Two-way sync
Changes in Amazon RDS or Apache Druid instantly reflect in both systems. No stale data, no manual imports.
Keep Amazon RDS and Apache Druid 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 Amazon RDS'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 Amazon RDS where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Amazon RDS sync into Apache Druid in real time, and result tables in Apache Druid sync back into Amazon RDS, with schema and type mapping between the two systems handled for you.
Point analytical queries at the synced copy in Apache Druid and keep Amazon RDS focused on its operational workload.
Rows from Amazon RDS land in Apache Druid as they change, replacing hand-built CDC and batch extract jobs.
Aggregates or model outputs computed in Apache Druid sync into Amazon RDS, where whatever reads from that database gets them without querying the warehouse.
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 RDS objects | Apache Druid objects | |
|---|---|---|
| Schemas Namespaces within a database used to isolate synced tables. | Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | |
| Tables The core sync target; rows map to records in connected SaaS systems. | Lookups Key-value mappings joined at query time, refreshable from external systems. | |
| Views Read-side projections exposed to outbound syncs. | Tasks Batch ingestion and compaction jobs monitored during data loads. | |
| Columns Field-level mapping targets, typed per the underlying engine. | Datasources The table-like unit of storage and querying, the main target of reads and ingestion. | |
| Primary and Unique Keys Match keys for idempotent upserts. | Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | |
| Read Replicas Low-impact read endpoints often used as the source side of a sync. | Dimensions String and categorical columns used for filtering and grouping in synced queries. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon RDS–Apache Druid connection.
Changes in Amazon RDS or Apache Druid instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Amazon RDS or Apache Druid 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 RDS or Apache Druid record.
Track your Amazon RDS ⇄ Apache Druid sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Amazon RDS and Apache Druid.
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 RDS and Apache Druid 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 RDS and Apache Druid 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 RDS and Apache Druid: authenticate both systems, choose the objects to sync (such as Amazon RDS's Schemas and Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
Change detection on Amazon RDS: Engine-native log-based CDC: MySQL/MariaDB binlog, PostgreSQL logical replication, SQL Server CDC; enabled through RDS parameter groups, with polling as a fallback. On Apache Druid: Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates. 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 Amazon RDS side: Read Replicas, Stored Procedures, Databases, Schemas. 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 RDS and Apache Druid: Offload heavy reads; Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed. Point analytical queries at the synced copy in Apache Druid and keep Amazon RDS focused on its operational workload.
Amazon RDS: SQL wire protocol of the chosen engine (PostgreSQL, MySQL, MariaDB, SQL Server, Oracle). Authentication: Database credentials over SSL/TLS, or IAM database authentication on supported engines. Apache Druid: REST API (SQL over HTTP and native JSON queries); JDBC via Avatica. Authentication: Deployment-dependent: basic authentication or an authenticator extension; often fronted by a proxy. 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 386 integrations available for Amazon RDS and Apache Druid.