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Data warehouse ⇄ Database

Apache Kylin to Google Cloud SQL integration — real-time data sync

Keep Apache Kylin and Google Cloud SQL 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 Apache Kylin and Google Cloud SQL

Flow Apache Kylin data into Google Cloud SQL in real time — no exports, no schedulers, no custom scripts.

Apache Kylin is a read-only source: Stacksync reads its data in real time and delivers it into Google Cloud SQL, so Google Cloud SQL always reflects the current state of Apache Kylin — without exports, scripts, or schedulers.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Google Cloud SQL's rows in Apache Kylin, 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 Google Cloud SQL where the services that read from it get them at normal query latency.

Common use cases

  • 01 Trigger downstream syncs after segment build jobs complete so consumers only read refreshed data.
  • 02 Read pre-aggregated metrics from Kylin and sync them into CRM fields or planning spreadsheets on a schedule.
  • 03 Migrate from a self-managed database by syncing Cloud SQL and the legacy system during cutover.
  • 04 Keep an internal admin application backed by Cloud SQL consistent with an ERP or billing system.

Common sync patterns

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 Kylin and keep Google Cloud SQL focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Google Cloud SQL land in Apache Kylin as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between Apache Kylin and Google Cloud SQL

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 Kylin objects Google Cloud SQL objects How this pairing syncs
Models Star-schema definitions over source tables that determine what can be queried. Rows Read and written by primary key during each sync cycle. Models is specific to Apache Kylin and Rows to Google Cloud SQL — each maps to any object or custom field on the other side.
Cubes / Indexes Pre-computed aggregate structures that answer queries at low latency. Views Read-only sources for shaping data before syncing it out. Cubes / Indexes is specific to Apache Kylin and Views to Google Cloud SQL — each maps to any object or custom field on the other side.
Source Tables Hive or other upstream tables that builds read from. Transaction logs MySQL binlog or PostgreSQL WAL, the source for log-based change capture. Source Tables is specific to Apache Kylin and Transaction logs to Google Cloud SQL — each maps to any object or custom field on the other side.
Segments Time-ranged build units that partition pre-computed data. Instances The managed MySQL, PostgreSQL, or SQL Server server a sync connects to. Segments is specific to Apache Kylin and Instances to Google Cloud SQL — each maps to any object or custom field on the other side.
Build Jobs Batch jobs that compute or refresh segments, monitored via the REST API. Databases Scope the tables included in a sync configuration. Build Jobs is specific to Apache Kylin and Databases to Google Cloud SQL — each maps to any object or custom field on the other side.
Projects Top-level workspaces that group models, tables, and jobs. Schemas Namespace tables in PostgreSQL and SQL Server instances. Projects is specific to Apache Kylin and Schemas to Google Cloud SQL — each maps to any object or custom field on the other side.

How changes propagate between Apache Kylin and Google Cloud SQL

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 Kylin Google Cloud SQL 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 Google Cloud SQL as a row-level write, with types converted between the two schemas.

Google Cloud SQL Apache Kylin Sub-second propagation

DetectionChanges in Google Cloud SQL are captured at the source via change data capture — no polling loop against its API. Engine-dependent log-based CDC: MySQL binlog, PostgreSQL logical replication, SQL Server change tracking.

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 Google Cloud SQL records.

Rate-limit considerations

  • Apache Kylin: No fixed API quotas; query capacity depends on the deployment and pre-computed index coverage.
  • Google Cloud SQL: Constrained by instance size and connection limits rather than API quotas.
What ships with Apache Kylin ⇄ Google Cloud SQL

Connect Apache Kylin and Google Cloud SQL for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Kylin–Google Cloud SQL connection.

Real-time

Real-time sync

Changes in Apache Kylin or Google Cloud SQL instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Kylin or Google Cloud SQL 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 Kylin or Google Cloud SQL record.

Observability

Monitoring

Track your Apache Kylin ⇄ Google Cloud SQL sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Kylin and Google Cloud SQL.

How the Apache Kylin and Google Cloud SQL connectors work

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

Google Cloud SQL

Integration surface
Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management
Authentication
Database credentials; IAM database authentication is available for MySQL and PostgreSQL
Change detection
Engine-dependent log-based CDC: MySQL binlog, PostgreSQL logical replication, SQL Server change tracking; polling as a fallback
Capabilities
read · write · CDC
Rate limits
Constrained by instance size and connection limits rather than API quotas.
How it works

How to connect Apache Kylin to Google Cloud SQL — 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 Kylin and Google Cloud SQL 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 Kylin connected
    Google Cloud SQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Kylin and Google Cloud SQL 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 Kylin ⇄ Google Cloud SQL
    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 Kylin Google Cloud SQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Kylin and Google Cloud SQL integration FAQ

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.

Learn more about security
SOC 2 Type II
ISO 27001
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GDPR
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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 323 integrations available for Apache Kylin and Google Cloud SQL.

Popular · 4 of 323
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