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Head-to-head · Stream Processing Software

Azure Stream Analytics vs Apache Spark

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Azure Stream Analytics #3 in Stream Processing Software 9.1/10 Paid ✓ 3 of 3 features Visit site
Apache Spark #8 in Stream Processing Software 8.6/10 Free plan Free plan✓ 0 of 3 features Visit Apache Spark

Azure Stream Analytics 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 Stream Analytics · 9.1/10
  • Free planonly Apache Spark
  • Most featuresAzure Stream Analytics · 3 of 3

Azure Stream Analytics scores higher on our rubric for stream processing software: 9.1 against 8.6 out of 10; our editors rank them #3 and #8.

Apache Spark offers free plan; Azure Stream Analytics doesn't.

Azure Stream Analytics is the better fit for azure users preferring managed SQL streaming. Apache Spark is the better fit for mature teams combining batch and streaming.

  • Azure Stream Analytics fits best

    Azure users preferring managed SQL streaming

  • Apache Spark fits best

    Mature teams combining batch and streaming

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Side by side

Feature Azure Stream Analytics 9.1/10 Visit ↗ Apache Spark 8.6/10 Visit ↗
At a glance
Editor score 9.1 8.6
Ranking #3 in Stream Processing Software #8 in Stream Processing Software
Best for Azure users preferring managed SQL streaming Mature teams combining batch and streaming
Pricing model Paid Free
Starting price — Free plan
Free plan — ✓ (best)
Free trial — —
Deployment Cloud, Self-hosted Self-hosted
Platforms Web Windows, macOS, Linux
Support Docs Community, Docs
Integrations 6 integrations 20 integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Azure Stream Analytics 3/3 · Apache Spark 0/3
SQL processing ✓ (best) Not checked yet
Stateful processing ✓ (best) Not checked yet
Event-time windows ✓ (best) Not checked yet
Specs
Deployment model Hybrid Not checked yet
Supported languages SQL, JavaScript, C# Not checked yet
Source connectors 5 Not checked yet
Our review
Pros
  • SQL windows, event-time handling, stateful aggregates, and temporal joins
  • Native adapters for Event Hubs, IoT Hub, Storage, Kafka, and Azure SQL
  • Runs in Azure or on Azure IoT Edge devices
  • Structured Streaming supports incremental stream processing
  • Broad APIs across Python, SQL, Scala, Java, and R
  • Connects to major data sources, clusters, and lakehouse systems
Cons
  • No product-level free plan
  • Dedicated tiers require at least 12 or 36 streaming units
  • Best fit depends on Azure services and ecosystem alignment
  • Self-hosted deployment requires cluster infrastructure
  • Streaming is one part of a broader batch-focused platform
  • Not a hosted ETL SaaS product with subscription tiers
Our verdict

Azure Stream Analytics is a managed service for processing streaming event data in real time. It is designed for teams that want SQL-based transformations, aggregations, joins, and analysis without managing a stream-processing cluster.…

Read the review →

Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. It is aimed at developers, data engineers, analysts, and organizations processing data across clusters. Structured Streaming…

Read the review →
  1. Azure Stream AnalyticsStream Processing Software 9.1Paid
  2. Apache SparkStream Processing Software 8.6Free plan

Strengths and trade-offs

  • Azure Stream Analytics — where it wins

    • SQL windows, event-time handling, stateful aggregates, and temporal joins
    • Native adapters for Event Hubs, IoT Hub, Storage, Kafka, and Azure SQL
    • Runs in Azure or on Azure IoT Edge devices

    Where it doesn't

    • No product-level free plan
    • Dedicated tiers require at least 12 or 36 streaming units
    • Best fit depends on Azure services and ecosystem alignment

  • Apache Spark — where it wins

    • Structured Streaming supports incremental stream processing
    • Broad APIs across Python, SQL, Scala, Java, and R
    • Connects to major data sources, clusters, and lakehouse systems

    Where it doesn't

    • Self-hosted deployment requires cluster infrastructure
    • Streaming is one part of a broader batch-focused platform
    • Not a hosted ETL SaaS product with subscription tiers

More comparisons

All stream processing software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update