasets across teams
Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
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.
What you can sync between Actian Vector and Snowflake
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 | Snowflake objects | How this pairing syncs | |
|---|---|---|---|
| Databases Top-level containers targeted by a sync connection. | Databases Top-level containers that scope which data a sync can touch. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Schemas Namespaces used to organize synced tables. | Schemas Namespaces within a database used to organize synced tables. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Tables Columnar tables that serve as sync sources or destinations. | Tables The main landing and activation target for synced records. | 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 Modeled projections used as the source side of outbound syncs. | 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. | Tasks Scheduled SQL used to transform synced data after it lands. | Users and Roles is specific to Actian Vector and Tasks to Snowflake — each maps to any object or custom field on the other side. | |
| Columns Typed columns mapped field-by-field during schema mapping. | VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. | Columns is specific to Actian Vector and VARIANT Columns to Snowflake — each maps to any object or custom field on the other side. |
How changes propagate between Actian Vector and Snowflake
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 Snowflake 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 Snowflake as a row-level write, with types converted between the two schemas.
Snowflake Actian Vector Sub-second propagation
DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.
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.
- Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Actian Vector ⇄ Snowflake
Connect Actian Vector and Snowflake for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Actian Vector–Snowflake connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Actian Vector or Snowflake 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 Snowflake record.
Observability
Monitoring
Track your Actian Vector ⇄ Snowflake 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 Snowflake.
How the Actian Vector and Snowflake 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
Snowflake
- Integration surface
- SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
- Authentication
- Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
- Change detection
- Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
- Capabilities
- read · write · CDC
- Rate limits
- No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
How it works
How to connect Actian Vector to Snowflake — 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 Snowflake 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
Snowflake
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Actian Vector and Snowflake 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 ⇄ SnowflakeCustomers 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 SnowflakeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Actian Vector and Snowflake integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Actian Vector and Snowflake: authenticate both systems, choose the objects to sync (such as Actian Vector's Databases and Schemas), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Actian Vector and Snowflake. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Actian Vector: Polling on timestamp or key columns; no log-based CDC interface is generally exposed to external consumers. On Snowflake: Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Actian Vector side: Tables, Views, Columns, Users and Roles, plus custom fields where Actian Vector exposes them. On the Snowflake side: Views, Materialized Views, Streams, Stages. Stacksync auto-detects both schemas and converts types between the two systems.
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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 Actian Vector and Snowflake: Shared datasets across teams; Consolidation after M&A; Migration without a big bang. Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
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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