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Database ⇄ Data warehouse

AWS Aurora PostgreSQL to Yellowbrick integration — real-time, two-way sync

Keep AWS Aurora PostgreSQL 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.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect AWS Aurora PostgreSQL and Yellowbrick

Connect AWS Aurora PostgreSQL and Yellowbrick with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want AWS Aurora PostgreSQL's rows in Yellowbrick, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in AWS Aurora PostgreSQL where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in AWS Aurora PostgreSQL sync into Yellowbrick in real time, and result tables in Yellowbrick sync back into AWS Aurora PostgreSQL, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Sync CRM and marketing data into Yellowbrick tables so it can be joined with large fact tables for enterprise BI.
  • 02 Land ERP transactional extracts in Yellowbrick for finance and supply-chain analytics.
  • 03 Expose ERP records such as customers, orders, and invoices as Postgres tables the engineering team can query and update with plain SQL.
  • 04 Capture row-level changes with logical replication and propagate them to SaaS tools without batch jobs.

Common sync patterns

Serve warehouse results at database speed

Aggregates or model outputs computed in Yellowbrick sync into AWS Aurora PostgreSQL, where whatever reads from that database gets them without querying the warehouse.

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

Point analytical queries at the synced copy in Yellowbrick and keep AWS Aurora PostgreSQL focused on its operational workload.

What you can sync between AWS Aurora PostgreSQL and Yellowbrick

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 Aurora PostgreSQL objects Yellowbrick objects How this pairing syncs
Tables The core sync unit; rows are matched across systems by primary key. Tables Columnar MPP tables; the primary targets for warehouse syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Foreign keys Relationship metadata that syncs can translate into object references elsewhere. Databases Top-level containers for schemas and tables. Foreign keys is specific to AWS Aurora PostgreSQL and Databases to Yellowbrick — each maps to any object or custom field on the other side.
Replication slots and publications The logical replication objects that power log-based CDC. Schemas Namespaces used to organize synced datasets by source or domain. Replication slots and publications is specific to AWS Aurora PostgreSQL and Schemas to Yellowbrick — each maps to any object or custom field on the other side.
Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. Views Logical views used to shape reads for BI and downstream syncs. Databases and schemas is specific to AWS Aurora PostgreSQL and Views to Yellowbrick — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted in both directions during bi-directional syncs. Users and Roles Access-control objects that govern what a sync service account can read and write. Rows is specific to AWS Aurora PostgreSQL and Users and Roles to Yellowbrick — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora PostgreSQL and Yellowbrick

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.

AWS Aurora PostgreSQL Yellowbrick Sub-second propagation

DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.

DeliveryEach detected change is applied to Yellowbrick as a row-level write, with types converted between the two schemas.

Yellowbrick AWS Aurora PostgreSQL Interval-based propagation

DetectionStacksync polls Yellowbrick for changes on an incremental schedule, reading only records changed since the previous pass. Polling on timestamp columns.

DeliveryEach detected change is applied to AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Yellowbrick: No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
What ships with AWS Aurora PostgreSQL ⇄ Yellowbrick

Connect AWS Aurora PostgreSQL and Yellowbrick for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora PostgreSQL–Yellowbrick connection.

Real-time

Two-way sync

Changes in AWS Aurora PostgreSQL or Yellowbrick instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora PostgreSQL or Yellowbrick 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 AWS Aurora PostgreSQL or Yellowbrick record.

Observability

Monitoring

Track your AWS Aurora PostgreSQL ⇄ Yellowbrick sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS Aurora PostgreSQL and Yellowbrick.

How the AWS Aurora PostgreSQL and Yellowbrick connectors work

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
Capabilities
read · write · CDC

Yellowbrick

Integration surface
SQL wire protocol (PostgreSQL-compatible) with JDBC/ODBC drivers; bulk loading via the ybload utility
Authentication
Database credentials, with LDAP and Kerberos options in enterprise deployments
Change detection
Polling on timestamp columns; no exposed transaction-log CDC
Capabilities
read · write
Rate limits
No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
How it works

How to connect AWS Aurora PostgreSQL to Yellowbrick — 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.

  1. 01

    Connect your apps

    Authenticate AWS Aurora PostgreSQL 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    AWS Aurora PostgreSQL connected
    Yellowbrick connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora PostgreSQL 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · AWS Aurora PostgreSQL ⇄ Yellowbrick
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    AWS Aurora PostgreSQL Yellowbrick
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora PostgreSQL and Yellowbrick integration FAQ

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.

Learn more about security
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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more
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