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Databases · head to head

Apache Spark vs DataGrip

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
DataGrip logo

DataGrip

Databases

Cross-platform database IDE from JetBrains for SQL and NoSQL databases

From
Free
Free plan
Yes
Visit DataGripFull review

The short version

  • Apache Spark is free; DataGrip starts at $8.25/mo.
  • Only Apache Spark lists unified engine; only DataGrip lists intelligent SQL Completion.
  • Where DataGrip falls short: commercial use requires a paid subscription; the free tier is non-commercial only.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and DataGrip differ
AttributeApache SparkDataGrip
Pricing modelOpen-sourceSubscription
PlatformsWebWindows, macOS, Linux

Identical on both: starting price (Free), free tier (Yes), category (Databases).

Not yet recorded: founded for Apache Spark.

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 DataGrip

  • Intelligent SQL Completion
  • Schema Navigation
  • Data Editor
  • Version Control for Scripts
  • Multi-database Support

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

DataGrip

  • Writing and running SQL queries across multiple database enginesnot Apache Spark
  • Browsing and editing schema and table data visuallynot Apache Spark
  • Version-controlling database migration scriptsnot Apache Spark
  • Standardizing database tooling across a JetBrains-based teamnot 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

DataGrip

  • Commercial use requires a paid subscription; the free tier is non-commercial only
  • No built-in database administration features like backup scheduling found in dedicated DBA tools
  • Heavier resource footprint than lightweight single-purpose SQL clients
  • NoSQL support (e.g. MongoDB) is less mature than its relational database tooling

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

DataGrip

Free
  • Free (non-commercial)Free
    • Personal, non-commercial use only
  • Individual - Year 1$99/yr
    • Full DataGrip license
    • Free updates during subscription
  • Individual - Year 2+$79/yr
    • Continuity discount from second year onward

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 DataGrip if

  • You need intelligent sql completion.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want schema navigation.
DataGrip review

Questions people ask

Is Apache Spark or DataGrip better?
Neither clearly leads. Apache Spark starts at Free and DataGrip at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or DataGrip?
Apache Spark starts at Free and DataGrip at Free.
Does Apache Spark or DataGrip run on more platforms?
Apache Spark runs on Web. DataGrip runs on Windows, macOS, Linux.
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 DataGrip is typically brought in for.
What can Apache Spark do that DataGrip cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. DataGrip covers Intelligent SQL Completion, Schema Navigation, Data Editor, Version Control for Scripts.

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.

DataGrip: Is DataGrip free for personal use?

JetBrains introduced a free non-commercial license for DataGrip in October 2025, allowing personal use, while commercial use still requires a paid annual or monthly subscription.

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.

DataGrip: Who qualifies for the free non-commercial license?

Only individuals are eligible, for uses like learning, open-source work, or content creation. Anyone paid by an employer, including at a non-profit, must use a commercial license instead.

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.

DataGrip: How long does the free non-commercial license last?

It lasts one year and auto-renews if DataGrip was used at least once in the final six months; otherwise you can simply reapply for a new license.

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.

DataGrip: Does the free license have fewer features than the paid version?

No, it is a full-featured IDE identical to the paid version, though it requires anonymized telemetry sharing that cannot be opted out of under the non-commercial agreement.

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.

DataGrip: Can I use DataGrip offline with the free license?

No, activation requires logging into a JetBrains Account; offline activation codes are not available for the free non-commercial license.

Source
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