Change Data Capture (CDC)
Log-based change capture that streams database updates in real-time without impacting production performance.
Data Platforms CDC · Warehousing · Streaming
Data Engineering That Doesn't Break Your Production System
We build the pipelines and warehouse layer that get analytics off your production database. Senior data engineering, based in Southeast Asia.
30 minutes · No commitment · We respond within 24h
Data warehouses built from audit to production
CDC, streaming, and batch pipelines at scale
Senior team with a Southeast Asia cost base
Your Database Was Never Meant for Analytics
Most companies start the same way, and it escalates:
Running reports directly on the production database
Adding more queries as the business grows
Database slowdowns or outages under peak load
Reports taking minutes, or failing completely
At scale, this becomes an availability risk. The database that runs your product is doing double duty.
Scalable Data Engineering Workflows
The four building blocks we use most:
Event-Driven Ingestion
Decoupled ingestion layers using pub/sub systems that handle spikes and failures gracefully.
Data Warehouse Architecture
Layered design from raw ingestion to processed datasets to reporting-ready tables, optimized for query performance.
Orchestrated ETL Workflows
Automated, scheduled, and monitored data transformations with failure handling and retry logic built in.
We also implement schema validation and governance layers to prevent bad data from corrupting downstream systems.
From Unstable Database to Real-Time Data Platform
A typical engagement: a multi-location business running every report directly on its production database.
The Situation
- Primary database used directly for reporting
- Frequent outages (3–5 times per day)
- Slow report generation blocking operations
- Increasing operational risk as data volume grew
What We Implemented
- CDC pipeline using Debezium for real-time change capture
- Event streaming via Pub/Sub for decoupled ingestion
- Data warehouse on BigQuery with layered architecture
- Airflow-based orchestration for ETL workflows
- UI-driven pipeline generation for operational self-service
None of it is exotic tooling. It is standard components in the right architecture.
Why Most Data Projects Fail
Common mistakes we fix before they become expensive problems:
Treating Pipelines as Scripts
One-off scripts break silently. We build systems with monitoring, retry logic, and failure alerts.
Batch Processing for Real-Time Needs
Hourly batch jobs create stale data. We design streaming architectures when freshness matters.
Lack of Governance
No schema validation means bad data propagates. We enforce quality checks at every stage.
Tight Coupling
Monolithic data systems are hard to debug. We decouple ingestion, processing, and storage.
Why Teams Work With Us
Four reasons clients pick us over the alternatives:
Data Systems Expertise
CDC, streaming, warehousing, and orchestration for production workloads.
Backend & Infrastructure Depth
We design for the backend the platform lives in: load, growth, and failure modes.
Regional Cost Base
Senior engineering at Southeast Asia rates. The savings come from geography, not junior staff.
Handover, Not Lock-In
Documentation and training so your team can run the platform without us.
Three Ways to Work With Us
Pick the entry point that matches where you are:
01
Data Pipeline Audit
We review your architecture, find the bottlenecks and outage risks, and hand you a prioritized technical roadmap.
02
Data Platform Development
The end-to-end build: real-time and batch pipelines, warehouse implementation, and governance.
03
Optimization & Scaling
Cut latency and infrastructure cost, and scale the pipelines you already have without a re-architecture.
How We Compare to Other Options
The usual trade-off is cost against production experience. Here is how the options stack up.
Vincere
In-house
Freelancers
Agencies
Cost
Fraction of local cost
$180K–350K/year
Variable
$$$–$$$$
Time to Start
1–2 weeks
3–6 months
2–6 weeks
2–4 weeks
Production Experience
Built-in
Depends on hire
Rare
Mixed
Data Architecture
Designed for scale
Full control
Ad-hoc
Template-driven
Communication
Structured & daily
In-person
Sporadic
Account manager
Ongoing Support
Continuous
Dedicated
Unavailable
Retainer required
Build a Data System That Scales
Start with the audit. We map your bottlenecks and architecture gaps, and hand you the sequence of fixes.
Book a Free Consultation →
30 minutes · Audit-first engagement · Response within 24h
Frequently Asked Questions
What is Change Data Capture (CDC) and why does it matter?
CDC captures database changes in real-time by reading the transaction log, rather than polling or batch exports. This means your analytics data stays current without adding query load to your production database. It's the foundation of reliable, low-latency data pipelines.
How long does it take to build a data warehouse from scratch?
A focused data warehouse MVP typically takes 4–8 weeks depending on data source complexity, volume, and integration requirements. We prioritize getting your critical reports unblocked first, then iterate on additional pipelines and optimizations.
What does the Data Pipeline Audit include?
We review your current database architecture, reporting workflows, and data dependencies. We identify bottlenecks, outage risks, and inefficiencies. You receive a prioritized roadmap with architecture recommendations, effort estimates, and a clear sequence of improvements.
How do you ensure data quality and governance?
We implement schema validation at ingestion, automated data quality checks in the pipeline, and access controls at the warehouse level. This prevents bad data from propagating and ensures stakeholders only see data they're authorized to access.
How do you handle communication as a remote team?
Daily written updates, weekly syncs, and the work happens in repositories and dashboards you own. You review actual commits and running pipelines, not a status deck.