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Databases · head to head

DuckDB vs Snowplow

DuckDB logo

DuckDB

Databases

MIT-licensed analytical SQL database that runs inside your process, with no server, no dependencies and one writer at a time.

From
Free
Rated
-
Full reviewWebsite
Snowplow logo

Snowplow

Business Intelligence

Behavioural data pipeline you run in your own cloud, relicensed away from Apache 2.0 in 2024

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only DuckDB has a free tier, so it costs nothing to try first.
  • Each has a real cost: DuckDB a database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.; Snowplow the core pipeline was relicensed from Apache 2.0 on 8 January 2024 to the Snowplow Limited Use Licence Agreement and a Confluent-derived community licence, so organisations that adopted it as permissively licensed software must now review their entitlement, buy a licence, or migrate.
  • They diverge on capability: DuckDB covers In-process execution, Snowplow covers Own-cloud deployment.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which DuckDB and Snowplow actually diverge.

Attributes where DuckDB and Snowplow differ
AttributeDuckDBSnowplow
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsLinux, macOS, Windows, WebAssemblyLinux, Web, Docker
CategoryDatabasesBusiness Intelligence
Founded2019Unknown

Identical on both: user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in DuckDB

  • In-process execution
  • Vectorised columnar engine
  • Direct file querying
  • Zero dependencies
  • Larger-than-memory queries
  • MIT licence
  • Postgres-flavoured SQL
  • Extension ecosystem

Only in Snowplow

  • Own-cloud deployment
  • Schema enforcement
  • Warehouse loading
  • Enrichment
  • Trackers
  • Streaming output
  • Data models
  • Snowplow BDP

What people use each for

The jobs each tool is most often brought in to do.

DuckDB

  • Transformation steps in a data pipeline that would otherwise need Spark, replaced by SQL over Parquet in a single processnot Snowplow
  • Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot Snowplow
  • Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot Snowplow
  • Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot Snowplow

Snowplow

  • A data team that needs full-fidelity event data in its own warehouse to build attribution or machine learning features rather than to populate dashboardsnot DuckDB
  • A regulated business that cannot send behavioural data to a third-party analytics vendor and must keep collection inside its own cloud accountnot DuckDB
  • A product organisation tired of silently malformed events, which wants a schema contract enforced at collection timenot DuckDB
  • A company modelling customer behaviour across web, mobile and server events that needs them in one consistent structurenot DuckDB

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

DuckDB

  • A database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
  • There is no network protocol, authentication or user management, so exposing it to remote clients means writing and securing your own service around it.
  • It is built for scans and aggregations, not for many small transactions, so a workload of high-frequency single-row inserts and updates performs badly compared with SQLite or Postgres.
  • Storage files are backwards compatible but not forwards compatible, so a file written by a newer version cannot be read by an older one and every consumer of a shared file must be upgraded together.
  • Query memory settings matter: some operations still need to hold significant state, so an under-configured memory limit turns a large join or a high-cardinality aggregation into a spill-heavy query or an out-of-memory failure rather than a slow success.

Snowplow

  • The core pipeline was relicensed from Apache 2.0 on 8 January 2024 to the Snowplow Limited Use Licence Agreement and a Confluent-derived community licence, so organisations that adopted it as permissively licensed software must now review their entitlement, buy a licence, or migrate.
  • You run the pipeline in your own cloud, which means the infrastructure bill, the on-call rota and the upgrade work are yours, and at high event volume that operational cost frequently exceeds what a hosted product would have charged.
  • Schema enforcement is the main benefit and the main friction, because every new event requires a schema to be authored and versioned, and teams without discipline around that end up blocked on their own governance process.
  • There is no analysis layer: Snowplow delivers data to your warehouse and nothing else, so you still need modelling, a BI tool and the people to run them before anyone sees a number.
  • The licence change fractured the community, spawning an Apache 2.0 fork, which means community contributions and third-party tooling are now split across two codebases with uncertain long-term maintenance.

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Snowplow

On request
  • Snowplow BDP$undefined/year
    • Commercial pricing quoted by event volume and deployment
    • Core pipeline components under the Snowplow Limited Use Licence Agreement, not Apache 2.0
    • Trackers, analytics SDKs and Iglu SDKs remain Apache 2.0

Which should you pick?

Choose DuckDB if

  • You need in-process execution.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, WebAssembly.
  • You also want vectorised columnar engine.
DuckDB review

Choose Snowplow if

  • You need own-cloud deployment.
  • You work on Linux, Web, Docker.
  • You also want schema enforcement.
Snowplow review

Questions people ask

Is DuckDB or Snowplow better?
Neither clearly leads. DuckDB starts at Free and Snowplow at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Snowplow?
DuckDB has a free tier; the other does not. Paid plans start at Free for DuckDB and On request for Snowplow.
Does DuckDB or Snowplow run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Snowplow runs on Linux, Web, Docker.
Can I use DuckDB for free?
Yes. DuckDB has a free tier, so you can try it without paying. Snowplow starts at On request.
What is DuckDB best used for?
DuckDB is most often used for transformation steps in a data pipeline that would otherwise need spark, replaced by sql over parquet in a single process, analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptable, local exploration of files that are too large for a pandas dataframe but far too small to justify a warehouse, continuous integration and testing of analytical sql, where a real engine can run in the test process without provisioning anything. Of those, transformation steps in a data pipeline that would otherwise need spark, replaced by sql over parquet in a single process and analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptable are not what Snowplow is typically brought in for.
What can DuckDB do that Snowplow cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Snowplow covers Own-cloud deployment, Schema enforcement, Warehouse loading, Enrichment.

Answered from the vendors’ own pages

DuckDB: Can multiple applications share one DuckDB database?

Not for writing. One process holds the database read-write; others may attach read-only and will not see later writes. Shared multi-writer access needs a different database or a table format with a catalogue.

Snowplow: Is Snowplow still open source?

Not in the permissive sense. On 8 January 2024 the core pipeline moved from Apache 2.0 to the Snowplow Limited Use Licence Agreement, with version 1.1 following in December 2024, alongside a community licence based on the Confluent Community Licence. Trackers and analytics SDKs remain Apache 2.0.

DuckDB: Is it a replacement for a data warehouse?

For single-node analytical workloads up to a few hundred gigabytes it very often is. It is not a replacement when many concurrent users need a shared, governed, always-on service.

Snowplow: Can we still run it for free in production?

Free use of the relicensed core components is materially constrained and commercial use generally requires an agreement. Read the current licence text against your intended use rather than relying on older documentation.

DuckDB: Do I have to load data into it?

No. It queries Parquet, CSV, JSON and Arrow in place, including on object storage. Its own storage format is optional and mainly useful when you want indexes, constraints and faster repeated access.

Snowplow: Is there an Apache 2.0 alternative?

Yes, a fork called OpenSnowcat was created in response to the relicensing and continues under Apache 2.0.

DuckDB: What is MotherDuck's relationship to it?

MotherDuck is a separate company offering a managed and hybrid service built on the DuckDB engine. DuckDB itself remains MIT-licensed and independent of it, with the IP held by the DuckDB Foundation.

Snowplow: What does Snowplow BDP cost?

Not published. It is quoted by event volume and deployment, and your own cloud infrastructure costs are separate and additional.

DuckDB: Is it suitable for OLTP?

No. It is designed for analytical scans. For transactional workloads with frequent small writes, SQLite or Postgres is the right tool.

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