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Data warehouse ⇄ Database

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

Keep Apache Druid and 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 MySQL

Connect 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 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 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 MySQL sync into Apache Druid in real time, and result tables in Apache Druid sync back into 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.
  • Two-way sync between a MySQL application database and a CRM so operational records and sales records stay identical
  • Expose SaaS objects as MySQL tables so legacy internal tools built on MySQL can read live business data

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

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

Operational data in the warehouse, minus the pipeline

Rows from MySQL land in Apache Druid as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between Apache Druid and 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 MySQL objects
Metrics Numeric columns, often pre-aggregated at ingestion via rollup. Primary and Unique Keys Match keys for idempotent upserts and conflict handling.
Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. JSON Columns Validated semi-structured payloads for nested SaaS data.
Lookups Key-value mappings joined at query time, refreshable from external systems. Stored Procedures Server-side logic that can post-process synced rows.
Tasks Batch ingestion and compaction jobs monitored during data loads. Triggers An alternative change-capture mechanism when binlog access is unavailable.
Datasources The table-like unit of storage and querying, the main target of reads and ingestion. Databases (Schemas) Top-level namespaces that scope a sync's reads and writes.
Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. Tables The primary sync target; rows map to records in connected systems.
What ships with Apache Druid ⇄ MySQL

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

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

Real-time

Two-way sync

Changes in Apache Druid or 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 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 MySQL record.

Observability

Monitoring

Track your Apache Druid ⇄ 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 MySQL.

How the Apache Druid and 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

MySQL

Integration surface
SQL wire protocol (MySQL client/server protocol)
Authentication
Database credentials entered as a connection string or parameters, with optional SSL root certificate upload and optional SSH tunnel (SSH user + SSH host)
Change detection
Database triggers — Stacksync creates deterministic triggers for internal logging and syncing (requires log_bin_trust_function_creators=ON when binary logging is enabled)
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits and server resources
MySQL setup guide
How it works

How to connect Apache Druid to 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 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
    MySQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Druid and 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 ⇄ 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 MySQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Druid and 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 MySQL.

Popular · 8 of 386
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