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Head-to-head · ETL Software

Azure Data Factory vs Apache Spark

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Azure Data Factory #6 in ETL Software 8.8/10 Paid ✓ 3 of 3 features Visit site
Apache Spark #12 in ETL Software 8.2/10 Free plan Free plan✓ 3 of 3 features Visit Apache Spark

Azure Data Factory leads on 0 checks, Apache Spark on 1, and 3 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 scoreAzure Data Factory · 8.8/10
  • Free planonly Apache Spark

Azure Data Factory scores higher on our rubric for etl software: 8.8 against 8.2 out of 10; our editors rank them #6 and #12.

Apache Spark offers free plan; Azure Data Factory doesn't. On deployment, Azure Data Factory gives you Hybrid where Apache Spark offers Self_hosted.

Azure Data Factory is the better fit for cloud ETL teams in mixed environments. Apache Spark is the better fit for large-scale open-source data processing.

  • Azure Data Factory fits best

    Cloud ETL teams in mixed environments

  • Apache Spark fits best

    Large-scale open-source data processing

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Side by side

Feature Azure Data Factory 8.8/10 Visit ↗ Apache Spark 8.2/10 Visit ↗
At a glance
Editor score 8.8 8.2
Ranking #6 in ETL Software #12 in ETL Software
Best for Cloud ETL teams in mixed environments Large-scale open-source data processing
Pricing model Paid Free
Starting price Not checked yet Free plan
Free plan — ✓ (best)
Free trial — —
Deployment Cloud, Self-hosted Self-hosted
Platforms Web Windows, macOS, Linux
Support Docs Community, Docs
Integrations 90+ integrations 20 integrations
Built for Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Azure Data Factory 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
  • More than 90 built-in connectors across cloud, on-premises, and multicloud sources
  • Visual pipelines, mapping data flows, scheduling, monitoring, and alerting
  • Supports incremental loading, CDC, self-hosted runtimes, and SSIS migration
  • 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
  • Usage-based pricing varies by region and currency
  • CDC factory resources are documented as public preview
  • Its broad feature set may exceed the needs of simple ETL projects
  • 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

Azure Data Factory is a fully managed, serverless service for building ETL and ELT pipelines. It is designed for teams moving and transforming data across cloud, on-premises, hybrid, and multicloud environments. Visual pipeline authoring,…

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. Azure Data FactoryETL Software 8.8Paid
  2. Apache SparkETL Software 8.2Free plan

Strengths and trade-offs

  • Azure Data Factory — where it wins

    • More than 90 built-in connectors across cloud, on-premises, and multicloud sources
    • Visual pipelines, mapping data flows, scheduling, monitoring, and alerting
    • Supports incremental loading, CDC, self-hosted runtimes, and SSIS migration

    Where it doesn't

    • Usage-based pricing varies by region and currency
    • CDC factory resources are documented as public preview
    • Its broad feature set may exceed the needs of simple ETL projects

  • 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
  • Azure Data Factory8.8/10 · Paid

    A visual, hybrid ETL service with broad connectivity and enterprise orchestration.

    Visit siteFull verdict →
  • Apache Spark8.2/10 · Free plan

    A free, self-hosted engine for distributed batch, streaming, SQL, and machine learning workloads.

    Visit Apache SparkFull verdict →

More comparisons

All ETL software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update