Is this a data collection system? Are we going to use PostgreSQL?

A data collection system built on PostgreSQL can be highly effective when designed with scalability, reliability, and query performance in mind. First, model the data with a normalized schema that captures the core entities: events, sources, and metadata. Use a dedicated "events" table with a composite primary key (source_id, event_timestamp) and a JSONB column for flexible event payloads. Index the timestamp and source_id columns to accelerate time‑range and source‑specific queries. For high write throughput, enable WAL archiving and use the "unlogged" tables for transient ingestion, then periodically sync to the main table.

Partitioning is essential: logical partitioning by time (e.g., monthly) keeps each partition small, improves vacuum performance, and allows dropping old data quickly. Use PostgreSQL’s native partitioning or declarative partitioning to avoid manual sharding.

To scale reads, set up streaming replication with read replicas. For write scaling, consider a write‑through cache (e.g., Redis) or a message queue (Kafka) that batches inserts into PostgreSQL. Monitor vacuum activity and autovacuum settings to prevent bloat.

Finally, secure the system with role‑based access, SSL connections, and regular backups. With these practices, PostgreSQL can serve as a robust backbone for a data collection pipeline that handles millions of events per day while remaining maintainable and cost‑effective.