Batch File Transfer to Streaming CDC Blueprint

What and Why

Many integrations still move data as nightly file dumps (CSV/flat files over SFTP): high latency, fragile parsing, and no record of intra-day changes. Change data capture (CDC) reads the source database's transaction log and streams each insert, update, and delete in near real time onto an event backbone. Consumers get fresh data continuously instead of once a day.

This blueprint replaces batch file transfers with log-based CDC streamed over Kafka, keeping files only where external parties require them.

Phases

Assessment. Inventory batch file flows: source tables, schedule, format, consumers, and freshness needs. Confirm source databases support log-based CDC (WAL, binlog, redo).

CDC setup. Deploy a CDC connector (Debezium) reading the source transaction log. Plan the initial snapshot followed by streaming so consumers get the full state then ongoing changes.

Stream design. Publish change events to Kafka topics with schemas in a registry (Avro) and a compatibility policy. Apply a medallion approach if landing into a lakehouse (raw, cleaned, curated).

Consumer migration. Migrate consumers from parsing files to consuming the stream, processing idempotently and handling deletes. Run file and stream in parallel and reconcile.

Decommission. Retire the file jobs once the stream is proven. Keep file exports only for external parties that can't consume a stream, generating them from the stream.

Key Risks and Mitigations

• Data consistency: stream and source must match. Use log-based CDC (not query polling), reconcile against the source during parallel running, and monitor lag.
• Initial load: snapshotting large tables can strain the source. Use incremental/parallel snapshots and run during low traffic.
• Schema changes: source DDL can break consumers. Manage schemas in a registry with compatibility checks and propagate changes through data contracts.

Recommended Tooling

Debezium for log-based CDC, Kafka with a schema registry, Avro payloads, a lakehouse or warehouse target with medallion layers, reconciliation jobs during cutover, and lag/freshness monitoring.

Success Metrics

Measure data freshness (nightly to seconds), pipeline reliability versus fragile file jobs, lead time to onboard a new consumer, and reconciliation accuracy during parallel running.

Prerequisites

Source databases with log-based CDC enabled, a Kafka platform with a schema registry, idempotent consumer design, and agreement on data contracts and ownership.