Databases · head to head
Apache Spark vs dbt
Apache Spark
Databases
A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.
- From
- Free
- Free plan
- Yes
dbt
Databases
SQL transformation framework enabling analytics engineers to version, test and deploy models
- From
- Free
- Free plan
- Yes
The short version
- dbt is a transformation framework.
- Apache Spark is free; dbt starts at $100/user/mo.
- Where dbt falls short: free tier severely limited: 3,000 models/month cap and single project maximum restricts team and production use.
- Prices and features on this page were last checked on 13 November 2026.
Where they differ
Only the attributes on which Apache Spark and dbt actually diverge.
| Attribute | Apache Spark | dbt |
|---|---|---|
| Pricing model | Open-source | Subscription|free |
| Platforms | Web | Web, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf) |
Identical on both: starting price (Free), free tier (Yes), category (Databases).
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 Apache Spark
- Unified engine
- Catalyst optimiser
- DataFrame and SQL APIs
- Structured Streaming
- Spark Connect
- Kubernetes and YARN support
- Table format integration
- MLlib
Only in dbt
Nothing recorded that Apache Spark does not also cover.
What people use each for
The jobs each tool is most often brought in to do.
Apache Spark
- Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot dbt
- Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot dbt
- Feature engineering and model training across datasets too large to fit in pandas on one nodenot dbt
- Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot dbt
dbt
- Data warehouse transformation and ELT pipelinesnot Apache Spark
- Analytics engineering for reporting and business intelligencenot Apache Spark
- Data quality testing and validation at scalenot Apache Spark
- Cross-functional data collaboration with version controlnot Apache Spark
- Cost optimisation of warehouse usage through intelligent schedulingnot Apache Spark
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Apache Spark
- Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query
- The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around
- It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing
- Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite
- Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade
- PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions
dbt
- Free tier severely limited: 3,000 models/month cap and single project maximum restricts team and production use
- Starter at $1,200/year per seat: minimum 5-seat team costs $6,000/year baseline; no single-seat or 2-seat paid option
- Enterprise pricing opaque: 'custom pricing' with no budget range for startups vs. enterprises; requires sales consultation
- Model volume metering unclear: 'successful models/month' as a limit is ambiguous; unclear if this counts transformation runs, test runs, or deployment attempts
Pricing, plan by plan
Apache Spark
FreeNo published plan breakdown. See the Apache Spark review.
dbt
Free- DeveloperFree
- Browser-based IDE
- MFA
- job scheduling
- Starter$100/user/mo
- dbt Wizard
- basic Catalog
- Semantic Layer
- EnterpriseOn request
- Advanced Catalog
- Semantic Layer
- dbt Copilot
- Enterprise+On request
- All Enterprise features
- PrivateLink
- IP restrictions
Which should you pick?
Choose Apache Spark if
- You need unified engine.
- You want to start without paying.
- You also want catalyst optimiser.
Choose dbt if
- You want to start without paying.
- You work on Web, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf).
Questions people ask
- Is Apache Spark or dbt better?
- Neither clearly leads. Apache Spark starts at Free and dbt at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Spark or dbt?
- Apache Spark starts at Free and dbt at Free.
- Does Apache Spark or dbt run on more platforms?
- Apache Spark runs on Web. dbt runs on Web, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf).
- Can I use Apache Spark for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Apache Spark best used for?
- Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what dbt is typically brought in for.
- What can Apache Spark do that dbt cannot?
- Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming.
Answered from the vendors’ own pages
Apache Spark: When is Spark the wrong choice?
When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.
dbt: How much does dbt cost?
dbt Developer is free. dbt Starter is $100/seat/month with a 5-seat minimum (or custom annual billing). Enterprise and Enterprise+ have custom pricing and require contacting sales.
SourceApache Spark: Is Spark the same on Databricks as the open source version?
No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.
dbt: What is included in the free Developer plan?
The Developer plan is free and includes 1 developer seat, 3,000 successful models/month limit, 1 project, browser-based IDE, MFA, and job scheduling.
SourceApache Spark: Can I use Spark for real-time processing?
For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.
dbt: What is the model limit on each plan?
Developer tier allows 3,000 successful models/month. Starter allows 15,000/month. Enterprise and Enterprise+ allow 100,000/month.
SourceApache Spark: Does upgrading between major versions break things?
Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.
dbt: Can I upgrade or downgrade my dbt plan?
Yes. dbt allows you to 'upgrade or downgrade at any time.' Starter plans bill monthly by credit card based on seat count; Enterprise plans are annual invoicing.
SourceApache Spark: Do I need to know Scala?
No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.
Related pages
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