Compare top databases and you get listicles ranking Postgres above MongoDB because a survey said so. That helps nobody choosing infrastructure for the next five years. Postgres dominates OLTP. Snowflake dominates enterprise warehouse batch SQL. ScyllaDB dominates partition-local ops at extreme write rates. ClickHouse® dominates billion-row aggregations at millisecond latencies when rollups match dashboard grain. They are not substitutes. They are answers to different questions.
Compare by workload category first, then operating model, then cost at your concurrency, then time your team can spend operating what you pick.
Step 1: Name the workload before the brand
| Workload | Question you ask | Wrong engine symptom |
|---|---|---|
| OLTP | Update this user's row atomically | Analytics scans block checkout |
| Operational NoSQL | Read this key in 3 ms at 500k RPS | JOIN across partitions times out |
| Data warehouse | Report revenue by region last year | Product API hits JDBC to Snowflake |
| Columnar OLAP | Dashboard p99 under 100 ms on 90 days of events | Postgres replica melts on GROUP BY |
| Stream processing | Windowed counts in flight | Batch ETL delivers yesterday's answer |
| Search / retrieval | Full-text or vector nearest neighbor | OLTP index seq scans |
Until the workload is explicit, "top database" lists compare incomparable tools. A benchmark that wins on star-schema joins says nothing about your event JSON.
OLTP SQL tier: Postgres and peers
Postgres, MySQL, SQL Server, Oracle compete for transactional cores.
Postgres leads greenfield SaaS for extensions (PostGIS, pgvector), JSONB, logical replication, and ecosystem depth. MySQL and MariaDB remain common in hosted LAMP-style stacks and managed defaults on several clouds. SQL Server and Oracle dominate enterprises with existing licenses, compliance tooling, and DBA orgs.
When you compare top databases in this tier, score:
- Failover time and automatic vs manual promotion
- Connection pool behavior under burst (ORM defaults often exhaust max connections)
- Read replica lag if BI touches replicas (usually signals analytics should move out)
- Online schema change story for large tables
- Cross-region replication cost and conflict handling
Postgres vs MySQL and Postgres vs SQL Server cover pairwise OLTP comparisons when the shortlist narrows to two relational engines, not when the real question is analytics offload.
Operational NoSQL tier: keyed paths at scale
MongoDB, DynamoDB, Cassandra, ScyllaDB, Redis serve document and key paths. DynamoDB scales managed partitions on AWS; design still matters because throughput is bounded per partition key.
MongoDB: flexible documents, sharded clusters, aggregation pipelines that become expensive cross-shard. DynamoDB: managed partition scaling on AWS with per-partition throughput ceilings you must design around. Cassandra/ScyllaDB: write-heavy wide-column, tunable consistency, compaction as a lifestyle. Redis: in-memory speed, TTL semantics, persistence modes that surprise teams during failover.
High-performance NoSQL covers wide-column tuning when latency and write rate dominate the comparison. ScyllaDB alternatives maps the competitive set when Cassandra-compatible ops are on the table.
Score p99 under your key distribution, not uniform benchmarks. Celebrity keys invalidate spreadsheet comparisons.
Warehouse tier: cross-domain batch SQL
Snowflake, BigQuery, Redshift, Databricks SQL own historical reporting, finance close, and partner data shares.
Strengths: separation of storage and compute, role-based governance, SQL over lakehouse tables, clean room and sharing models for partners.
Weaknesses for product: query latency and per-query cost at high concurrency. Warehouses excel when seconds or minutes per query are acceptable and analysts are the primary users.
Redshift vs ClickHouse contrasts warehouse batch latency with columnar serving when the comparison includes interactive product analytics, not only BI decks.
When comparing warehouses, include egress to product systems, credit burn on poorly clustered tables, and whether ML features require export to other systems anyway.
Columnar OLAP tier: aggregations and serving
ClickHouse, Druid, Pinot, StarRocks optimize append-heavy analytics. ClickHouse is column-oriented OLAP SQL over MergeTree storage: vectorized execution, aggressive compression, materialized views, and merge tree engines tuned for events and metrics. Druid and Pinot target pre-aggregated cube serving when query shapes are fixed upfront. StarRocks adds MPP join patterns on fresher data with a separate FE/BE operational surface.
