Two-way sync
Changes in Databricks or Teradata Vantage instantly reflect in both systems. No stale data, no manual imports.
Keep Databricks and Teradata Vantage in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
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 Databricks and Teradata Vantage 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.
Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
Bring the acquired company's warehouse data across continuously instead of through one-off dumps.
When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
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 | Teradata Vantage objects | How this pairing syncs | |
|---|---|---|---|
| Views Curated read-only projections used as sync sources for downstream tools. | Views The conventional access layer in Teradata shops; syncs often read views rather than base tables. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Macros Stored parameterized SQL that encapsulates repeatable reads. | Delta Tables is specific to Databricks and Macros to Teradata Vantage — each maps to any object or custom field on the other side. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Stored procedures Server-side logic sometimes invoked as part of load workflows. | Materialized Views is specific to Databricks and Stored procedures to Teradata Vantage — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Users In Teradata, users are databases with a password, and they own objects and space. | Volumes is specific to Databricks and Users to Teradata Vantage — each maps to any object or custom field on the other side. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Columns Teradata SQL types mapped to the paired system's field types during sync. | SQL Warehouses is specific to Databricks and Columns to Teradata Vantage — each maps to any object or custom field on the other side. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Databases Hierarchical containers that own tables and space allocations. | Change Data Feed is specific to Databricks and Databases to Teradata Vantage — each maps to any object or custom field on the other side. |
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.
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 Teradata Vantage as a row-level write, with types converted between the two schemas.
DetectionStacksync polls Teradata Vantage for changes on an incremental schedule, reading only records changed since the previous pass. Query-based polling.
DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Teradata Vantage connection.
Changes in Databricks or Teradata Vantage instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Databricks or Teradata Vantage data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Databricks or Teradata Vantage record.
Track your Databricks ⇄ Teradata Vantage sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Databricks and Teradata Vantage.
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.
Authenticate Databricks and Teradata Vantage with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pick the Databricks and Teradata Vantage 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.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Teradata Vantage: authenticate both systems, choose the objects to sync (such as Databricks's Views and Delta Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Teradata Vantage: Query-based polling; the SQL surface exposes no externally consumable change log. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Databricks side: Views, Materialized Views, Volumes, SQL Warehouses, plus custom fields where Databricks exposes them. On the Teradata Vantage side: Tables, Views, Macros, Stored procedures. Stacksync auto-detects both schemas and converts types between the two systems.
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.
Common patterns for Databricks and Teradata Vantage: 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.
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. Teradata Vantage: ANSI SQL over JDBC/ODBC/.NET drivers; REST access available through Teradata's query service. Authentication: Database credentials; LDAP or Kerberos in enterprise deployments. Stacksync manages authentication, retries, and rate limits on both sides.
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.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 483 integrations available for Databricks and Teradata Vantage.