Two-way sync
Changes in AWS S3 or Yellowbrick instantly reflect in both systems. No stale data, no manual imports.
Keep AWS S3 and Yellowbrick 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 AWS S3 and Yellowbrick 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.
Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.
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.
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.
| AWS S3 objects | Yellowbrick objects | |
|---|---|---|
| Buckets Top-level containers a sync targets; region and policy are set at this level. | Tables Columnar MPP tables; the primary targets for warehouse syncs. | |
| Objects The stored files (CSV, JSON, Parquet); syncs read them as datasets or write exports into them. | Views Logical views used to shape reads for BI and downstream syncs. | |
| Prefixes Key-name paths used to partition synced datasets, since S3 has no real directories. | Users and Roles Access-control objects that govern what a sync service account can read and write. | |
| Object Metadata System and user-defined metadata read alongside object contents. | Databases Top-level containers for schemas and tables. | |
| Object Versions Prior copies retained when versioning is enabled, relevant for reprocessing. | Schemas Namespaces used to organize synced datasets by source or domain. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS S3–Yellowbrick connection.
Changes in AWS S3 or Yellowbrick instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever AWS S3 or Yellowbrick data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single AWS S3 or Yellowbrick record.
Track your AWS S3 ⇄ Yellowbrick sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between AWS S3 and Yellowbrick.
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 AWS S3 and Yellowbrick 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 AWS S3 and Yellowbrick 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 AWS S3 and Yellowbrick: authenticate both systems, choose the objects to sync (such as AWS S3's Buckets and Objects), map fields visually, and changes propagate both ways in milliseconds — no code required.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed AWS S3 and Yellowbrick connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom AWS S3–Yellowbrick integration in-house.
Yes — Stacksync ships production-grade connectors for both AWS S3 and Yellowbrick. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on AWS S3: S3 Event Notifications on object create/delete delivered to SQS, SNS, Lambda, or EventBridge; list-based polling as a fallback. On Yellowbrick: Polling on timestamp columns; no exposed transaction-log CDC. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the AWS S3 side: Multipart Uploads, Buckets, Objects, Prefixes, plus custom fields where AWS S3 exposes them. On the Yellowbrick side: Views, Users and Roles, Databases, Schemas. 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.
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 386 integrations available for AWS S3 and Yellowbrick.