Compare on:
- Bytes scanned per dashboard interaction (not just best-case query time)
- Ingest lag from Kafka or HTTP to queryable rows
- Compaction and merge impact on p99 during heavy ingest
- Whether product needs HTTP endpoints or only SQL clients
ClickHouse vs StarRocks and StarRocks reviews help when the shortlist is MPP vs columnar event analytics.
Reference matrix: orientation, not a scorecard
| Engine class | OLTP | Key/value ops | Warehouse SQL | Sub-100 ms analytics API |
|---|---|---|---|---|
| Postgres | Strong | Weak | Poor at scale | Poor at scale |
| MongoDB | Moderate | Strong documents | Poor | Poor |
| DynamoDB | Conditional writes | Strong | No | No |
| ScyllaDB | No | Strong counters/sessions | No | No |
| Snowflake | No | No | Strong | Too slow/costly for product SLAs |
| ClickHouse | No | No | Strong batch | Strong with rollups + API layer |
Your scale and query mix reorder this table. A seed-stage app lives on Postgres alone. A social product at hundreds of millions of daily events adds Kafka, NoSQL serving, columnar OLAP, and often a warehouse for finance.
Bake-off methodology that survives procurement
- Capture ten production queries per candidate workload. Vendor benchmark SQL is irrelevant.
- Load test concurrency your product tier actually needs (500 concurrent dashboard users is not "10 analysts in SQL Lab").
- Measure ops: backup restore drill, version upgrade, on-call runbook page count, mean time to diagnose replica skew.
- Price steady state and peak including cross-AZ egress, support tier, and engineer headcount.
- Time-to-ship schema change: can you deploy a new metric column safely this week?
Low latency database criteria belong in the scorecard when comparisons include product-facing paths, not only quarterly reporting.
Governance and compliance in comparisons
Enterprise comparisons add row-level security, audit logs, column masking, and data residency. A engine that wins on raw speed loses if legal blocks the region you must store PII. Warehouses often lead governance features; operational NoSQL and columnar stores require explicit policy design.
Document which tier holds raw PII vs hashed identifiers vs aggregates. Privacy-preserving identity resolution matters when comparisons include partner data joins.
How Tinybird scores the analytics tier after OLTP picks
Every compare top databases exercise eventually hits the same wall: operational picks are Postgres plus DynamoDB, or Postgres plus ScyllaDB, but product still needs cohort funnels and usage dashboards at concurrency. The analytics tier decision is separate from OLTP vs NoSQL. It asks: columnar self-managed, columnar managed cloud, warehouse with reverse ETL, or a stack where real-time ingest, SQL, and published APIs ship together.
When that last option wins the scorecard, teams often land on Tinybird—managed ClickHouse with ingest connectors, SQL Pipes as HTTP endpoints, and branch-based deploys—rather than hiring a ClickHouse platform squad before the first dashboard ships.
Managed ClickHouse vs DIY vs warehouse reverse ETL
| Option | Wins comparison when... | Loses when... |
|---|---|---|
| Self-managed ClickHouse | You have CH ops expertise and need max control | On-call wants managed upgrades and ingest fabric |
| ClickHouse Cloud | You want vendor-managed CH without API layer | Product still needs custom auth/rate-limit service |
| Warehouse + reverse ETL | Finance owns metrics; product tolerates minutes lag | Sub-second dashboards or in-app analytics |
| Tinybird | Columnar SQL wins and you need endpoints + branches + lag metrics | Pure batch BI with no product API |
Managed ClickHouse options and ClickHouse deployment options frame the self-managed vs vendor spectrum before you add API requirements.
