Apache Airflow vs Apache Spark
On the evidence we track, Apache Airflow leads this comparison with a composite score of 73/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for data engineering tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Apache Airflow
LeaderA community-built orchestration system that enables users to define data pipelines and tasks in Python, execute them on distributed workers, and monitor progress through a web-based interface. It supports dynamic pipeline generation, extensive third-party integrations, and operates best for batch-oriented workflows.
Apache Spark
Apache Spark is an open-source, multi-language distributed computing engine that unifies data engineering, data science, and machine learning workloads. It processes data at scale using batch or streaming paradigms, provides SQL query capabilities for analytics, and includes built-in libraries for machine learning and graph processing.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
- Kubernetes
- Docker
Apache Airflow
Leader- Amazon Web Services
- Microsoft Azure
- Google Cloud Platform
- Databricks
- Snowflake
- dbt Cloud
- Slack
- Salesforce
- Tableau
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Apache Spark
- Apache Kafka
- Apache Cassandra
- Apache Flink
- Apache Druid
- Elasticsearch
- Datadog
- Jenkins
- GitHub
- OpenAI
- Anthropic
- +24 more
Apache Spark
- Hadoop
- HDFS
- YARN
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.