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Apache Spark vs Okta

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
Okta logo

Okta

Technology

The identity platform for builders

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.; Okta requires $1,500 annual minimum contract
  • They diverge on capability: Apache Spark covers Unified engine, Okta covers Single sign-on (SSO).
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and Okta actually diverge.

Attributes where Apache Spark and Okta differ
AttributeApache SparkOkta
Pricing modelopen-sourceUnknown
PlatformsWebWeb, SaaS
FoundedUnknown2009

Identical on both: starting price (Free), free tier (Yes), 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 Okta

  • Single sign-on (SSO)
  • Multi-factor authentication
  • Lifecycle management
  • Universal directory
  • API access management
  • B2B integration
  • Passwordless authentication
  • Adaptive MFA

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

Okta

  • Employee SSOnot Apache Spark
  • Customer identitynot Apache Spark
  • B2B integrationnot Apache Spark
  • Zero Trust securitynot Apache Spark
  • Compliancenot 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.

Okta

  • Requires $1,500 annual minimum contract
  • Admin-only MFA can be vulnerable to push notification fatigue attacks
  • Implementation requires significant services and integration fees in year one

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Okta

Free
  • Starter Suite$6/user/month
    • SSO
    • MFA
    • Universal Directory
  • Core Essentials Suite$14/user/month
    • Adaptive MFA
    • Privileged Access
    • Lifecycle Management
  • Essentials Suite$17/user/month
    • Same as Core Essentials
    • Most popular
  • Professional Suite$undefined/custom
    • Device Access
    • Identity Security Posture
    • Threat Protection

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 Okta if

  • You need single sign-on (sso).
  • You want to start without paying.
  • You work on Web, SaaS.
  • You also want multi-factor authentication.
Okta review

Questions people ask

Is Apache Spark or Okta better?
Neither clearly leads. Apache Spark starts at Free and Okta at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Okta?
Apache Spark starts at Free and Okta at Free.
Does Apache Spark or Okta run on more platforms?
Apache Spark runs on Web. Okta runs on Web, SaaS.
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 Okta is typically brought in for.
What can Apache Spark do that Okta cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Okta covers Single sign-on (SSO), Multi-factor authentication, Lifecycle management, Universal directory.

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.

Okta: What is the minimum contract for Okta Workforce Identity?

Okta requires a $1,500 annual minimum contract for Workforce Identity plans. Pricing starts at $6 per user per month (Starter) and ranges to $17 per user per month (Essentials), billed annually.

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.

Okta: Does Okta offer a free trial?

Yes, Okta provides a 30-day free trial so customers can explore all product capabilities before committing to a paid plan.

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.

Okta: How many pre-built integrations does Okta support?

Okta offers over 7,000 pre-built integrations with popular enterprise applications including Salesforce, Slack, Workday, Box, Confluence, and Zendesk.

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.

Okta: What security features does Okta provide for admins?

Okta provides multi-factor authentication, adaptive MFA, single logout, and identity threat detection and response. However, only 94% of Okta customers have MFA enabled for administrators.

Source
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.

Okta: Can Okta integrate with on-premises directories?

Yes, Okta's Universal Directory and lifecycle management support cloud and on-premises identity sources, including Active Directory and LDAP.

Source
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