Real-time
Real-time sync
Changes in Apache Druid or Apache Kylin instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Apache Druid to Apache Kylin integration — real-time data sync
Keep Apache Druid and Apache Kylin 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 Apache Kylin
Keep tables consistent across Apache Druid and Apache Kylin, for a migration, a multi-warehouse stack, or a dataset two platforms both need.
Apache Kylin is a read-only source: Stacksync reads its data in real time and delivers it into Apache Druid, so Apache Druid always reflects the current state of Apache Kylin — without exports, scripts, or schedulers.
Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.
Common use cases
- 01 Query aggregated event metrics from Druid and sync them into CRM account fields for usage-based selling.
- 02 Feed operational records into Druid via batch ingestion so analysts get interactive slice-and-dice on fresh data.
- 03 Trigger downstream syncs after segment build jobs complete so consumers only read refreshed data.
- 04 Read pre-aggregated metrics from Kylin and sync them into CRM fields or planning spreadsheets on a schedule.
Common sync patterns
Shared datasets across teams
Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
Consolidation after M&A
Bring the acquired company's warehouse data across continuously instead of through one-off dumps.
Migration without a big bang
When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
What you can sync between Apache Druid and Apache Kylin
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 | Apache Kylin objects | How this pairing syncs | |
|---|---|---|---|
| Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | Segments Time-ranged build units that partition pre-computed data. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in the writable direction. | |
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Models Star-schema definitions over source tables that determine what can be queried. | Tasks is specific to Apache Druid and Models to Apache Kylin — 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. | Cubes / Indexes Pre-computed aggregate structures that answer queries at low latency. | Datasources is specific to Apache Druid and Cubes / Indexes to Apache Kylin — 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. | Source Tables Hive or other upstream tables that builds read from. | Dimensions is specific to Apache Druid and Source Tables to Apache Kylin — each maps to any object or custom field on the other side. | |
| Metrics Numeric columns, often pre-aggregated at ingestion via rollup. | Build Jobs Batch jobs that compute or refresh segments, monitored via the REST API. | Metrics is specific to Apache Druid and Build Jobs to Apache Kylin — each maps to any object or custom field on the other side. | |
| Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | Projects Top-level workspaces that group models, tables, and jobs. | Ingestion Supervisors is specific to Apache Druid and Projects to Apache Kylin — each maps to any object or custom field on the other side. |
How changes propagate between Apache Druid and Apache Kylin
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 Apache Kylin 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.
DeliveryApache Kylin does not accept inbound record writes, so this direction carries requests rather than records: Apache Kylin's output flows back as field updates on the originating Apache Druid records.
Apache Kylin Apache Druid Interval-based propagation
DetectionStacksync polls Apache Kylin for changes on an incremental schedule, reading only records changed since the previous pass. Data freshness follows segment build and refresh jobs, so integrations poll query results.
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.
- Apache Kylin: No fixed API quotas; query capacity depends on the deployment and pre-computed index coverage.
What ships with Apache Druid ⇄ Apache Kylin
Connect Apache Druid and Apache Kylin for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Apache Kylin connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Druid or Apache Kylin 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 Apache Kylin record.
Observability
Monitoring
Track your Apache Druid ⇄ Apache Kylin 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 Apache Kylin.
How the Apache Druid and Apache Kylin 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
Apache Kylin
- Integration surface
- SQL over JDBC/ODBC plus a REST API for queries and administration
- Authentication
- Username/password (HTTP basic authentication on the REST API)
- Change detection
- Not applicable for row-level capture; data freshness follows segment build and refresh jobs, so integrations poll query results
- Capabilities
- read
- Rate limits
- No fixed API quotas; query capacity depends on the deployment and pre-computed index coverage
How it works
How to connect Apache Druid to Apache Kylin — 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 Apache Kylin 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
Apache Kylin
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Druid and Apache Kylin 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 ⇄ Apache KylinCustomers 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 Apache KylinCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Druid and Apache Kylin integration FAQ
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Yes. Stacksync provides a managed, real-time integration between Apache Druid and Apache Kylin — Apache Kylin is a read-only source, so data flows from it into the other system: authenticate both systems, choose the objects to sync, map fields visually, and changes propagate in milliseconds — no code required.
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Apache Kylin is a read-only source, so this integration runs one-way: Stacksync reads from Apache Kylin in real time and delivers into Apache Druid. Field mapping and monitoring work the same as for two-way pairs.
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Common patterns for Apache Druid and Apache Kylin: Shared datasets across teams; Consolidation after M&A; Migration without a big bang. Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
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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. Apache Kylin: SQL over JDBC/ODBC plus a REST API for queries and administration. Authentication: Username/password (HTTP basic authentication on the REST API). Stacksync manages authentication, retries, and rate limits on both sides.
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Apache Druid: Druid stores data in immutable, time-partitioned segments; there is no row-level update path, so writes happen through ingestion and reprocessing rather than upserts. Apache Kylin: It exposes both ANSI-SQL access over JDBC/ODBC and a REST API used for querying and for managing models, segments, and jobs. Stacksync's field mapping accounts for these differences between Apache Druid and Apache Kylin without custom code.
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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 Apache Kylin records are not retained after a sync operation.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 328 integrations available for Apache Druid and Apache Kylin.
Popular · 3 of 328
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