Two-way sync
Changes in Amazon Aurora or IBM Informix instantly reflect in both systems. No stale data, no manual imports.
Keep Amazon Aurora and IBM Informix in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Two databases that must agree is one of the oldest problems in engineering: different engines for different workloads, separate services with overlapping reference data, a migration in flight, or regional instances that share a subset of records. Hand-rolled replication across systems means change capture, conflict handling, and type mapping, all built and maintained by your team.
Stacksync syncs tables or collections between Amazon Aurora and IBM Informix continuously and bi-directionally, translating types between the two engines and resolving conflicts by rules you configure. Rows written on either side appear on the other within seconds.
Services that own separate databases stay consistent on the records they share, without a custom replication layer.
Mirror selected tables to another region or environment continuously, filtered to just the rows that should travel.
Keep the same dataset live in both Amazon Aurora and IBM Informix, so each workload runs on the engine that suits it.
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 Aurora objects | IBM Informix objects | How this pairing syncs | |
|---|---|---|---|
| Databases Logical databases within a cluster that scope a sync connection. | Databases Top-level containers that scope a sync connection. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Tables Relational tables synced bi-directionally at row level. | Tables Relational tables mapped directly to sync targets. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Views Read-only query-backed sources for downstream syncs. | Views Read-only projections used to shape outbound data. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. | TimeSeries objects Informix's native time-series type, usually exposed to syncs through virtual tables. | Primary and Foreign Keys is specific to Amazon Aurora and TimeSeries objects to IBM Informix — each maps to any object or custom field on the other side. | |
| Read Replicas Reader endpoints that syncs can target to keep load off the writer. | Stored procedures Server-side logic sometimes invoked as part of write paths. | Read Replicas is specific to Amazon Aurora and Stored procedures to IBM Informix — each maps to any object or custom field on the other side. | |
| Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. | Logical logs The transaction log that Informix's CDC interface reads committed changes from. | Schemas is specific to Amazon Aurora and Logical logs to IBM Informix — 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 Amazon Aurora are captured at the source via change data capture — no polling loop against its API. Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters.
DeliveryEach detected change is applied to IBM Informix as a row-level write, with types converted between the two schemas.
DetectionChanges in IBM Informix are captured at the source via change data capture — no polling loop against its API. Informix's Change Data Capture API reading committed changes from logical logs.
DeliveryEach detected change is applied to Amazon Aurora as a row-level write, with types converted between the two schemas.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon Aurora–IBM Informix connection.
Changes in Amazon Aurora or IBM Informix instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Amazon Aurora or IBM Informix data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Amazon Aurora or IBM Informix record.
Track your Amazon Aurora ⇄ IBM Informix sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Amazon Aurora and IBM Informix.
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 Amazon Aurora and IBM Informix 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 Amazon Aurora and IBM Informix 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 Amazon Aurora and IBM Informix: authenticate both systems, choose the objects to sync (such as Amazon Aurora's Databases and Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
Change detection on Amazon Aurora: Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback. On IBM Informix: Informix's Change Data Capture API reading committed changes from logical logs; polling as a fallback. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Amazon Aurora side: Databases, Schemas, Tables, Views, plus custom fields where Amazon Aurora exposes them. On the IBM Informix side: Databases, Tables, Rows, Views. 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 Amazon Aurora and IBM Informix: Shared reference data between services; Regional or environment copies; Cross-engine sync. Services that own separate databases stay consistent on the records they share, without a custom replication layer.
Amazon Aurora: MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS. Authentication: Database credentials or IAM database authentication. IBM Informix: SQL over JDBC/ODBC drivers; DRDA connectivity is also supported. Authentication: Database credentials. 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 392 integrations available for Amazon Aurora and IBM Informix.