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Head-to-head · ETL Software

Apache Hop vs Apache Spark

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Apache Hop #5 in ETL Software 8.9/10 Free plan Free plan✓ 3 of 3 features Visit Apache Hop
Apache Spark #12 in ETL Software 8.2/10 Free plan Free plan✓ 3 of 3 features Visit Apache Spark

Apache Hop leads on 0 checks, Apache Spark on 0, and 4 are even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreApache Hop · 8.9/10
  • Free planboth

Apache Hop scores higher on our rubric for etl software: 8.9 against 8.2 out of 10; our editors rank them #5 and #12.

On deployment, Apache Hop gives you Hybrid where Apache Spark offers Self_hosted.

Apache Hop is the better fit for open-source visual ETL teams. Apache Spark is the better fit for large-scale open-source data processing.

  • Apache Hop fits best

    Open-source visual ETL teams

  • Apache Spark fits best

    Large-scale open-source data processing

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Side by side

Feature Apache Hop 8.9/10 Visit ↗ Apache Spark 8.2/10 Visit ↗
At a glance
Editor score 8.9 8.2
Ranking #5 in ETL Software #12 in ETL Software
Best for Open-source visual ETL teams Large-scale open-source data processing
Pricing model Free Free
Starting price Free plan Free plan
Free plan ✓ ✓
Free trial — —
Deployment Cloud, Self-hosted, Desktop Self-hosted
Platforms Windows, macOS, Linux Windows, macOS, Linux
Support Community, Docs Community, Docs
Integrations 20 integrations 20 integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Apache Hop 3/3 · Apache Spark 3/3
Incremental loading ✓ ✓
Change data capture ✓ ✓
Custom code transforms ✓ ✓
Specs
Deployment Hybrid Self_hosted
Source connectors Not checked yet Not checked yet
Destination connectors Not checked yet Not checked yet
Transformation mode Mixed Mixed
Our review
Pros
  • 250+ pipeline transforms and 80+ workflow actions
  • Runs locally, on servers, containers, Spark, Flink, and Dataflow
  • Supports CDC, scripting, Kubernetes Helm, and 40+ database dialects
  • Distributed batch and streaming processing with Structured Streaming
  • Python, SQL, Scala, Java, and R interfaces
  • Broad connectivity across files, databases, warehouses, and data platforms
Cons
  • Its breadth may exceed the needs of simple ETL jobs
  • Multiple execution runtimes add architecture choices to manage
  • Teams wanting a packaged paid tier need a different procurement model
  • Self-hosted deployment requires cluster and infrastructure management
  • More technical than hosted ETL tools for non-developer teams
  • No subscription tiers or packaged hosted SaaS experience
Our verdict

Apache Hop is an open-source data orchestration, engineering, integration, and ETL platform for data engineers and teams building repeatable integration workflows. Its visual environment supports pipeline and workflow design, while…

Read the review →

Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. It is designed for developers, data engineers, analysts, and organizations running workloads across clusters. Spark handles…

Read the review →
  1. Apache HopETL Software 8.9Free plan
  2. Apache SparkETL Software 8.2Free plan

Strengths and trade-offs

  • Apache Hop — where it wins

    • 250+ pipeline transforms and 80+ workflow actions
    • Runs locally, on servers, containers, Spark, Flink, and Dataflow
    • Supports CDC, scripting, Kubernetes Helm, and 40+ database dialects

    Where it doesn't

    • Its breadth may exceed the needs of simple ETL jobs
    • Multiple execution runtimes add architecture choices to manage
    • Teams wanting a packaged paid tier need a different procurement model

  • Apache Spark — where it wins

    • Distributed batch and streaming processing with Structured Streaming
    • Python, SQL, Scala, Java, and R interfaces
    • Broad connectivity across files, databases, warehouses, and data platforms

    Where it doesn't

    • Self-hosted deployment requires cluster and infrastructure management
    • More technical than hosted ETL tools for non-developer teams
    • No subscription tiers or packaged hosted SaaS experience

More comparisons

All ETL software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update