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Database

Google Cloud SQL to MarkLogic integration — real-time, two-way sync

Keep Google Cloud SQL and MarkLogic 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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  • POC with real engineers in minutes

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

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Why teams connect Google Cloud SQL and MarkLogic

Keep Google Cloud SQL and MarkLogic synchronized in real time, across engines, regions, or services, in one or both directions.

Two databases that must agree is one of the oldest problems in engineering: different engines for different workloads, separate services with overlapping reference data, a migration in flight, or regional instances that share a subset of records. Hand-rolled replication across systems means change capture, conflict handling, and type mapping, all built and maintained by your team.

Stacksync syncs tables or collections between Google Cloud SQL and MarkLogic continuously and bi-directionally, translating types between the two engines and resolving conflicts by rules you configure. Rows written on either side appear on the other within seconds.

Common use cases

  • 01 Keep an internal admin application backed by Cloud SQL consistent with an ERP or billing system.
  • 02 Migrate from a self-managed database by syncing Cloud SQL and the legacy system during cutover.
  • 03 Write updates from operational systems back into the document hub to keep the canonical record current.
  • 04 Keep reference datasets and semantically linked entities aligned across downstream applications.

Common sync patterns

Cross-engine sync

Keep the same dataset live in both Google Cloud SQL and MarkLogic, so each workload runs on the engine that suits it.

Migration with zero-downtime cutover

When one database is replacing the other, sync both directions during the transition and switch traffic when ready, without a freeze window.

Shared reference data between services

Services that own separate databases stay consistent on the records they share, without a custom replication layer.

What you can sync between Google Cloud SQL and MarkLogic

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.

Google Cloud SQL objects MarkLogic objects How this pairing syncs
Instances The managed MySQL, PostgreSQL, or SQL Server server a sync connects to. Documents JSON and XML documents, the primary records read from and written to the database. Instances is specific to Google Cloud SQL and Documents to MarkLogic — each maps to any object or custom field on the other side.
Databases Scope the tables included in a sync configuration. Collections Named groupings used to scope which documents a sync reads or updates. Databases is specific to Google Cloud SQL and Collections to MarkLogic — each maps to any object or custom field on the other side.
Schemas Namespace tables in PostgreSQL and SQL Server instances. Semantic Triples RDF data stored alongside documents, queryable with SPARQL for linked-data syncs. Schemas is specific to Google Cloud SQL and Semantic Triples to MarkLogic — each maps to any object or custom field on the other side.
Tables Mapped directly to sync targets; schema changes can be propagated. TDE Views Relational projections of documents that let syncs read document data as SQL rows. Tables is specific to Google Cloud SQL and TDE Views to MarkLogic — each maps to any object or custom field on the other side.
Rows Read and written by primary key during each sync cycle. Document Metadata & Properties Permissions, quality, and property fragments carried with each document. Rows is specific to Google Cloud SQL and Document Metadata & Properties to MarkLogic — each maps to any object or custom field on the other side.
Views Read-only sources for shaping data before syncing it out. Databases & Forests Storage units that define the scope and placement of synced content. Views is specific to Google Cloud SQL and Databases & Forests to MarkLogic — each maps to any object or custom field on the other side.

How changes propagate between Google Cloud SQL and MarkLogic

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.

Google Cloud SQL MarkLogic 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.

DeliveryEach detected change is applied to MarkLogic as a row-level write, with types converted between the two schemas.

MarkLogic Google Cloud SQL Interval-based propagation

DetectionStacksync polls MarkLogic for changes on an incremental schedule, reading only records changed since the previous pass. No exposed transaction log.

DeliveryEach detected change is applied to Google Cloud SQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Google Cloud SQL: Constrained by instance size and connection limits rather than API quotas.
What ships with Google Cloud SQL ⇄ MarkLogic

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Google Cloud SQL or MarkLogic 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 Google Cloud SQL or MarkLogic record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Google Cloud SQL and MarkLogic connectors work

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.

MarkLogic

Integration surface
REST API (Client API), plus SQL/ODBC access over TDE views and Java/Node client libraries
Authentication
Username/password (digest or basic), with certificate-based options
Change detection
No exposed transaction log; polling on document timestamps/metadata, or server-side triggers that record changes for pickup
Capabilities
read · write
How it works

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

    Choose tables

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

Google Cloud SQL and MarkLogic 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
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 392 integrations available for Google Cloud SQL and MarkLogic.

Popular · 8 of 392
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