NoSQL vs SQL for OLTP serving and columnar SQL

SQL and NoSQL are presented as complementary tools rather than opposing choices, with database selection driven by specific access patterns, consistency requirements, scale, and operational capacity. Relational databases such as Postgres are well suited to transactional product data requiring ACID guarantees, relationships, constraints, and flexible SQL, while document, key-value, wide-column, and graph databases serve different needs such as flexible records, high-throughput keyed access, partition-local writes, or relationship traversal. The discussion emphasizes that distributed-system consistency tradeoffs, schema evolution, partition design, and operational burden matter as much as query syntax or licensing cost. Mature architectures commonly combine an operational SQL or NoSQL store with an immutable event stream and a separate columnar SQL warehouse or analytics system, avoiding expensive analytical scans on production databases. It argues that change data capture or dual event publication preserves historical data for reporting, funnels, retention, and auditing, while API layers can expose analytical queries without granting applications direct access to operational databases. Tinybird is described as a managed ClickHouse-based option for ingesting events, transforming them with SQL, and publishing analytics endpoints, but it is positioned as an analytics complement rather than a replacement for operational databases.

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