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Keboola vs Apache Storm

A side-by-side editorial comparison of Keboola and Apache Storm — release velocity, themes, recent moves, and the top alternatives to consider.

Keboola vs Apache Storm: at a glance

FeatureKeboolaApache Storm
SectorAnalyticsAnalytics
Velocity score7.56.3
Sparks · 30d20
Top themesdata-platform, ai-agents, mcp, etlstream-processing, modernization, security, scheduler
Last editorial update1d ago1mo ago
WebsiteVisit →Visit →

What is Keboola?

Keboola's Kai AI assistant hits GA, completing the pivot from data platform to AI-native pipeline orchestration layer.

Keboola's Kai AI assistant is now generally available for multi-tenant contracted customers, marking the formal transition from experimental feature to production-ready product. The platform is simultaneously navigating Snowflake's forced password authentication deprecation — a platform-level forcing function requiring customer migrations before September 7. Recent work also includes the MCP server gaining unified cross-project authentication, which enables AI coding assistants to act as first-class operators across a full Keboola stack.

Read the full Keboola trajectory →

What is Apache Storm?

Storm 3.0 finishes removing the Clojure it was built in, and moves to a Java 21 baseline.

Storm is running two lines: a 2.8.x maintenance branch that is mostly Dependabot traffic punctuated by real security releases, and the new 3.0.0 line cut on 22 July. 3.0.0 removes all remaining Clojure from the codebase, raises the baseline to Java 21 (with master already on 25), and ships throughput work — zstd compression for thrift cluster state, tuple compression between workers, and decoupling of the control plane from the data plane on receive queues. The 2.8.6 and 2.8.7 releases earlier in the window carried four CVEs, including a deserialization RCE reachable by any user with topology submission rights.

Read the full Apache Storm trajectory →

Keboola vs Apache Storm: editorial side-by-side

K
Keboola
ANALYTICS
7.5

Keboola's Kai AI assistant hits GA, completing the pivot from data platform to AI-native pipeline orchestration layer.

◆ Current state

Keboola's Kai AI assistant is now generally available for multi-tenant contracted customers, marking the formal transition from experimental feature to production-ready product. The platform is simultaneously navigating Snowflake's forced password authentication deprecation — a platform-level forcing function requiring customer migrations before September 7. Recent work also includes the MCP server gaining unified cross-project authentication, which enables AI coding assistants to act as first-class operators across a full Keboola stack.

◆ Where it's heading

Keboola is building toward a model where AI agents can autonomously manage the data pipeline lifecycle. The MCP server's unified auth is a signal: the target is a world where a developer's coding assistant can browse, create, and modify Keboola pipelines without a human navigating the UI. Kai GA and the MCP expansion are the same thesis from two directions — AI as interface, not AI as feature. Branched storage expansion to BigQuery and Flows improvements in the background are the operational stability layer those agents will depend on.

◆ Prediction

Kai gaining the ability to create and modify Flows, and the MCP server expanding its coverage to transformation and workspace management, are the two most visible next moves. The Branched Storage on BigQuery release is a prerequisite for Branches 2.0 approval workflows, which would give Kai a way to propose and commit pipeline changes with human review gates.

A
Apache Storm
ANALYTICS
6.3

Storm 3.0 finishes removing the Clojure it was built in, and moves to a Java 21 baseline.

◆ Current state

Storm is running two lines: a 2.8.x maintenance branch that is mostly Dependabot traffic punctuated by real security releases, and the new 3.0.0 line cut on 22 July. 3.0.0 removes all remaining Clojure from the codebase, raises the baseline to Java 21 (with master already on 25), and ships throughput work — zstd compression for thrift cluster state, tuple compression between workers, and decoupling of the control plane from the data plane on receive queues. The 2.8.6 and 2.8.7 releases earlier in the window carried four CVEs, including a deserialization RCE reachable by any user with topology submission rights.

◆ Where it's heading

The project is converting itself from a legacy JVM codebase into an ordinary modern Java one, and the 3.0 work shows where that energy goes next: scheduling and queueing. Recent PRs add AIMD dynamic batch sizing to JCQueue, jitter metrics and a jitter-aware stream grouping, round-robin rebalance onto returning supervisors, and several fixes for stale or orphaned worker heartbeats. Alongside that, the distribution is being slimmed — optional Hadoop and Kafka dependencies were unbundled and shared jars de-duplicated. The 2.x branch is being kept alive for security and dependency currency, not for features.

◆ Prediction

Expect 3.0.x point releases to concentrate on the scheduler and worker-lifecycle fixes that 3.0.0 opened up, and expect the 2.8.x line to keep receiving CVE backports while feature work stays on 3.x. The Java 25 baseline already on master suggests the next minor will move the floor again.

Alternatives to Keboola and Apache Storm

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either Keboola or Apache Storm.

See all Keboola alternatives → · See all Apache Storm alternatives →

Recent activity from Keboola and Apache Storm

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1d agoKeboolaKai is Now Generally Available (Multi-Tenant, Contracted Customers)
  2. 6d agoKeboolaMigrate Snowflake Workspaces from Password to Key Pair Auth
  3. 22d agoKeboolaKeboola MCP: Log In Once, Work Across Every Project
  4. 27d agoKeboolaPython 3.10 Deprecation for Streamlit Apps (September 2026)
  5. 1mo agoKeboolaFlows: New Condition Operators and Run Selected Tasks
  6. 1mo agoKeboolaBranched Storage on BigQuery
  7. 1mo agoApache StormStorm 3.0 drops Clojure entirely and moves to Java 21
  8. 1mo agoApache Storm2.8.9 is a dependency sweep with one Flux viewer guard
  9. 1mo agoApache Storm2.8.8 backports a Kafka topology-lag fix
  10. 4mo agoApache StormTwo TLS CVEs fixed: JVM-wide downgrade and auth bypass
  11. 4mo agoApache StormDeserialization RCE and stored XSS in the UI are fixed
  12. 5mo agoApache Storm2.8.5 is dependency upgrades plus small logging fixes

Frequently asked questions

What is the difference between Keboola and Apache Storm?

They serve adjacent needs but don't currently overlap on shipped themes. Keboola is currently shipping more aggressively (velocity 7.5 vs 6.3), with 2 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is Keboola better than Apache Storm?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Keboola is currently shipping more aggressively (velocity 7.5 vs 6.3), with 2 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to Keboola?

Top Keboola alternatives in Analytics are ranked by recent ship velocity. Browse the "Keboola alternatives" section above for the current picks, or visit /alternatives/keboola for the full list with editorial commentary on each.

What are the best alternatives to Apache Storm?

Top Apache Storm alternatives in Analytics are ranked by recent ship velocity. Browse the "Apache Storm alternatives" section above for the current picks, or visit /alternatives/storm for the full list with editorial commentary on each.