Two-way sync
Changes in AWS Aurora PostgreSQL or TimescaleDB instantly reflect in both systems. No stale data, no manual imports.
Keep AWS Aurora PostgreSQL and TimescaleDB 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 AWS Aurora PostgreSQL and TimescaleDB 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.
Mirror selected tables to another region or environment continuously, filtered to just the rows that should travel.
Keep the same dataset live in both AWS Aurora PostgreSQL and TimescaleDB, so each workload runs on the engine that suits it.
When one database is replacing the other, sync both directions during the transition and switch traffic when ready, without a freeze window.
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 | TimescaleDB objects | |
|---|---|---|
| Replication slots and publications The logical replication objects that power log-based CDC. | Schemas Postgres namespaces used to separate synced datasets by team or environment. | |
| Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. | Hypertables Time-partitioned tables that hold the main time-series data; the primary read and write target in syncs. | |
| Tables The core sync unit; rows are matched across systems by primary key. | Chunks Time-bounded partitions of a hypertable; syncs read and write through the parent hypertable and never address chunks directly. | |
| Rows Inserted, updated, and deleted in both directions during bi-directional syncs. | Continuous Aggregates Incrementally maintained rollups that serve as pre-aggregated read sources for downstream systems. | |
| Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. | Regular PostgreSQL Tables Relational reference data such as devices, tenants, or accounts synced alongside the series data. | |
| Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. | Views Standard SQL views used to shape or filter data for consumers. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora PostgreSQL–TimescaleDB connection.
Changes in AWS Aurora PostgreSQL or TimescaleDB instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever AWS Aurora PostgreSQL or TimescaleDB 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 Aurora PostgreSQL or TimescaleDB record.
Track your AWS Aurora PostgreSQL ⇄ TimescaleDB sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between AWS Aurora PostgreSQL and TimescaleDB.
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 Aurora PostgreSQL and TimescaleDB 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 Aurora PostgreSQL and TimescaleDB 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 Aurora PostgreSQL and TimescaleDB: authenticate both systems, choose the objects to sync (such as AWS Aurora PostgreSQL's Replication slots and publications and Databases and schemas), map fields visually, and changes propagate both ways in milliseconds — no code required.
AWS Aurora PostgreSQL: Aurora's storage layer replicates data six ways across three Availability Zones and is shared by up to 15 read replicas. TimescaleDB: TimescaleDB is packaged as a PostgreSQL extension, so standard Postgres drivers and SQL tooling work unchanged. Stacksync's field mapping accounts for these differences between AWS Aurora PostgreSQL and TimescaleDB without custom code.
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 TimescaleDB records are not retained after a sync operation.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed AWS Aurora PostgreSQL and TimescaleDB connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom AWS Aurora PostgreSQL–TimescaleDB integration in-house.
Yes — Stacksync ships production-grade connectors for both AWS Aurora PostgreSQL and TimescaleDB. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on AWS Aurora PostgreSQL: Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback. On TimescaleDB: Log-based capture via PostgreSQL logical decoding where the deployment allows it — hypertable changes surface on the underlying chunk tables and must be remapped to the parent — or timestamp-based polling on time columns; regular Postgres tables replicate through standard logical replication. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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 Aurora PostgreSQL and TimescaleDB.