Databases · head to head
Apache Spark vs Ninox
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
The short version
- Apache Spark is free; Ninox starts at €25/mo.
- Where Ninox falls short: free plan limited to 5 users and 5,000 records.
- Prices and features on this page were last checked on 13 November 2026.
Where they differ
Only the attributes on which Apache Spark and Ninox actually diverge.
| Attribute | Apache Spark | Ninox |
|---|---|---|
| Pricing model | Open-source | Freemium |
Identical on both: starting price (Free), free tier (Yes), platforms (Web), 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 Ninox
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 Ninox
- Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Ninox
- Feature engineering and model training across datasets too large to fit in pandas on one nodenot Ninox
- Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Ninox
Ninox
- No-code database solutions for small to medium teamsnot Apache Spark
- Workflow automation and process managementnot Apache Spark
- Customer data and project tracking with custom viewsnot 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
Ninox
- Free plan limited to 5 users and 5,000 records
- API call limits restrict automation on lower tiers (1,000/month free, 10,000/month team)
- Only 7-day history on free plan vs. unlimited on Business
Pricing, plan by plan
Apache Spark
FreeNo published plan breakdown. See the Apache Spark review.
Ninox
Free- FreeFree
- Up to 5 users
- 100 MB storage
- Up to 5,000 records
- Team€25/mo
- 5 GB storage per user
- 50,000 records per user
- 10,000 API calls per user per month
- Business€40/mo
- 10 GB storage per user
- 250,000 records per user
- 50,000 API calls per user per month
- EnterpriseOn request
- Custom pricing available
- Dedicated support
- Custom solutions and governance
Which should you pick?
Choose Apache Spark if
- You need unified engine.
- You want to start without paying.
- You also want catalyst optimiser.
Questions people ask
- Is Apache Spark or Ninox better?
- Neither clearly leads. Apache Spark starts at Free and Ninox at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Spark or Ninox?
- Apache Spark starts at Free and Ninox at Free.
- Does Apache Spark or Ninox run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- 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 Ninox is typically brought in for.
- What can Apache Spark do that Ninox 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.
Ninox: How much does Ninox cost?
Ninox is free for individuals with up to 5 users. Team plan is 25 EUR per user per month (billed annually), and Business plan is 40 EUR per user per month (billed annually). Enterprise plans have custom pricing.
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.
Ninox: Is Ninox free?
Yes, Ninox offers a free plan for up to 5 users with 100 MB storage and 5,000 records. No payment required to start, and includes all core views: table, form, chart, kanban, maps, and calendar.
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.
Ninox: What is Ninox Team plan pricing?
The Team plan costs 25 EUR per user per month (billed annually). It includes 5 GB storage per user, 50,000 records, 10,000 API calls per month per user, and 30-day history. Annual billing is required.
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.
Apache 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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