NoSQL vs SQL for OLTP serving and columnar SQL

NoSQL vs SQL debates usually compare the wrong things. A MongoDB cluster beside Postgres is not "NoSQL vs SQL" in the abstract. It is document serving vs relational transactions. A ScyllaDB counter beside Snowflake is wide-column ops vs warehouse SQL. Teams pick badly when they force one model to do every job, then spend a year tuning the wrong engine for GROUP BY.

The useful question: what access pattern does this path need, what consistency does the business require, and who operates the system at 3 a.m.?

What SQL databases optimize for

Relational SQL engines (Postgres, MySQL, SQL Server, Oracle) excel at:

• ACID transactions across related rows in one commit boundary
• Ad hoc joins on normalized schemas at moderate data sizes
• Constraints and foreign keys enforced at write time
• Mature tooling for migrations, backups, point-in-time recovery, and ORM layers

Postgres remains the default application database because row-level consistency and flexible SQL cover most product state: accounts, orders, permissions, billing entitlements. JSONB blurred the document vs relational line: nested payloads without giving up transactions on the parent row.

SQL struggles when:

• Analytics scans billions of rows while OLTP traffic shares the same CPU and buffer pool
• Horizontal scale means application-level sharding because single-node limits hit before feature velocity slows
• Every new dashboard dimension requires a new index or materialized view on the primary
• Cross-region active-active writes need conflict resolution SQL was not designed to hide

How to handle analytics workloads in Postgres walks the split when BI queries slow the same database that handles checkout. The symptom is always the same: pg_stat_activity full of long-running aggregations while p99 on UPDATE orders climbs.

What NoSQL categories actually mean

"NoSQL" is four different families pretending to be one label:

Postgres vs MongoDB compares document vs relational when the argument is truly about those two shapes. ClickHouse® vs MongoDB is a different comparison: analytical columnar SQL vs document storage for event history, not vs Postgres for OLTP.

NoSQL does not mean "no query language." Cassandra and ScyllaDB use CQL. MongoDB has aggregation pipelines with $lookup stages that behave like joins until shard count makes them expensive. The split is data model, partition placement, and consistency, not syntax alone.

CAP, consistency, and the NoSQL vs SQL fork

Distributed systems trade consistency, availability, and partition tolerance. SQL primaries often default to strong consistency within one node or one synchronous replica set. NoSQL systems expose the tradeoff in configuration, as documented for Cassandra consistency levels and DynamoDB read consistency:

That matters for NoSQL vs SQL on product paths:

• Inventory decrement needs transactional SQL or carefully designed conditional writes in DynamoDB with compensating sagas
• Like counter often tolerates brief eventual consistency if the UI rounds gracefully and reconciliation jobs exist
• Session store wants low-latency keyed reads; Redis or DynamoDB beat normalized SQL joins for read-modify-write every request
• Ledger or billing stays on SQL unless you enjoy distributed transaction research

Wide-column NoSQL tuned for partition-local reads delivers single-digit millisecond p99 on keyed paths when partition keys match query keys. Warehouse SQL on columnar storage delivers sub-second to second-scale aggregates over terabytes. Those numbers are not comparable without naming the query and the concurrency.

Schema evolution on both sides

SQL migrations use ALTER TABLE, often with lock risk on large tables. Teams batch migrations, use expand-contract patterns, or rely on managed services with online schema change tools.

NoSQL schema evolution is application-side: new optional fields in documents, new columns in wide rows, dual-write periods when partition keys change (painful). MongoDB schema validation can enforce shape but not save you from bad partition decisions retroactively.

Event-first architectures reduce the debate: append immutable facts to a log, derive serving views in NoSQL or SQL materializations. The log becomes the contract; serving stores become disposable projections. That pattern only works when product teams agree on event envelopes and versioning up front.

