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Apache Spark vs Nagios XI

Apache Spark logo

Apache Spark

Technology

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Rated
-
Full reviewWebsite
Nagios XI logo

Nagios XI

Technology

Enterprise-grade monitoring solution with advanced visualization and reporting

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Nagios XI free Edition caps at 7 nodes or 100 services, whichever limit is hit first
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and Nagios XI actually diverge.

Attributes where Apache Spark and Nagios XI differ
AttributeApache SparkNagios XI
Pricing modelopen-sourceone-time

Identical on both: starting price (Free), free tier (Yes), platforms (Web), user rating (Not yet rated), category (Technology).

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 Nagios XI

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 Nagios XI
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Nagios XI
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Nagios XI
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Nagios XI

Nagios XI

  • Monitoring servers, networks, and IoT devices for status changesnot Apache Spark
  • Detecting infrastructure problems before they cause outagesnot Apache Spark
  • Creating customizable dashboards for real-time IT visibilitynot Apache Spark
  • Tracking SLA compliance and generating capacity planning reportsnot 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.

Nagios XI

  • Free Edition caps at 7 nodes or 100 services, whichever limit is hit first
  • Standard Edition is licensed per node count, starting at $2,595 for 100 nodes and rising to $14,995 for 1,000 nodes
  • Enterprise Edition, which adds capacity planning and SLA reporting, is sold as an add-on starting at $4,690 for 100 nodes on top of the base license
  • Sitewide Edition for large distributed environments has no published price and requires contacting sales

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Nagios XI

Free
  • Free EditionFree
    • 7 nodes OR 100 services limit
    • Self-supported only
  • Standard 100-Node$2595/perpetual
    • 100 nodes
    • First year includes support and upgrades
  • Standard 500-Node$8295/perpetual
    • 500 nodes
    • First year includes support and upgrades
  • Standard 1000-Node$14995/perpetual
    • 1,000 nodes
    • First year includes support and upgrades

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose Nagios XI if

  • You want to start without paying.
Nagios XI review

Questions people ask

Is Apache Spark or Nagios XI better?
Neither clearly leads. Apache Spark starts at Free and Nagios XI at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Nagios XI?
Apache Spark starts at Free and Nagios XI at Free.
Does Apache Spark or Nagios XI 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 Nagios XI is typically brought in for.
What can Apache Spark do that Nagios XI 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.

Nagios XI: What is Nagios XI's pricing model?

Nagios XI uses perpetual licensing with one-time purchase costs based on node count, ranging from $2,595 for 100 nodes to $14,995 for 1,000 nodes.

Source
Apache 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.

Nagios XI: Is there a free version of Nagios XI?

Yes, Nagios XI Free Edition is available at no cost with a limit of 7 nodes or 100 services, whichever is reached first.

Source
Apache 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.

Nagios XI: What is included with Nagios XI licensing?

Initial licenses include one year of free version upgrades, up to 10 support cases, and support portal access. Each license permits three installations: production, backup/failover, and testing/development.

Source
Apache 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.

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