Push vs Pull

Push vs Pull is about who initiates the data transfer. Push means the source system actively sends updates. Pull means the destination system requests updates.

Implementation approaches:

• Push systems need retry logic and duplicate handling

• Pull systems need careful polling intervals

• Each push request needs a unique ID for deduplication

• Version numbers help process changes in order

• Entity IDs help track changes for different items separately

Polling vs Webhooks

Polling vs Webhooks defines how systems check for updates. Polling regularly asks "any updates?" while webhooks notify immediately when changes happen.

Implementation considerations:

• Polling is simpler but can waste resources

  • The interval depends on your requirements.

  • Polling may not be suitable if real-time/accuracy is a concern.

• Webhooks need proper error handling and retry logic: Make sure it's idempotent. In your own DB store the request IDs.

• Webhook processors must handle duplicates using request IDs

• Failed webhook processing often uses Dead Letter Queues (DLQ). (We queue the work needed to do)

• Often it's webook -> process -> retry -> dlq

• Systems often combine both for reliability

Batch vs Stream Processing

Batch vs Stream Processing determines when we handle data. Batch processing waits to collect data before processing. Stream processing handles each piece as it arrives.

Implementation patterns:

• Batch processing good for end-of-day reconciliation.

• Stream processing needed for real-time reactions

• Kafka Streams helps process streaming data

• Batch systems often use scheduled jobs

• Stream systems need state management

Event-Driven Architecture

Event-Driven Architecture decouples systems by communicating through events. Systems publish events without knowing who's listening.

Common approaches:

• CDC with Kafka captures database changes

• Direct event publishing for business events

• Traditional pub/sub for simple notifications

• Each event needs type, payload, and metadata

• Events can chain together for complex workflows

Summary

The key insight is choosing patterns based on needs:

• Use webhooks for simple real-time updates

• Use CDC with Kafka for reliable, ordered changes

• Use batch processing when real-time isn't crucial

• Use event-driven when systems need loose coupling

You can also take hybrid approaches depending on the system's different needs.