When production stacks use both

Mature platforms rarely pick one side:

1. Postgres (SQL) for authoritative product state and billing truth
2. Kafka or Redpanda for durable event fan-out between writers and consumers
3. ScyllaDB, DynamoDB, or Redis (NoSQL) for high-QPS serving paths keyed by user, session, or entity
4. ClickHouse® (columnar SQL) for analytics, funnel SQL, and product-facing aggregates
5. Snowflake or BigQuery for finance, long-range cross-domain reporting, and partner exports

ClickHouse vs Postgres explains OLTP vs OLAP split without pretending either replaces the other. CDC from Postgres or MongoDB into an analytics tier avoids running SELECT COUNT(*) GROUP BY on the operational primary. A practical guide to real-time CDC with MongoDB covers change streams into columnar storage when document DB remains system of record for documents but not for analyst SQL.

Decision checklist by access path

Cost and ops: the hidden column in NoSQL vs SQL

License cost is not operating cost. Self-managed Cassandra or ScyllaDB saves cloud markup until you pay platform engineers for compaction tuning, rebuilds, and upgrade ordering. Managed RDS Postgres costs more per vCPU until you account for backups, patching, and failover drills you no longer run.

Compare cost per successful user request at peak, not cost per terabyte stored. NoSQL counter storage is cheap until celebrity keys force partition splits and emergency resharding. SQL storage is cheap until replicas multiply to isolate analytics.

Self-hosted vs managed ClickHouse applies the same operating-model lens to the analytics tier both NoSQL and SQL paths eventually need.

How Tinybird ends the false binary in NoSQL vs SQL

The NoSQL vs SQL argument often stops at operational stores and ignores analytics entirely. Product teams still ask funnel questions, retention cohorts, and abuse detection queries over months of behavior. Neither MongoDB aggregation pipelines nor Postgres replicas handle that concurrency well at billion-event scale.

Both camps eventually add a columnar SQL tier: append-optimized storage, rollup-friendly engines, and HTTP endpoints that never touch OLTP credentials. Self-managing ClickHouse still leaves ingest connectors, merge tuning, and a bespoke API service on your roadmap. Tinybird is managed ClickHouse with those layers included—Events API and Kafka ingest, SQL transformations, published endpoints—so Postgres rows, DynamoDB keys, and ScyllaDB counters stay on serving paths while analyst SQL lives elsewhere.

Why operational NoSQL makes analytics ingestion harder, not easier

NoSQL serving stores optimize for current state: latest counter value, latest session blob, latest feature vector. Analytics needs history: how counts moved, when sessions started, which features co-occurred before churn. If the only copy of behavior lives in mutable NoSQL rows, you lose auditability and replay.

The fix is dual publication: write the serving update to ScyllaDB or DynamoDB and append an immutable event to Kafka or the Events API. Serving stays fast. History lands in MergeTree tables with partition keys aligned to query filters (toYYYYMM(event_time), tenant_id).

ClickHouse OpenTelemetry integration shows per-signal schemas when logs, metrics, and traces must not share one wide table. The same discipline applies when Mongo documents and SQL rows both emit events: separate datasources or strict envelope fields, not one JSON junk drawer.

Ingest paths when SQL and NoSQL both exist

Landing Segment events the right way means keeping identity in graph edges or dimension tables, not duplicated on every fact row. That mirrors NoSQL vs SQL discipline: traits in one place, events append-only elsewhere.

SQL Pipes as the analytics API both sides avoid building

Opening JDBC from a Next.js server to Postgres, or CQL from a dashboard to Cassandra, couples product release cadence to database credentials and connection storms. SQL Pipes on Tinybird publish the same analytical queries as HTTPS endpoints with typed parameters and documented responses—no separate auth service to build or pool to exhaust.

