Data / CDC
CDCInternalIntegration Pattern

Int-CDC-Internal

Problem

A backend workload or microservice requires timely, non-invasive access to incremental data changes originating from another backend workload's persistent data store. The goal is to facilitate data synchronization, enable event-driven processing, or power analytical insights without directly impacting the source system's performance or requiring intrusive modifications to its schema or application logic.

Solution

Implement a dedicated Change Data Capture (CDC) service that continuously monitors the source backend workload's data store for transactional changes (inserts, updates, deletes). This CDC service captures these changes as a stream of events, transforms them into a standardized format, and publishes them to a highly available, scalable event streaming platform or secure object storage for asynchronous consumption by subscribing backend workloads or data pipelines.

Cloud Paradigm

  • Event-Driven Architecture (EDA)
  • Data Streaming and Event Sourcing
  • Loosely Coupled Integration
  • Managed Data Services
  • Data Pipeline Automation
  • Data Mesh Principles (for decentralized data ownership and consumption)

Implementation Guidelines

Solution Flow

Change Data Capture (CDC) Flow:

  1. Source Backend Workload: A backend workload performs transactional operations (e.g., insert, update, delete) on its persistent data store.
  2. CDC Service Integration: A dedicated Change Data Capture (CDC) service, deployed in a private subnet (workloads), establishes a secure, non-invasive connection to the source data store. This connection can leverage database transaction logs, triggers, or specific vendor-provided CDC interfaces.
  3. Event Capture & Transformation: The CDC service continuously monitors the data store's transaction log or change streams, capturing raw data changes. It then transforms these changes into a standardized, structured event format (e.g., JSON, Avro, Protobuf), often including metadata like operation type, timestamp, and transaction ID.
  4. Event Publication: The transformed change events are published to a highly available and durable event streaming platform (e.g., Kafka-compatible service) or secure object storage. This acts as a central repository for the stream of changes.

Data Consumption Flow:

  1. Subscribing Backend Workload: A consumer backend workload or data pipeline, located in a private subnet (workloads), subscribes to the relevant topic(s) or bucket(s) on the event streaming platform or object storage.
  2. Event Ingestion: The subscribing workload ingests the change events, typically in chronological order.
  3. Data Processing & Synchronization: The consumer processes the ingested events to update its own data store, trigger downstream business logic, or feed an analytical data lake, ensuring data consistency with the source system without direct coupling.

Additional Details

  • Idempotency: Consuming applications should be designed with idempotency in mind to gracefully handle duplicate events or reprocessing scenarios, which can occur during system failures or scaling events within the event streaming platform.
  • Data Format & Schema Evolution: Standardize the event data format (e.g., JSON, Avro) and implement robust schema management practices. Use schema registries to manage schema evolution, ensuring backward and forward compatibility for consuming applications.
  • Error Handling & Dead-Letter Queues: Implement comprehensive error handling within the CDC service and consuming applications. Utilize dead-letter queues (DLQs) on the event streaming platform to capture and isolate events that fail processing, allowing for manual inspection and reprocessing.
  • Observability: Implement robust monitoring, logging, and alerting for the entire CDC pipeline. This includes tracking:
    • Latency from source change to event publication/consumption.
    • Throughput (events/messages per second).
    • Error rates in capture, transformation, and publication.
    • Consumer lag (the delay between events being published and processed).
    • Resource utilization of the CDC service and event streaming platform.
  • Scalability & Resilience: Design the CDC service and event streaming platform for high availability and scalability, allowing it to handle fluctuating data volumes and source system load without interruption. Utilize managed cloud services for event streaming where possible.
  • Data Retention: Define clear data retention policies for the event streaming platform or object storage to manage storage costs and compliance requirements.

Security Controls

  • Transport Security: Enforce strict Transport Layer Security (TLS 1.2 or higher) for all connections between the CDC component, source data store, event streaming platform, and consuming workloads.
  • Data Encryption: Ensure data is encrypted at rest within the source data store, the event streaming platform, and any transient storage used by the CDC service.
  • Authentication & Authorization:
    • The CDC service must authenticate securely with the source data store, ideally using service accounts with least privilege access, managed identities, or short-lived credentials.
    • Access to the event streaming platform or object storage containing change events must be restricted via Identity and Access Management (IAM) policies, ensuring only authorized subscribing workloads can consume data.
  • Secure Credential Management: Store all sensitive credentials (e.g., database connection strings, API keys) in a managed secrets management service, not in application code or configuration files. Rotate credentials regularly.
  • Network Segmentation: Deploy the CDC service, source data store, and event streaming platform within appropriate private subnets (workloads) with strict network access controls (e.g., Security Groups, Network ACLs) to limit ingress and egress to only necessary endpoints.
  • Data Governance & Masking: Implement data masking or tokenization for sensitive data elements within the change events before publishing, if required by compliance or privacy regulations.
  • Audit Logging: Enable comprehensive logging for all CDC operations, including successful data captures, publication events, and access attempts, for auditing and compliance purposes.