Log-Based Change Capture
Debezium, Oracle GoldenGate, and SQL Server Change Data Capture read changes from the transaction log and stream them downstream with low latency and little load on production.
Database Solutions
Database Data Integration
Keep Every System Working From the Same Data
Our database engineers build the pipelines that move, replicate, and synchronize data between operational databases, warehouses, and applications. Whether you need change data capture in near real time or large nightly batch loads, we design for reliability first.
What We Deliver
Where We Make the Difference
Integration often breaks at the database layer: log readers that fall behind, bulk loads that lock tables, and sync jobs that quietly drop rows. We design with the internals of each engine in mind, so pipelines stay fast, light on the source, and simple to operate.
Fast Bulk Movement
For large volumes we use native bulk APIs, parallel and partition-level loading, and careful commit sizing so big transfers finish inside their window without blocking users.
Quality Checks in the Pipeline
Transformation and validation rules run inside the pipeline, so bad records are caught, quarantined, and reported instead of landing silently in reports or downstream systems.
Two-Way Synchronization
When more than one database accepts writes, we design conflict resolution, ordering, and consistency rules up front so distributed copies stay aligned and trustworthy.
Our Approach
How We Deliver Database Data Integration
Step 1: Define Freshness and Volume
For each data flow we agree how fresh the data must be, how much of it moves, and what should happen on failure, then choose the pattern that fits.
Step 2: Design the Data Flow
We specify extraction, transformation, loading, and error handling in a technical design your team can review before any pipeline code is written.
Step 3: Build and Load-Test
Pipelines are built and tested against production-sized data, so throughput, latency, and recovery behavior are proven before they carry live traffic.
Step 4: Run It in the Open
We deploy with lag and failure alerts, write the operating procedures, and stay on hand through early production until every flow runs steadily.
When should we use CDC instead of batch loads?
Use CDC when downstream users need fresh data or when full extracts put too much load on the source. Batch still makes sense for large, infrequent loads where some delay is acceptable. Many environments use both.
Will change data capture slow down our production database?
Log-based CDC reads the transaction log rather than querying tables, so the impact is usually small. We measure it during testing and size log retention so the capture process does not fall behind.
Can you integrate with our ERP or other packaged applications?
Yes. We pull from and push to the databases and APIs behind packaged applications, including ERP and CRM systems, while respecting each vendor's rules on direct database access.
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