Your data lake, in minutes —
not 24 hours.
CloudZA's ETL Engine replaces nightly batch jobs with continuous replication from your source databases into a queryable data lake — with zero load on your production infrastructure. No full-table scans. No overnight wait. No stale dashboards.
What you get from day one
- Minimal load on your application databaseBinlog reads only — no full-table scans, no production slowdowns
- Analytics insights with zero app changesConnect to your existing databases as-is — no schema changes, no new dependencies
- Replicates millions of records per minuteHigh-throughput pipeline built for production data volumes
- Configurable data freshnessFrom near-real-time to hourly — you set the cadence to match your SLAs
- Zero-downtime deploymentRuns alongside your existing infrastructure — no risky cutover required
Keeping analytics current at scale is harder than it looks.
As data volumes grow and operations run in real time, the gap between what happened and what your dashboards show widens. Stale data, brittle pipelines, and compliance exposure compound.
Analytics lag behind operations
By the time analysts open their dashboards, the data no longer reflects current state. Collections, operations, and finance teams make decisions on numbers that are already out of date.
Fragile pipelines that fail silently
A single failed extract stalls the entire pipeline. Nobody notices until the morning stand-up. By then, the window for action has passed and someone needs to run a manual fix.
Infrastructure load from heavy extracts
Full-table scans pull significant load from production databases at precisely the wrong moment — when they are already under operational pressure.
Data residency gaps
Centralising multi-region data for analytics without moving personal data across borders is complex to architect and hard to audit. Most ETL tools don't address it at all.
The pipeline, end to end.
DB
Any source database
ETL ENGINE
(via cdc)
Datalake
Apache Iceberg on S3
QuickSuite/BI
Dashboards & analytics
Live analytics, without the heavy lifting.
Point it at your databases
Connect the ETL Engine to your existing source databases as-is — no schema changes, no app rewrites, no new dependencies. Setup is measured in days, not quarters.
Changes stream in continuously
Every insert, update, and delete flows into your data lake on the cadence you choose — from near-real-time to hourly. No full-table scans, no overnight batch windows, minimal load on production.
Query live, build dashboards
Analysts and BI tools query the data lake directly — no exports, no manual refreshes. Dashboards stay current to the minute, so every team decides on data that reflects what's happening now.
Analytics without borders — data that stays within them.
Data residency is enforced at the infrastructure level. Each AWS region writes to its own isolated S3 data lake. Only Glue catalog metadata — table definitions, no row data — is replicated centrally for unified querying. This means global analytics without moving personal data across jurisdictions.
Fully managed AWS services. Infrastructure as code.
The entire solution is deployed via AWS CDK — multi-region stacks, reproducible, version-controlled infrastructure. The ETL Engine runs on EC2 in a private subnet; the analytics layer is entirely serverless. No servers to patch, no dashboards to maintain manually.
CloudZA designs, deploys, and operates the platform as a managed service. You get access to a monitoring dashboard; we handle incident response, schema drift, and optimisation.
Where real-time data pipelines make the difference.
Multi-brand analytics at scale
Enterprises with multiple brands or business lines need unified dashboards across products and regions — without centralising data where regulations don't permit it. The ETL Engine delivers per-region Iceberg lakes with a unified query layer on top.
Finance reporting without exports
A regulated financial services business needed GWP, commission, and margin visibility without relying on developers to run manual extracts. The ETL Engine streamed changes to Iceberg, giving finance teams self-service QuickSight dashboards refreshing from live production data — no CSV uploads, no manual triggers.
Compliance-safe cross-region consolidation
An organisation operating across the EU, SA, and AU needed global analytics without moving personal data across borders. Per-region Iceberg lakes with metadata-only cross-region replication delivered unified dashboards while keeping row data in-jurisdiction.
Build a pipeline that keeps up.
We'll assess your current pipeline, map your source databases, and scope a CDC deployment that fits your AWS environment — region by region, database by database. No disruptive cutover; the new pipeline runs alongside the existing one until you're ready to switch.