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Database ⇄ Data warehouse

Amazon Aurora to Apache Druid integration — real-time, two-way sync

Keep Amazon Aurora 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.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon Aurora and Apache Druid

Connect Amazon Aurora 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 Amazon Aurora'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 Aurora 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 Aurora sync into Apache Druid in real time, and result tables in Apache Druid sync back into Amazon Aurora, with schema and type mapping between the two systems handled for you.

Common use cases

  • Sync Druid query results into a warehouse to combine real-time aggregates with historical models.
  • Keep lookup tables in Druid refreshed from a CRM or database so query-time joins use current reference data.
  • Write enriched or scored records from analytics pipelines back into the Aurora tables that power an application.
  • Offload sync reads to Aurora reader endpoints to avoid load on the writer instance.

Offload heavy reads

Point analytical queries at the synced copy in Apache Druid and keep Amazon Aurora focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Amazon Aurora 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 Amazon Aurora, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Amazon Aurora and Apache Druid

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 Aurora objects Apache Druid objects
Views Read-only query-backed sources for downstream syncs. Datasources The table-like unit of storage and querying, the main target of reads and ingestion.
Materialized Views Precomputed result sets (PostgreSQL-compatible clusters) readable as sources. Segments Time-partitioned immutable files that hold datasource data; ingestion produces them.
Columns and Data Types Standard MySQL or PostgreSQL types mapped during field mapping. Dimensions String and categorical columns used for filtering and grouping in synced queries.
Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. Metrics Numeric columns, often pre-aggregated at ingestion via rollup.
Read Replicas Reader endpoints that syncs can target to keep load off the writer. Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid.
Databases Logical databases within a cluster that scope a sync connection. Lookups Key-value mappings joined at query time, refreshable from external systems.
What ships with Amazon Aurora ⇄ Apache Druid

Connect Amazon Aurora and Apache Druid for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon Aurora–Apache Druid connection.

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora or Apache Druid 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 Amazon Aurora or Apache Druid record.

Observability

Monitoring

Track your Amazon Aurora ⇄ Apache Druid sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Aurora and Apache Druid.

How the Amazon Aurora and Apache Druid connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits

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
How it works

How to connect Amazon Aurora to Apache Druid — 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 Amazon Aurora 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Amazon Aurora connected
    Apache Druid connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Aurora 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Amazon Aurora ⇄ Apache Druid
    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
    Amazon Aurora Apache Druid
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Aurora and Apache Druid 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 Amazon Aurora and Apache Druid.

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