Example: daily active users after events flow from either SQL or NoSQL origins into one product_events table:

NODE daily_active_by_store
SQL >
    %
    SELECT
        toDate(event_time) AS day,
        store_region,
        uniq(user_id) AS dau
    FROM product_events
    WHERE event_time >= today() - 30
      AND plan = {{ String(plan) }}
    GROUP BY day, store_region
    ORDER BY day DESC, dau DESC
    SELECT
        toDate(event_time) AS day,
        uniq(user_id) AS dau
    FROM product_events
    WHERE event_time >= today() - 30
    GROUP BY day
    ORDER BY day DESC
%

TYPE endpoint
JWT fixed_params pin tenant_id or store_region server-side so clients cannot widen scope. That pattern matters when NoSQL partitions are per-tenant but analytics endpoints serve multi-tenant dashboards.
Schema iteration when NoSQL partition keys change
NoSQL migrations that rewrite partition keys often run for quarters with dual writes. Analytics schema cannot freeze during that window. Branches on Tinybird provide zero-copy environments with production-shaped volume: test new columns (event_origin LowCardinality(String)) or new rollup Pipes before prod deploy.
Define datasources as code (.datasource files or @tinybirdco/sdk TypeScript) so analytics schema changes ride the same PR review as application changes. ClickHouse operating models compares who owns schema deploys when OLTP, NoSQL, and OLAP coexist.
Observability across the split stack
When product latency regresses, teams blame the wrong tier. Service Data Sources expose ingest lag per datasource and p95/p99 per published endpoint. If Kafka consumer lag grows, NoSQL counters may look fresh while analytics dashboards show stale cohorts. If endpoint p99 spikes while ingest is healthy, rollups or SQL templates need tuning, not more ScyllaDB nodes.
OLAP most common mistakes lists scan patterns that hurt p99 after you already solved NoSQL vs SQL for serving.
Where teams misread NoSQL vs SQL
Picking Mongo because JSON. JSONB in Postgres may cover the same need with one fewer system and transactional guarantees on parent rows.
Running analytics on the OLTP primary. NoSQL or SQL, full scans starve product traffic. Replica lag is not a analytics strategy.
Calling columnar OLAP "NoSQL." ClickHouse speaks SQL. The storage model is columnar and append-optimized, not schemaless documents.
Treating sync replication as free. Cross-region SQL and quorum NoSQL both pay latency and conflict costs.
One migration fixes everything. Moving chat to ScyllaDB does not retire the warehouse. ShareChat NoSQL modernization describes operational wins that still leave analytics as a sibling system fed by Kafka.
What NoSQL vs SQL comes down to
SQL wins transactional truth and flexible relational queries at moderate scale. NoSQL wins partition-local throughput and model flexibility when ops teams accept tuning and limited cross-partition query. Analytics at billion-row scale wants columnar SQL, a third category both sides add after the first pain point.
Pick per access path. Wire immutable events once. Serve product reads from the engine built for that shape. Publish analytical SQL through an API layer that does not expose operational credentials to frontends.
Frequently Asked Questions (FAQs)
Is NoSQL faster than SQL?
For keyed reads and massive write throughput on well-partitioned data, many NoSQL systems beat general-purpose SQL primaries. For complex joins and multi-row transactions, SQL remains the default.
Can Postgres replace MongoDB?
Often, with JSONB and careful indexing. Document-heavy workloads with heavy sharding and flexible nested arrays may still prefer MongoDB.
Is ClickHouse NoSQL?
No. ClickHouse uses SQL on columnar storage. It targets analytics and aggregation, not row-level OLTP or document serving.
When should both coexist?
Almost always at scale: SQL or NoSQL for operational state, a log for events, columnar SQL or a warehouse for history and aggregates.
Does Tinybird replace operational databases?
No. Tinybird serves analytical SQL and HTTP endpoints over ingested events. Postgres, MongoDB, DynamoDB, and ScyllaDB remain on serving paths.
How do teams connect MongoDB and analytics?
Change streams or CDC into columnar tables, with identity and traits modeled separately from append-only events.
End the analytics half of the NoSQL vs SQL debate
Tinybird is managed ClickHouse with Events API and Kafka ingest, SQL Pipes as endpoints, and branch-based schema iteration. Keep operational stores for serving; query history in SQL.
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