Google Pub/Sub integration for ecommerce reporting

Direct APIs are synchronous point-to-point connections (checkout calls ERP, ERP calls warehouse). Pub/Sub is asynchronous and decoupled: a producer publishes an event, subscribers consume it independently. If a subscriber is slow or down, the producer is not blocked. New consumers can be added without changing the producer.

We define formal event schemas and publish them to a shared registry. Producers validate events against the schema before publishing; subscribers validate on consume. We set up dead-letter queues to catch unparseable messages and monitor them daily. Breaking schema changes are caught in staging before reaching production.

Messages stay in the Pub/Sub topic for the configured retention window (we typically set 7-14 days for commerce estates). When the subscriber comes back online, it processes the backlog in order. If it is down longer than the retention window, we re-run historical extracts from the source systems to fill the gap.

Pub/Sub guarantees at-least-once delivery; duplicates can happen. We include a unique event ID in every message and have subscribers check for duplicates before writing to the warehouse (using a deduplication table or unique constraints). This ensures the same event never inflates a count, even if Pub/Sub delivers it twice.

Pub/Sub retains messages for the configured window. If you need data older than that, we re-run historical extracts from the source systems and publish them as catch-up events. For new subscribers, we can bulk-load historical data directly into the warehouse before connecting the live topic.

Pub/Sub orders messages within a partition key (e.g., order ID or customer ID), but not globally across all messages. We design partition keys so related events arrive in order. For events that must be strictly ordered across the entire topic, we handle ordering in the subscriber application.

We monitor topic lag (how far behind each subscriber is), error rates, message volume and retention window usage. We alert on lag spikes (subscriber is falling behind), error rate changes and topics approaching retention expiry. Dashboards show which subscribers are healthy and which need attention.

Pub/Sub charges per message published and per GB of retained data. We monitor volume and set up budget alerts. We compress messages, drop non-essential fields and tune retention windows to keep costs predictable. For high-volume topics, we discuss batch publishing and filtering strategies.

We build subscriber applications (on Cloud Run, Cloud Functions or Dataflow) that consume Pub/Sub messages and write them to BigQuery, Snowflake or your warehouse. We handle batching, partitioning and incremental updates so the warehouse stays in sync without duplicate or delayed data.

Yes. One producer publishes to a Pub/Sub topic; multiple subscribers consume it independently. One subscriber might write to the warehouse, another to your CDP, a third to a search index. Each consumer operates at its own pace without blocking the others.

We document which systems produce events, which topics they publish to, which subscribers consume them and what downstream tables and dashboards depend on them. We maintain a data lineage diagram in a wiki or metadata catalogue so teams can see the path from source to BI.

Pub/Sub is a managed service with a high SLA. If it does have an outage, events queue on the producer side (we implement buffering and retries). When Pub/Sub recovers, the queue drains automatically. For critical real-time flows, we can architect dual-write strategies or regional failover.

We route all data changes through compliance topics with audit logging enabled. For GDPR deletion requests, we have workflows to identify affected events in the log and purge them from the warehouse. The immutable event log provides a complete audit trail for regulatory investigations.

Pub/Sub is a pure broker; it does not transform messages. Transformation happens in subscriber applications. We build lightweight transformers (field mapping, deduplication, filtering) in Cloud Functions or Dataflow that sit between Pub/Sub and the warehouse, keeping the logic visible and testable.