Change Data Capture (CDC)

The most efficient CDC method reads directly from the database's transaction log (e.g., MySQL binlog, PostgreSQL WAL, or SQL Server T-Log). This approach is non-intrusive, capturing every INSERT, UPDATE, and DELETE without adding query load to the source database. It guarantees that changes are captured in the exact commit order, preserving transactional integrity for downstream graph construction.

CDC operates on two distinct distribution paradigms:

• Push (Event-Driven): The source system actively sends change events to a message broker like Apache Kafka. This is ideal for low-latency graph updates.
• Pull (Query-Based): A target system periodically polls a change table using a version number or timestamp. This is simpler to implement but introduces latency and can miss intermediate states if multiple changes occur between polls.

A robust CDC pipeline must gracefully handle schema drift in the source database. When a new column is added or a data type is modified, the CDC connector must propagate these changes to the downstream schema registry without breaking the ingestion flow. Failure to manage this results in poison pill messages that can stall the entire knowledge graph synchronization pipeline.

A standard CDC event payload contains:

• Before/After State: The full row state pre- and post-mutation.
• Operation Type: A flag indicating CREATE, UPDATE, DELETE, or TRUNCATE.
• Metadata: Timestamp, transaction ID, and source table name. This structure allows the downstream graph processor to map row-level operations directly to node and edge mutations in a property graph.

CDC pipelines operate in two distinct phases:

• Snapshot Phase: A bulk export of the current state of the source table to establish a baseline in the target system.
• Streaming Phase: Continuous capture of real-time changes from the transaction log. The transition between these phases must be carefully managed to prevent data gaps or duplication, often using a watermarking strategy.

In microservice architectures, CDC often complements the Transactional Outbox Pattern. Instead of dual-writing to a database and a message queue, a service writes to its local database only. The CDC process then tails the outbox table and reliably publishes the events to Apache Kafka, ensuring atomicity between state persistence and event publication for eventual consistency in the graph.

The process of augmenting raw data with machine-readable metadata and concept links. CDC delivers bare facts; semantic enrichment maps them to an ontology.

• Entity Linking: Connects text mentions to Wikidata or Schema.org identifiers.
• Classification: Tags incoming rows with taxonomy concepts.
• Transformation: Converts relational foreign keys into labeled graph edges with semantic meaning.

A dimensionality reduction technique mapping graph nodes into low-dimensional vector space. After CDC updates the graph structure, graph embeddings must be recomputed or updated to reflect new relationships.

• TransE / RotatE: Translational distance models for knowledge graph completion.
• Node2Vec: Captures homophily and structural equivalence in property graphs.
• Dynamic Embeddings: Online learning methods that update vectors incrementally as CDC streams arrive, avoiding full retraining.

A comprehensive methodology for defining an organization's critical data entities to provide a single authoritative reference. CDC acts as the real-time synchronization backbone for an MDM hub.

• Golden Record: The single best version of a customer or product, continuously updated via CDC.
• Registry vs. Transactional Hub: CDC feeds changes into the central MDM system for stewardship.
• Downstream Propagation: Once the master is updated, CDC pushes the harmonized data to all subscribing graph databases.

An algorithmic technique that hashes similar items into the same buckets with high probability. In a high-velocity CDC pipeline, LSH enables efficient deduplication before insertion into the graph.

• Blocking: Reduces the quadratic comparison space by grouping similar records.
• MinHash: Estimates Jaccard similarity for text-heavy attributes.
• Real-time Deduplication: Prevents the creation of duplicate nodes when CDC captures near-identical inserts from different source systems.