Change Data Capture for AI-Ready Data Pipelines

AI features live or die on data freshness. A recommendation engine trained on last week's inventory, a fraud model scoring against yesterday's transaction history, or a search index missing this morning's product updates — all of these erode trust fast. Change data capture (CDC) is the pattern most engineering teams reach for when "batch overnight" is no longer good enough. This article explains what CDC is, how it fits into an AI-ready data stack, and when the added operational complexity is actually worth it for a mid-market team.

What Is Change Data Capture?

Change data capture is a data integration pattern that identifies changes (inserts, updates, deletes) made to data in a source system and delivers those changes as a continuous stream of events to downstream consumers. Instead of periodically re-querying an entire table, CDC captures only what changed, when it changed, and pushes it onward — typically within seconds rather than hours.

The most common implementation is log-based CDC, which reads a database's transaction log (the write-ahead log in Postgres, the binlog in MySQL, redo logs in Oracle) rather than querying the tables themselves. This keeps load on the source database low because CDC doesn't compete with production traffic for query capacity — it's reading a log that the database is already writing.

How Does CDC Fit Into an AI-Ready Data Stack?

CDC sits at the front of the pipeline, between source-of-truth operational databases and everything downstream that needs current data: analytics warehouses, search indexes, feature stores, and vector databases. It's the plumbing that keeps those systems from drifting out of sync with what's actually happening in the business.

In a typical AI-ready architecture, CDC events flow into a streaming platform, get transformed and enriched, then land in one or more destinations: a warehouse for BI and model training, a search index for retrieval, or a feature store for real-time inference. Google's Vertex AI, for example, offers feature stores and online feature serving specifically for this kind of downstream consumption — but feature stores and vector search assume the data arriving is already current. CDC is what makes that assumption hold. Without it, teams fall back to nightly batch jobs, and any AI feature built on top inherits that staleness. This is the same reason data infrastructure work often precedes AI feature delivery — you can't serve real-time recommendations or fraud scores on data that's a day old.

CDC vs Batch ETL vs API Polling: Which Should You Use?

The right approach depends on how fresh your downstream systems actually need to be, and how much operational investment you're prepared to make. There's no universally "better" option — each trades latency against complexity.

Approach	Latency	Operational complexity	Load on source system	Good fit for
Batch ETL (scheduled)	Hours	Low	Periodic spikes	Reporting, monthly reconciliation
API polling	Minutes to hours	Low–medium	Continuous, scales with poll frequency	Third-party integrations without log access
Log-based CDC	Seconds to minutes	Medium–high	Minimal	Real-time analytics, search sync, AI feature freshness

Batch ETL remains the right call for a lot of reporting workloads — if the business only looks at a dashboard once a day, streaming changes in real time adds cost without adding value. CDC earns its complexity when downstream consumers — search, personalisation, fraud detection, operational dashboards — need to reflect what happened minutes ago, not last night.

When Is CDC Worth the Operational Complexity for a Mid-Market Team?

CDC is worth adopting when the cost of stale data is measurable and recurring — not when it's theoretically nice to have. For most mid-market teams (50–2000 employees), that threshold is reached when at least one of these is true: customer-facing search or recommendations feel visibly out of date, fraud or risk models need to act on same-session behaviour, multiple systems are manually reconciled because nightly syncs disagree, or an AI feature genuinely can't function on batch-refreshed data.

If none of those apply, a well-scheduled batch pipeline with a shorter interval is often the pragmatic choice. CDC introduces genuine new failure modes: schema changes on the source table can break downstream consumers, out-of-order events need handling, and someone needs to own monitoring for pipeline lag and dead-letter queues. For a team without a dedicated data engineering function, that's a real ongoing cost, not a one-off build. This is a common gap we see when scoping data infrastructure engagements — teams have identified a genuine freshness problem but haven't yet weighed the operational ownership it requires.

What Are the Common CDC Implementation Patterns?

Three patterns dominate in practice, each with different trade-offs around intrusiveness and reliability.

Log-based CDC reads the database's own transaction log and is the least intrusive option — it doesn't require schema changes or extra queries against production tables, which is why it's the default recommendation for OLTP databases under real traffic. Trigger-based CDC uses database triggers to write change records to a separate table whenever a row changes; it's simpler to reason about but adds write overhead to every transaction on the source table. Timestamp or polling-based CDC queries a table periodically for rows modified since the last check; it's the easiest to implement but can miss hard deletes and introduces polling lag by design.

Most mature CDC platforms (Debezium is the widely used open-source option for log-based capture) default to log-based capture where the source database supports it, and fall back to polling for systems without log access — legacy databases or SaaS APIs, for instance.

What Are the Risks and Trade-offs?

CDC is not a set-and-forget integration — it's an operational system that needs monitoring like any other production service. Schema evolution on source tables, network partitions between the streaming layer and consumers, and event ordering across sharded databases are the three failure modes that most commonly catch teams out after go-live.

There's also a data governance dimension specific to Australian businesses. If CDC pipelines carry personal information — customer records, transaction history, health data — they fall within the scope of the Privacy Act 1988 and, for regulated financial services entities, APRA's CPS 234 information security standard. A pipeline that replicates production data into multiple downstream stores multiplies the number of places that data needs to be secured and audited, which is worth factoring into the design before the pipeline goes live rather than after.

Where Do Feature Stores and Vector Search Fit In?

Feature stores and vector search are downstream consumers of CDC, not replacements for it. A feature store centralises the features used for model training and real-time inference so they stay consistent between the two; a vector search index enables fast similarity lookups over embeddings for retrieval-augmented generation and semantic search. Both are only as fresh as the pipeline feeding them.

Platforms like Google's Vertex AI provide feature stores and online feature serving with nearest-neighbour vector search built in — but they're built to receive a continuous stream of current data, not to generate freshness themselves. If the upstream pipeline is still nightly batch, an online feature store just serves stale features faster. Getting the sequencing right — CDC first, feature store and retrieval infrastructure second — is one of the more common architecture decisions we work through with teams during ai-engineering scoping.

Getting the Sequencing Right

The honest answer for most mid-market teams is that CDC is a means to an end, not a milestone worth pursuing for its own sake. Start from the AI or analytics feature that actually needs real-time data, work backwards to what data freshness that requires, and only then decide whether CDC is the right mechanism — or whether a shorter batch interval solves the problem with far less operational overhead. Teams carrying legacy monoliths or fragmented data stores often need to address the underlying application modernisation work before a CDC pipeline is even feasible to build cleanly.

For more on how data foundations connect to AI outcomes, browse our insights.

If you're weighing up whether CDC is worth the investment for your data stack, we can help — starting with an honest look at where your data actually needs to be real-time, and where it doesn't.