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Airflow vs Apache Spark: What are the differences?
What is Airflow? A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
What is Apache Spark? Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Airflow can be classified as a tool in the "Workflow Manager" category, while Apache Spark is grouped under "Big Data Tools".
Some of the features offered by Airflow are:
- Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.
- Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
- Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.
On the other hand, Apache Spark provides the following key features:
- Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
- Write applications quickly in Java, Scala or Python
- Combine SQL, streaming, and complex analytics
Airflow and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than Airflow with 12.9K GitHub stars and 4.71K GitHub forks.
According to the StackShare community, Apache Spark has a broader approval, being mentioned in 266 company stacks & 112 developers stacks; compared to Airflow, which is listed in 72 company stacks and 33 developer stacks.
Pros of Airflow
- Features39
- Task Dependency Management12
- Beautiful UI11
- Cluster of workers9
- Extensibility9
- Open source5
- Python4
- Complex workflows3
- K2
- Dashboard2
- Custom operators2
- Good api1
- Apache project1
Pros of Apache Spark
- Open-source58
- Fast and Flexible47
- One platform for every big data problem7
- Easy to install and to use6
- Great for distributed SQL like applications6
- Works well for most Datascience usecases3
- Machine learning libratimery, Streaming in real2
- In memory Computation2
- Interactive Query0
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Cons of Airflow
Cons of Apache Spark
- Speed2
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What is Airflow?
What is Apache Spark?
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