Real-time
Two-way sync
Changes in AWS Aurora PostgreSQL or Greenplum instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
AWS Aurora PostgreSQL to Greenplum integration — real-time, two-way sync
Keep AWS Aurora PostgreSQL and Greenplum 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
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Migrated from MuleSoft
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Migrated from Matillion
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect AWS Aurora PostgreSQL and Greenplum
Connect AWS Aurora PostgreSQL and Greenplum 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 Greenplum, 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 Greenplum in real time, and result tables in Greenplum sync back into AWS Aurora PostgreSQL, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Feed BI reporting by syncing curated Greenplum views to downstream tools on a schedule.
- 02 Consolidate data from multiple source systems into partitioned Greenplum fact tables.
- 03 Feed operational dashboards from a read replica while the writer handles sync traffic.
- 04 Expose ERP records such as customers, orders, and invoices as Postgres tables the engineering team can query and update with plain SQL.
Common sync patterns
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 Greenplum and keep AWS Aurora PostgreSQL focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from AWS Aurora PostgreSQL land in Greenplum as they change, replacing hand-built CDC and batch extract jobs.
What you can sync between AWS Aurora PostgreSQL and Greenplum
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 | Greenplum objects | How this pairing syncs | |
|---|---|---|---|
| Tables The core sync unit; rows are matched across systems by primary key. | Tables Heap or append-optimized tables mapped directly to sync targets. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Rows Inserted, updated, and deleted in both directions during bi-directional syncs. | Rows Read and written by key; distribution keys determine where rows live. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. | Databases Top-level containers that scope a sync connection. | Columns is specific to AWS Aurora PostgreSQL and Databases to Greenplum — each maps to any object or custom field on the other side. | |
| Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. | Schemas Namespace tables and control which objects a sync can see. | Primary keys and constraints is specific to AWS Aurora PostgreSQL and Schemas to Greenplum — each maps to any object or custom field on the other side. | |
| Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. | Partitions Large tables are commonly partitioned by date, which shapes incremental reads. | Views and materialized views is specific to AWS Aurora PostgreSQL and Partitions to Greenplum — each maps to any object or custom field on the other side. | |
| Foreign keys Relationship metadata that syncs can translate into object references elsewhere. | Views Read-only projections used to shape data before syncing it out. | Foreign keys is specific to AWS Aurora PostgreSQL and Views to Greenplum — each maps to any object or custom field on the other side. |
How changes propagate between AWS Aurora PostgreSQL and Greenplum
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 Greenplum 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 Greenplum as a row-level write, with types converted between the two schemas.
Greenplum AWS Aurora PostgreSQL Interval-based propagation
DetectionStacksync polls Greenplum for changes on an incremental schedule, reading only records changed since the previous pass. Polling with timestamp or key-based cursors.
DeliveryEach detected change is applied to AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Greenplum: Bounded by cluster resources and concurrency settings rather than an API quota.
What ships with AWS Aurora PostgreSQL ⇄ Greenplum
Connect AWS Aurora PostgreSQL and Greenplum for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora PostgreSQL–Greenplum connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever AWS Aurora PostgreSQL or Greenplum 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 Greenplum record.
Observability
Monitoring
Track your AWS Aurora PostgreSQL ⇄ Greenplum 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 Greenplum.
How the AWS Aurora PostgreSQL and Greenplum 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
Greenplum
- Integration surface
- PostgreSQL wire protocol (libpq), plus JDBC/ODBC drivers
- Authentication
- Database credentials
- Change detection
- Polling with timestamp or key-based cursors; Greenplum does not expose logical-decoding CDC
- Capabilities
- read · write
- Rate limits
- Bounded by cluster resources and concurrency settings rather than an API quota.
How it works
How to connect AWS Aurora PostgreSQL to Greenplum — 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 AWS Aurora PostgreSQL and Greenplum with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
AWS Aurora PostgreSQL
connected
Greenplum
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the AWS Aurora PostgreSQL and Greenplum 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 · AWS Aurora PostgreSQL ⇄ GreenplumCustomers 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.
AWS Aurora PostgreSQL GreenplumCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
AWS Aurora PostgreSQL and Greenplum integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between AWS Aurora PostgreSQL and Greenplum: authenticate both systems, choose the objects to sync (such as AWS Aurora PostgreSQL's Tables and Rows), map fields visually, and changes propagate both ways in milliseconds — no code required.
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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 AWS Aurora PostgreSQL and Greenplum: Fresh analytics without loading windows; Offload heavy reads; Operational data in the warehouse, minus the pipeline. Because changes stream continuously, analysts query current data instead of waiting for last night's load.
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AWS Aurora PostgreSQL: SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC. Authentication: Database credentials, optionally AWS IAM database authentication, over TLS. Greenplum: PostgreSQL wire protocol (libpq), plus JDBC/ODBC drivers. Authentication: Database credentials. Stacksync manages authentication, retries, and rate limits on both sides.
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Greenplum: Rows are distributed across segment hosts by a per-table distribution key; joins that co-locate on that key avoid cross-segment data motion. AWS Aurora PostgreSQL: Replication slots retain WAL for their consumers, so an interrupted CDC sync can resume without losing changes. Stacksync's field mapping accounts for these differences between AWS Aurora PostgreSQL and Greenplum without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means AWS Aurora PostgreSQL and Greenplum records are not retained after a sync operation.
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:
