Real-time data synchronization is one of those problems that sounds simple ("just keep the data in sync") and turns out to be deeply complex. After building sync systems for financial data, network infrastructure, and inventory management, I've identified four patterns that work in production — and the trade-offs that determine which one to use.
Pattern 1: Smart Polling
The simplest pattern, and often the right one. Poll the source system on a schedule, compare with the local state, and apply changes.
When to use it: The source system doesn't support webhooks or events. The data volume is manageable (under 100K records per poll). Near-real-time (30-second to 5-minute delay) is acceptable.
The trick: Use change detection to avoid processing unchanged records. Timestamps, checksums, or version numbers let you fetch only what's changed since the last poll. Without this, polling doesn't scale.
Pattern 2: Webhook-Driven Sync
The source system pushes notifications when data changes. Your system receives the webhook, fetches the updated data, and applies the change.
When to use it: The source supports webhooks. You need changes reflected within seconds. The volume of changes is moderate (hundreds to low thousands per minute).
The critical detail: Webhooks are unreliable. They get dropped, delivered out of order, or delivered multiple times. Every webhook-driven system needs an idempotent handler and a reconciliation job that periodically verifies the full dataset against the source. Trust but verify.
Pattern 3: Change Data Capture (CDC)
Read the database transaction log directly. Every insert, update, and delete is captured as an event and streamed to consumers.
When to use it: You control the source database. You need guaranteed delivery of every change. High-volume systems where polling is too slow and webhooks are too unreliable.
Implementation: I use PostgreSQL's logical replication or Debezium for CDC. The events flow into Kafka, where downstream consumers process them independently. This gives you exactly-once semantics (with proper consumer design) and the ability to replay the event stream if something goes wrong.
Pattern 4: Event-Driven Architecture
The source system publishes domain events to a message broker. Consumers subscribe to relevant events and maintain their own materialized views of the data.
When to use it: Multiple consumers need the same data in different shapes. The system is already service-oriented. You need to decouple the source from consumers completely.
The trade-off: Event-driven systems are eventually consistent. If you need strict consistency (account balances, inventory counts), you need additional coordination — saga patterns, distributed transactions, or careful event ordering.
Choosing the Right Pattern
Start with the simplest pattern that meets your latency and reliability requirements. For most systems, that's smart polling or webhooks with reconciliation. Reach for CDC or event-driven architectures when you have concrete scaling or decoupling requirements — not because they're architecturally elegant.
The best sync system is the one your team can operate, debug, and evolve. Complexity is a maintenance cost, and every additional moving part is a potential failure point.