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Head-to-head · ETL Software

Informatica Data Integration vs Apache Spark

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Informatica Data Integration #2 in ETL Software 9.3/10 Free plan Free plan✓ 3 of 3 features Visit Informatica
Apache Spark #12 in ETL Software 8.2/10 Free plan Free plan✓ 3 of 3 features Visit Apache Spark

Informatica Data Integration 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 scoreInformatica Data Integration · 9.3/10
  • Free planboth

Informatica Data Integration scores higher on our rubric for etl software: 9.3 against 8.2 out of 10; our editors rank them #2 and #12.

On deployment, Informatica Data Integration gives you Hybrid where Apache Spark offers Self_hosted.

Informatica Data Integration is the better fit for enterprise hybrid ETL and ELT teams. Apache Spark is the better fit for large-scale open-source data processing.

  • Informatica Data Integration fits best

    Enterprise hybrid ETL and ELT teams

  • Apache Spark fits best

    Large-scale open-source data processing

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Side by side

Feature Informatica Data Integration 9.3/10 Visit ↗ Apache Spark 8.2/10 Visit ↗
At a glance
Editor score 9.3 8.2
Ranking #2 in ETL Software #12 in ETL Software
Best for Enterprise hybrid ETL and ELT teams Large-scale open-source data processing
Pricing model Free plan + paid Free
Starting price — Free plan
Free plan ✓ ✓
Free trial — —
Deployment Cloud, Self-hosted Self-hosted
Platforms Web, Windows Windows, macOS, Linux
Support Live chat, Phone, Community, Docs Community, Docs
Integrations 10 integrations 20 integrations
Built for Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Informatica Data Integration 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
  • Supports ETL, ELT, CDC, replication and schema-change handling
  • Combines hosted, cloud and Secure Agent runtimes
  • Broad connectors for databases, warehouses and SaaS applications
  • 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
  • Paid pricing is consumption-based and requires a quote
  • Some free-plan support is limited to community support
  • Advanced capabilities may require more technical implementation effort
  • 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

Informatica Data Integration is a cloud data integration platform for enterprise data teams working across cloud applications, databases, flat files and on-premises systems. It supports ETL and ELT pipelines, data replication, change data…

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. Informatica Data IntegrationETL Software 9.3Free plan
  2. Apache SparkETL Software 8.2Free plan

Strengths and trade-offs

  • Informatica Data Integration — where it wins

    • Supports ETL, ELT, CDC, replication and schema-change handling
    • Combines hosted, cloud and Secure Agent runtimes
    • Broad connectors for databases, warehouses and SaaS applications

    Where it doesn't

    • Paid pricing is consumption-based and requires a quote
    • Some free-plan support is limited to community support
    • Advanced capabilities may require more technical implementation effort

  • 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