CDC and Event-Driven Integration

While the previous lesson focused on pre-warming tables and planning for capacity spikes, the operational story does not end once writes land in Amazon Keyspaces. The next challenge is reacting to those mutations after they happen, capturing every INSERT, UPDATE, and DELETE so that downstream systems can act on the data without the writing application needing to know or care. This is where change data capture enters the picture.

In Amazon Keyspaces, Change data capture (CDC)A service-native mechanism that records row-level mutations from a database table in near real time and exposes them as a stream of events for downstream consumers. streams provide a fully managed, transparent pipeline of row-level change records. Unlike open-source Cassandra commit-log tailing, which requires self-managed infrastructure and parsing logic, and unlike DynamoDB Streams, which serves a different data model, Keyspaces CDC is purpose-built for the Keyspaces service. It requires no schema changes and no modifications to the application's write path.

Before diving deeper, a few terms will recur throughout this lesson. A CDC stream is the ordered sequence of change records emitted by a CDC-enabled table. A stream consumerA downstream process or AWS service that reads change records from a CDC stream and performs actions such as transformation, routing, or storage. reads those records and forwards them for processing. Per-replica enablement means that on multi-Region tables, an operator explicitly chooses which Region's replica emits CDC events. A downstream processor is any service or function that ultimately acts on the change, whether that is an analytics store, a notification channel, or a search index.

This lesson traces the full arc of Keyspaces CDC, starting with how streams work internally, moving through ordering semantics and multi-Region nuances, and finishing with practical event-driven integration patterns that unlock value from captured changes.

How Keyspaces CDC streams work

When CDC is enabled on an Amazon Keyspaces table, the service begins recording a change record for every row-level mutation. Each record captures the after-image of the affected row, meaning it reflects the final state of the row following the mutation rather than the CQL statement that caused it. This distinction matters because CDC is not a query replay log; it does not store intermediate ...