Two-way sync
Changes in Apache Druid or AWS Aurora PostgreSQL instantly reflect in both systems. No stale data, no manual imports.
Keep Apache Druid and AWS Aurora PostgreSQL in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Operational databases and analytical warehouses want the same data at different moments. Analysts want AWS Aurora PostgreSQL's rows in Apache Druid, 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 Apache Druid in real time, and result tables in Apache Druid sync back into AWS Aurora PostgreSQL, with schema and type mapping between the two systems handled for you.
Aggregates or model outputs computed in Apache Druid sync into AWS Aurora PostgreSQL, where whatever reads from that database gets them without querying the warehouse.
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Point analytical queries at the synced copy in Apache Druid and keep AWS Aurora PostgreSQL focused on its operational workload.
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.
| Apache Druid objects | AWS Aurora PostgreSQL objects | |
|---|---|---|
| Dimensions String and categorical columns used for filtering and grouping in synced queries. | Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. | |
| Metrics Numeric columns, often pre-aggregated at ingestion via rollup. | Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. | |
| Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. | |
| Lookups Key-value mappings joined at query time, refreshable from external systems. | Foreign keys Relationship metadata that syncs can translate into object references elsewhere. | |
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Replication slots and publications The logical replication objects that power log-based CDC. | |
| Datasources The table-like unit of storage and querying, the main target of reads and ingestion. | Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–AWS Aurora PostgreSQL connection.
Changes in Apache Druid or AWS Aurora PostgreSQL instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Apache Druid or AWS Aurora PostgreSQL data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Apache Druid or AWS Aurora PostgreSQL record.
Track your Apache Druid ⇄ AWS Aurora PostgreSQL sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Apache Druid and AWS Aurora PostgreSQL.
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 Apache Druid and AWS Aurora PostgreSQL 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 Apache Druid and AWS Aurora PostgreSQL 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 Apache Druid and AWS Aurora PostgreSQL: authenticate both systems, choose the objects to sync (such as Apache Druid's Dimensions and Metrics), map fields visually, and changes propagate both ways in milliseconds — no code required.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Apache Druid and AWS Aurora PostgreSQL connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Druid–AWS Aurora PostgreSQL integration in-house.
Yes — Stacksync ships production-grade connectors for both Apache Druid and AWS Aurora PostgreSQL. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Apache Druid: Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates. On AWS Aurora PostgreSQL: Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp 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 Apache Druid side: Segments, Dimensions, Metrics, Ingestion Supervisors, plus custom fields where Apache Druid exposes them. On the AWS Aurora PostgreSQL side: Columns, Primary keys and constraints, Views and materialized views, Foreign keys. 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.
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 Apache Druid and AWS Aurora PostgreSQL.