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Databases · head to head

Apache Spark vs Xata

Apache Spark logo

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
Visit Apache SparkFull review
Xata logo

Xata

Databases

Apache 2.0 platform for running many Postgres instances on Kubernetes, with copy-on-write branching and scale-to-zero.

From
Free
Free plan
Yes
Visit XataFull review

The short version

  • Apache Spark is free; Xata starts at $1/mo.
  • Only Apache Spark lists unified engine; only Xata lists copy-on-write branching.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and Xata differ
AttributeApache SparkXata
Pricing modelOpen-sourceUsage-based

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 Xata

  • Copy-on-write branching
  • Scale-to-zero compute
  • Compute autoscaling and bin-packing
  • High availability with failover
  • Point-in-time recovery
  • Serverless driver
  • pgroll migrations
  • pgstream replication

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

Xata

  • Giving every pull request or coding agent its own branch of the production database, with real data volumes rather than a seeded fixturenot Apache Spark
  • Running managed-Postgres economics in your own cloud account where data residency or compliance rules out a third-party control planenot Apache Spark
  • Consolidating many small, mostly idle Postgres databases onto shared infrastructure where scale-to-zero and bin-packing recover the idle costnot Apache Spark
  • Testing a destructive migration against a copy of production without waiting for a full restore or paying for a duplicate of the storagenot 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

Xata

  • Self-hosting means operating Kubernetes and CloudNativePG, so the Apache 2.0 licence removes the vendor bill but replaces it with a platform team, and a database platform is not something a part-time operator maintains safely
  • Copy-on-write branches are cheap to create but diverge as they are written to, so a long-lived branch carrying a heavy backfill quietly accumulates real storage and the cost arrives later than the decision that caused it
  • Scale-to-zero means the first connection after an idle period pays a cold start, which is invisible in a busy production database and very visible in a demo, a staging environment or a cron job that runs once an hour
  • The Xata sold before 2025 was a different product, a proprietary API and SDK layered over Postgres, so tutorials, blog posts and SDK examples from that era describe something that no longer exists and existing users had to migrate
  • As a managed service it competes with RDS, Aurora and Cloud SQL, and it is a much smaller company, so procurement, certification coverage and the depth of the support bench behind a 3am corruption incident are all weaker than the incumbent even though the underlying Postgres is the same

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Xata

Free
  • Free TrialFree
    • 14 days free
    • No credit card required
  • Usage-Based$1
    • 1,000 branches for $1
    • Scale-to-zero compute model
    • Branches hibernate when idle

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 Xata if

  • You need copy-on-write branching.
  • You want to start without paying.
  • You also want scale-to-zero compute.
Xata review

Questions people ask

Is Apache Spark or Xata better?
Neither clearly leads. Apache Spark starts at Free and Xata at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Xata?
Apache Spark starts at Free and Xata at Free.
Does Apache Spark or Xata 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 Xata is typically brought in for.
What can Apache Spark do that Xata cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Xata covers Copy-on-write branching, Scale-to-zero compute, Compute autoscaling and bin-packing, High availability with failover.

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.

Xata: Is it real Postgres or a compatible reimplementation?

Real Postgres. It runs upstream Postgres instances on Kubernetes via CloudNativePG, so extensions, the wire protocol and version upgrades behave as they do anywhere else.

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.

Xata: Can I self-host the whole thing?

Yes. The platform is Apache 2.0 and designed for self-hosting a large number of Postgres instances on your own Kubernetes. Xata Cloud is the same platform run as a service.

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.

Xata: Does branching copy my data?

No. Branches are copy-on-write at the storage layer, so creating one is near-instant regardless of database size and storage is only consumed as the branch diverges from its parent.

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.

Xata: Is this the same Xata I used a couple of years ago?

No. The earlier product was a proprietary database API with its own SDK and search layer. The current product is a Postgres platform, and material written for the old one does not apply.

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.

Xata: What happens to a branch when the parent changes?

A branch is a point-in-time fork. Later changes on the parent are not propagated, so long-lived branches drift and need to be recreated rather than refreshed if you want current data.

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