Gluesync Platform — Integration

Integration

Real-time data movement, transformation and synthetic data generation across heterogeneous systems.

Explore Integration Talk to our team

Batch ETL is creating invisible data lag right now. Gluesync CDC versus 4-hour stale windows.

Why Integration Matters

The cost of fragmented data movement

01 / Fragmented channels

Point-to-Point Pipelines

Data teams manually maintain separate scripts, triggers, and scheduled tasks for every database pairing, causing siloed configurations.

02 / System overhead

Operational Strain

Latency spikes, query locks, and schema evolution break downstream consumers, consuming engineering hours on constant repair pipelines.

03 / The platform outcome

Coordinated Capabilities

A single engine handles real-time change data capture, structural automated transformations, and safe synthetic generation together.

Capabilities

Three capabilities within Integration

Real-time data movement, transformation and synthetic data generation across heterogeneous systems.

Change data capture in action

Replication

Continuous change capture and bulk synchronization

Capture and replicate data changes across databases using log-based CDC, fast bulk loads, and flexible field mapping — with zero custom scripting required.

Capture delta changes with Log-based change data capture (CDC)
Fast bulk data loads
No-code field mapping & transformations
User-defined transformation via code editor

View Replication docs ↗

Reads at commit time. Delivers in real time.

Gluesync captures changes from the transaction log and delivers only the committed rows to the target. No full-table polling, no batch window and no unnecessary overhead on the source.

PostgreSQL source to Snowflake target through the Gluesync CDC engine

Pipeline automations

Automations

Event-driven actions and pipeline orchestration

Automate data workflows with scheduled jobs, platform-native triggers, and webhook integrations — reducing manual intervention across your pipeline.

Scheduled actions
In-platform Triggers
Webhooks

View Automations docs ↗

Event-driven data flow orchestration pipeline

Synthetic masking

Synthetics

On-demand mock data and test environment seeding

Generate realistic synthetic datasets on the fly to populate staging and QA environments — preserving schema structure without exposing sensitive production data.

Generate mock data on the fly
Continuously seed data to database tables
ORM-oriented stub data generation

View Synthetics docs ↗

Production Data Anonymization

Preserve referential integrity and database schema layout while fully scrubbing PII. Replicate production events downstream with secure synthetic representations.