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Data warehouse ⇄ Database

Amazon Redshift to Postgres Heroku integration — real-time, two-way sync

Keep Amazon Redshift and Postgres Heroku 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 Amazon Redshift and Postgres Heroku

Connect Postgres Heroku and Amazon Redshift 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 Postgres Heroku's rows in Amazon Redshift, 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 Postgres Heroku where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Postgres Heroku sync into Amazon Redshift in real time, and result tables in Amazon Redshift sync back into Postgres Heroku, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Centralize CRM, ERP, and product data in Redshift so analysts join it with warehouse tables.
  • 02 Publish finance rollups computed in Redshift back to spreadsheets or operational tools.
  • 03 Expose CRM objects as Postgres tables the Heroku application can query and join directly
  • 04 Sync Heroku Postgres into a warehouse for reporting without running ETL dynos

Common sync patterns

Offload heavy reads

Point analytical queries at the synced copy in Amazon Redshift and keep Postgres Heroku focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Postgres Heroku land in Amazon Redshift as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in Amazon Redshift sync into Postgres Heroku, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Amazon Redshift and Postgres Heroku

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.

Amazon Redshift objects Postgres Heroku objects How this pairing syncs
Schemas Namespaces used to organize synced tables and control grants. Schemas Namespaces that scope which tables a sync reads and writes. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Tables Columnar tables used as sync destinations for SaaS and database data. Tables Standard Postgres tables; the primary two-way sync target for app data. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views SQL views readable as modeled sources for reverse syncs. Views Read-side projections exposed to outbound syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Materialized Views Precomputed results that downstream syncs can read for performance. Materialized Views Precomputed result sets synced outward on refresh. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
External Tables (Spectrum) S3-backed tables queryable through Redshift, readable in syncs. Primary and Unique Keys Match keys for idempotent upserts from connected systems. External Tables (Spectrum) is specific to Amazon Redshift and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side.
Stored Procedures SQL procedures sometimes invoked around load steps. JSONB Columns Semi-structured payloads for nested SaaS objects and metadata. Stored Procedures is specific to Amazon Redshift and JSONB Columns to Postgres Heroku — each maps to any object or custom field on the other side.

How changes propagate between Amazon Redshift and Postgres Heroku

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.

Amazon Redshift Postgres Heroku Interval-based propagation

DetectionStacksync polls Amazon Redshift for changes on an incremental schedule, reading only records changed since the previous pass. Polling or query-based diffing.

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

Postgres Heroku Amazon Redshift Interval-based propagation

DetectionStacksync polls Postgres Heroku for changes on an incremental schedule, reading only records changed since the previous pass. Trigger-based capture or polling in most configurations.

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

Rate-limit considerations

  • Amazon Redshift: Bounded by cluster or serverless capacity and concurrency settings rather than API quotas.
  • Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Amazon Redshift ⇄ Postgres Heroku

Connect Amazon Redshift and Postgres Heroku for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon Redshift–Postgres Heroku connection.

Real-time

Two-way sync

Changes in Amazon Redshift or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Redshift or Postgres Heroku 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 Amazon Redshift or Postgres Heroku record.

Observability

Monitoring

Track your Amazon Redshift ⇄ Postgres Heroku sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Redshift and Postgres Heroku.

How the Amazon Redshift and Postgres Heroku connectors work

Amazon Redshift

Integration surface
SQL over JDBC/ODBC (PostgreSQL-derived protocol); Redshift Data API over HTTPS
Authentication
Database credentials or IAM-based authentication
Change detection
Polling or query-based diffing; Redshift does not expose a transaction log for external CDC consumers
Capabilities
read · write
Rate limits
Bounded by cluster or serverless capacity and concurrency settings rather than API quotas

Postgres Heroku

Integration surface
SQL wire protocol (standard PostgreSQL)
Authentication
Database credentials from the Heroku DATABASE_URL config var; SSL required
Change detection
Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings
Capabilities
read · write
Rate limits
No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan
How it works

How to connect Amazon Redshift to Postgres Heroku — 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 Amazon Redshift and Postgres Heroku 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
    Amazon Redshift connected
    Postgres Heroku connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Redshift and Postgres Heroku 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 · Amazon Redshift ⇄ Postgres Heroku
    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
    Amazon Redshift Postgres Heroku
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Redshift and Postgres Heroku 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

Related integrations

Every pair below is a real-time, two-way sync. Search all 484 integrations available for Amazon Redshift and Postgres Heroku.

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