Ingest scoring: which upstream databases feed analytics
Comparisons should list every system that emits facts: OLTP SQL, document NoSQL, SaaS webhooks, observability agents. A managed columnar platform scores well when ingest diversity is high—on Tinybird that means:
- Events API for direct application POST without standing up Kafka first
- Kafka / Redpanda connector when the comparison already selected a bus for NoSQL write decoupling (Redpanda integration)
- S3/GCS loads for warehouse exports or batch reconciliation files
- Integrations for Segment, OpenTelemetry, Prometheus metrics when comparisons include observability alongside product events
Each path becomes a datasource with schema enforced at ingest. Bad JSON fails early instead of corrupting rollups.
Endpoint layer as a comparison criterion
Teams often forget the API tier in database comparisons. StarRocks exposes MySQL protocol. ClickHouse exposes HTTP and native protocol. Product needs REST with JWT tenant scope, OpenAPI docs, and per-route SLOs. Building that service in Go or Node around JDBC is a line item competitors skip in benchmarks.
SQL Pipes publish analytical SQL with template parameters:
NODE feature_adoption_funnel
SQL >
SELECT
step,
uniq(user_id) AS users,
users / first_value(users) OVER (ORDER BY step) AS retention
FROM (
SELECT user_id, 1 AS step FROM events WHERE event_type = 'signup'
UNION ALL
SELECT user_id, 2 FROM events WHERE event_type = 'activated'
UNION ALL
SELECT user_id, 3 FROM events WHERE event_type = 'paid'
)
GROUP BY step
ORDER BY step
TYPE endpoint
Compare time to first production endpoint across stacks, not only time to first SELECT on a laptop.
Branches and CI when the comparison includes release velocity
Database comparisons that ignore deployment safety favor familiar engines until a bad migration takes down dashboards. Branches create isolated workspaces with production-scale data for testing new Pipes and datasources before prod. Pair with npx tinybird deploy --dry-run in CI so analytics schema changes receive the same review as application migrations.
ClickHouse cluster operations in production covers replica and workload isolation when comparisons conclude self-managed CH. Choosing managed removes replica SKU math for teams who compared ops headcount and chose not to run clusters themselves.
Observability as a tiebreaker
When two columnar engines tie on benchmark SQL, compare operational visibility. Service Data Sources expose ingest lag, query errors, and per-endpoint latency percentiles without exporting logs to a third system first. That matters when the comparison includes on-call burden, not only license fees.
Comparison traps that invalidate scorecards
Single-vendor slide decks. Every vendor's benchmark wins on their hardware story and pre-tuned config.
Migrating before splitting workloads. Moving Postgres to Mongo without separating analytics repeats the problem in a new syntax.
Ignoring the API gap. Fast SQL port is not a product API. Auth, rate limits, versioning, and pagination still ship separately unless the platform includes them.
License vs cloud markup confusion. OSS engine cost differs from managed bill. ScyllaDB pricing and warehouse credit calculators belong in one spreadsheet row per tier.
Choosing on familiarity alone. Team knows Postgres, so analytics stay on Postgres until p99 forces emergency ClickHouse. Plan the split during architecture review, not during incident response.
What comparing top databases should produce
A shortlist per workload, not one winner on a podium. Most mature platforms run OLTP SQL, optional operational NoSQL, a warehouse for finance and BI, and columnar OLAP for product analytics with published endpoints.
Compare honestly on production queries, price ops into the matrix, and score the analytics tier on ingest diversity and API time-to-ship, not only warehouse scan speed.
Frequently Asked Questions (FAQs)
What is the best database overall?
None. Best depends on workload: transactions, keyed ops, warehouse reporting, or low-latency analytics APIs.
Is Postgres enough forever?
Often for early stage. Analytics and high-QPS serving paths usually add engines later.
Where does Tinybird fit in comparisons?
Managed columnar SQL with ingest, SQL Pipes as HTTP endpoints, and branch-based deploys when analytics must ship inside the product.
Should warehouses replace OLTP databases?
No. Warehouses complement operational stores for historical cross-domain SQL.
How many databases is too many?
Enough that each engine serves a clear access pattern with owned runbooks. Four tiers is common at scale.
Do benchmarks replace proof of concept?
No. Run your queries at your concurrency with your key skew.