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Apache Spark vs Zeta

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
Zeta logo

Zeta

Technology

Cloud native credit card processing and core banking from Bhavin Turakhia's Zeta

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Apache Spark has a free tier, so it costs nothing to try first.
  • 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.; Zeta it competes against card processors with decades of production track record, and in card processing an outage is a public event, so a shorter operational history is a genuine risk factor rather than a technicality.
  • They diverge on capability: Apache Spark covers Unified engine, Zeta covers Tachyon card processing.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Apache Spark and Zeta differ
AttributeApache SparkZeta
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsWebWeb, API, Cloud, iOS, Android

Identical on both: 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 Zeta

  • Tachyon card processing
  • Real time authorisation
  • Rewards and offers engine
  • Core banking modules
  • Programme configuration
  • Mobile and web SDKs
  • Fraud and controls
  • Cloud deployment

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

Zeta

  • A United States bank whose card platform makes launching a new rewards product a multi quarter vendor projectnot Apache Spark
  • An issuer that wants authorisation level controls and real time data rather than end of day filesnot Apache Spark
  • A large fintech launching a credit card programme at scale with custom product logicnot Apache Spark
  • A bank consolidating card and deposit processing onto one modern platformnot 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.

Zeta

  • It competes against card processors with decades of production track record, and in card processing an outage is a public event, so a shorter operational history is a genuine risk factor rather than a technicality.
  • Contracts are enterprise scale and quoted with volume minimums, which puts it out of reach of small issuers who would otherwise benefit most from modern tooling.
  • A card portfolio migration runs twelve to twenty four months at minimum, during which the issuer runs two platforms and pays for both.
  • Zeta is a processor rather than a licence holder, so network membership, BIN sponsorship and regulatory obligations remain entirely with the issuer.
  • Public references for large live United States production volumes are fewer than the announcements imply, so diligence should insist on active account counts and uptime history rather than partnership press releases.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Zeta

On request
  • Tachyon platform$undefined/year
    • Quoted per institution, commonly on active accounts or transaction volume
    • Implementation for a card portfolio migration measured in quarters to years
    • Minimum volume commitments typical on enterprise contracts

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 Zeta if

  • You need tachyon card processing.
  • You work on Web, API, Cloud, iOS, Android.
  • You also want real time authorisation.
Zeta review

Questions people ask

Is Apache Spark or Zeta better?
Neither clearly leads. Apache Spark starts at Free and Zeta at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Zeta?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and On request for Zeta.
Does Apache Spark or Zeta run on more platforms?
Apache Spark runs on Web. Zeta runs on Web, API, Cloud, iOS, Android.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. Zeta starts at On request.
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 Zeta is typically brought in for.
What can Apache Spark do that Zeta cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Zeta covers Tachyon card processing, Real time authorisation, Rewards and offers engine, Core banking modules.

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.

Zeta: Which Zeta is this?

The banking technology company founded by Bhavin Turakhia, which builds the Tachyon card processing and core banking platform. Not any similarly named payroll or accounting product.

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.

Zeta: What is its strongest use case?

United States credit card issuing and processing, where legacy platforms make product changes slow and expensive.

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.

Zeta: Does Zeta provide the BIN or licence?

No. It processes. Network membership, BIN sponsorship and regulatory obligations stay with the issuer.

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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