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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.

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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

  • Feed operational records into Druid via batch ingestion so analysts get interactive slice-and-dice on fresh data.
  • Sync Druid query results into a warehouse to combine real-time aggregates with historical models.
  • Let operations teams edit records in a spreadsheet-style tool with changes written back to Aurora safely.
  • Give backend services read and write access to ERP or billing data by syncing it into Aurora tables the application already queries.

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
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.
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.
Tasks Batch ingestion and compaction jobs monitored during data loads. Databases (schemas) Logical namespaces that scope which tables a sync connection can see.
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.
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.
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.
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.

Real-time

Two-way sync

Changes in Apache Druid or AWS Aurora MySQL instantly reflect in both systems. No stale data, no manual imports.

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.

  1. 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Apache Druid connected
    AWS Aurora MySQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Apache Druid ⇄ AWS Aurora MySQL
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Apache Druid AWS Aurora MySQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Druid and AWS Aurora MySQL integration FAQ

SECURITY

Security teams love 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.

Learn more about security
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 386 integrations available for Apache Druid and AWS Aurora MySQL.

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