Real-time
Two-way sync
Changes in Apache Druid or AWS Aurora MySQL instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Apache Druid to AWS Aurora MySQL integration — real-time, two-way sync
Keep Apache Druid and AWS Aurora MySQL 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
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Apache Druid and AWS Aurora MySQL
Connect AWS Aurora MySQL and Apache Druid 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 MySQL'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 MySQL 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 MySQL sync into Apache Druid in real time, and result tables in Apache Druid sync back into AWS Aurora MySQL, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Feed operational records into Druid via batch ingestion so analysts get interactive slice-and-dice on fresh data.
- 02 Sync Druid query results into a warehouse to combine real-time aggregates with historical models.
- 03 Let operations teams edit records in a spreadsheet-style tool with changes written back to Aurora safely.
- 04 Give backend services read and write access to ERP or billing data by syncing it into Aurora tables the application already queries.
Common sync patterns
Offload heavy reads
Point analytical queries at the synced copy in Apache Druid and keep AWS Aurora MySQL focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from AWS Aurora MySQL land in Apache Druid as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Apache Druid sync into AWS Aurora MySQL, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Apache Druid and AWS Aurora MySQL
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 MySQL objects | How this pairing syncs | |
|---|---|---|---|
| Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. | Ingestion Supervisors is specific to Apache Druid and Foreign keys to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Lookups Key-value mappings joined at query time, refreshable from external systems. | Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. | Lookups is specific to Apache Druid and Stored procedures and triggers to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Databases (schemas) Logical namespaces that scope which tables a sync connection can see. | Tasks is specific to Apache Druid and Databases (schemas) to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Datasources The table-like unit of storage and querying, the main target of reads and ingestion. | Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. | Datasources is specific to Apache Druid and Tables to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. | Segments is specific to Apache Druid and Rows to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Dimensions String and categorical columns used for filtering and grouping in synced queries. | Columns MySQL data types are mapped to the paired system's field types during schema setup. | Dimensions is specific to Apache Druid and Columns to AWS Aurora MySQL — each maps to any object or custom field on the other side. |
How changes propagate between Apache Druid and AWS Aurora MySQL
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.
Apache Druid AWS Aurora MySQL Interval-based propagation
DetectionStacksync polls Apache Druid for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Druid through streaming or batch ingestion rather than row updates.
DeliveryEach detected change is applied to AWS Aurora MySQL as a row-level write, with types converted between the two schemas.
AWS Aurora MySQL Apache Druid Sub-second propagation
DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback.
DeliveryEach detected change is applied to Apache Druid as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Apache Druid: No fixed API quotas; query concurrency is bounded by broker and historical node capacity.
What ships with Apache Druid ⇄ AWS Aurora MySQL
Connect Apache Druid and AWS Aurora MySQL for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–AWS Aurora MySQL connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Druid or AWS Aurora MySQL 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 Apache Druid or AWS Aurora MySQL record.
Observability
Monitoring
Track your Apache Druid ⇄ AWS Aurora MySQL sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Apache Druid and AWS Aurora MySQL.
How the Apache Druid and AWS Aurora MySQL connectors work
Apache Druid
- Integration surface
- 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
- Change detection
- Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates
- Capabilities
- read · write
- Rate limits
- No fixed API quotas; query concurrency is bounded by broker and historical node capacity
AWS Aurora MySQL
- Integration surface
- SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
- Authentication
- Database credentials, optionally AWS IAM database authentication, over TLS
- Change detection
- Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback
- Capabilities
- read · write · CDC
How it works
How to connect Apache Druid to AWS Aurora MySQL — 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 Apache Druid and AWS Aurora MySQL with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Apache Druid
connected
AWS Aurora MySQL
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Druid and AWS Aurora MySQL 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 · Apache Druid ⇄ AWS Aurora MySQLCustomers 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.
Apache Druid AWS Aurora MySQLCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Druid and AWS Aurora MySQL integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Druid and AWS Aurora MySQL: authenticate both systems, choose the objects to sync (such as Apache Druid's Ingestion Supervisors and Lookups), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
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 Apache Druid and AWS Aurora MySQL records are not retained after a sync operation.
-
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Apache Druid and AWS Aurora MySQL connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Druid–AWS Aurora MySQL integration in-house.
-
Yes — Stacksync ships production-grade connectors for both Apache Druid and AWS Aurora MySQL. 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 MySQL: Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns 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: Ingestion Supervisors, Lookups, Tasks, Datasources, plus custom fields where Apache Druid exposes them. On the AWS Aurora MySQL side: Stored procedures and triggers, Databases (schemas), Tables, Rows. Stacksync auto-detects both schemas and converts types between the two systems.
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:
