Softwr

Databases · head to head

Apache Spark vs Knack

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
Knack logo

Knack

Databases

Hosted no-code database and web app builder priced by stored records and storage, with unlimited user accounts on every plan.

From
$59/month
Free plan
No
Visit KnackFull review

The short version

  • Apache Spark is free; Knack starts at $59/mo.
  • Only Apache Spark lists unified engine; only Knack lists visual database builder.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and Knack differ
AttributeApache SparkKnack
Starting priceFree$59/month
Pricing modelOpen-sourceSubscription
Free tierYesNo

Identical on both: 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 Knack

  • Visual database builder
  • User logins and roles
  • Page builder
  • Unlimited users
  • E-commerce
  • Automation flows
  • Scheduled tasks
  • REST API and webhooks

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

Knack

  • A member or client portal where thousands of external people need their own login and see only their own recordsnot Apache Spark
  • Replacing a shared spreadsheet used by a team that has outgrown it but cannot justify a developer to build a bespoke internal toolnot Apache Spark
  • Inventory, asset or case tracking for a small operations team that needs roles, forms and a record of who changed whatnot Apache Spark
  • A registration or booking application for an association, school or non-profit where the user count is large and the record count stays modestnot 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

Knack

  • Plans are capped by stored records, so an application that logs events or keeps history rather than current state climbs tiers on data volume alone, and archiving old records becomes a permanent maintenance chore rather than a one-off
  • API access is capped per day, at 1,000 calls on the entry plan and 10,000 at the Corporate tier, so a scheduled sync or a busy automation can exhaust the day's quota before the working day ends and integrations then fail silently
  • The application itself cannot be exported or self-hosted; records can be downloaded but the pages, roles, flows and business logic exist only inside Knack, so outgrowing the platform means rebuilding the app rather than moving it
  • Customisation beyond the builder is limited to injected CSS and JavaScript that Knack's support does not cover, so any non-standard interface behaviour becomes yours to maintain against a DOM the vendor can change without warning
  • Compliance capabilities that regulated buyers assume are baseline, HIPAA and GovCloud options, advanced SSO, IP restrictions and private environments, are gated to the Corporate and Enterprise tiers, so a small regulated pilot cannot legitimately start on an entry plan

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Knack

$59/month
  • Starter$59/mo
    • 20,000 database records
    • Unlimited users, tables, fields
    • 25 AI credits
  • Pro$130/mo
    • 50,000 database records
    • Remove Knack branding
    • E-commerce payments
  • Corporate$300/mo
    • 125,000-2.5M database records
    • App restore functionality
    • Advanced SSO
  • EnterpriseOn request
    • Dedicated account manager
    • HIPAA/GovCloud-based options
    • Custom pricing

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 Knack if

  • You need visual database builder.
  • You also want user logins and roles.
Knack review

Questions people ask

Is Apache Spark or Knack better?
Neither clearly leads. Apache Spark starts at Free and Knack at $59/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Knack?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and $59/month for Knack.
Does Apache Spark or Knack 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?
Yes. Apache Spark has a free tier, so you can try it without paying. Knack starts at $59/month.
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 Knack is typically brought in for.
What can Apache Spark do that Knack cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Knack covers Visual database builder, User logins and roles, Page builder, Unlimited users.

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.

Knack: Is Knack charged per user?

No. User accounts are unlimited on all plans. Plans are bounded by stored record count, file storage, API calls per day and automation transactions.

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.

Knack: What happens when I exceed the record limit?

You move to a higher tier or reduce stored records. Because the limit is on data rather than people, apps that accumulate history hit it while apps that track a fixed set of entities generally do not.

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.

Knack: Can I export my application or self-host it?

You can export record data. The application itself, its pages, roles, permissions and flows, is not exportable and cannot be self-hosted, so leaving means rebuilding elsewhere.

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.

Knack: Is it suitable for public-facing portals?

That is its strongest case, because logins are unlimited and role-based permissions are built in. The constraint is record volume and API throughput, not audience size.

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.

Knack: Can I use it for regulated data?

HIPAA-aligned and government cloud options exist but only on the Enterprise tier, along with private server environments. Lower tiers should not be used for regulated workloads.

Share

Related pages

More on Apache Spark

More on Knack

Keep looking

Other head to heads