By John Apostolo, HEXstream full-stack data analyst

Change data capture (CDC) is a way to capture row-level changes—inserts, updates, deletes—from a source system and move them downstream continuously or in near-real-time. Batch processing, by contrast, moves snapshots or time-bound chunks of data on a schedule (hourly, nightly, weekly), producing new outputs per run.

Let’s explore…

Design principles for CDC

CDC systems are built around freshness and fidelity. The biggest design choice is where you capture change: log-based CDC usually preserves the most detail with the least impact on the source, while triggers or application events can be organized more simply but risk gaps or performance overhead. 

CDC also forces you to pick your “truth model”—do you emit raw row changes, business events, or full-state snapshots? 

Row changes are great for auditability and replay, but they push complexity onto consumers, who must handle deduping, ordering and idempotent upserts. Because failures are inevitable, CDC designs should treat replay as normal: stable keys, clear sequencing, and a defined bootstrap/backfill path are not “nice-to-haves”…they’re the difference between a pipeline you trust and one you babysit.

Design principles for batch

Batch optimizes for throughput and repeatability. The core tradeoff is freshness versus simplicity and cost. Full refreshes are the most reliable but can be expensive; incremental batch reduces cost but introduces watermark logic, late-arriving data handling, and partition repair policies. Batch architectures shine when you need heavy joins, large aggregations, and reproducible “as-of” reporting, where deterministic reruns matter more than minute-by-minute updates.

CDC-optimized databases

Downstream stores for CDC should be comfortable with constant upserts and deletes. That often means minimizing indexes, partitioning for write locality, and choosing merge-on-read versus merge-on-write (query-time cost vs. ingest-time cost). Compaction and retention policies matter because tombstones and high churn can quietly degrade performance.

Batch-optimized databases

Lastly, batch-optimized databases reward append-heavy loads, columnar storage, compression, and partition pruning. They prefer bulk inserts and partition overwrites over row updates, and they benefit from clustering/sort keys for common query patterns. The design bias is clear: scan fast, aggregate cheaply, and keep runs reproducible—even if “real-time” isn’t the goal.

CLICK HERE TO CONTACT US TO OPTIMIZE YOUR PROCESSING STRATEGIES.