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Airflow vs Pachyderm: What are the differences?
- 1. Task Scheduling and Orchestration: Airflow is primarily used for task scheduling and orchestration, allowing users to define, schedule, and monitor complex workflows as Directed Acyclic Graphs (DAGs). Pachyderm, on the other hand, focuses on data versioning and data lineage, allowing users to track changes to data over time and reproduce data pipelines.
- 2. Data Versioning and Lineage: Pachyderm is specifically designed for data versioning and lineage, enabling users to track changes to data, reproduce past results, and debug data pipelines by examining metadata associated with each data version. Airflow does not provide built-in functionality for data versioning and lineage.
- 3. Data Storage and Processing: Airflow is agnostic to the backend data storage and processing framework used, allowing users to integrate with various popular storage and processing technologies such as Hadoop, Spark, and SQL databases. Pachyderm, on the other hand, provides its own storage and processing layer built on top of distributed file systems like HDFS or object stores like S3, and is optimized for large-scale data processing tasks.
- 4. Plugin Ecosystem: Airflow has a rich ecosystem of plugins that extend its functionality, allowing users to easily integrate with third-party tools and services for various tasks such as data extraction, transformation, loading, and monitoring. Pachyderm, being more focused on data versioning and lineage, does not have as extensive of a plugin ecosystem.
- 5. Language Support: Airflow allows users to define tasks using Python, making it highly flexible and customizable. Pachyderm, on the other hand, supports task definition using Docker containers, allowing users to execute tasks in any programming language as long as it is containerized.
- 6. Community and Adoption: Airflow has a large and active community of users and contributors, with widespread adoption in the data engineering and data science community. Pachyderm, being a relatively newer project, has a smaller community and adoption compared to Airflow.
In Summary, Airflow is primarily used for task scheduling and orchestration, while Pachyderm focuses on data versioning and lineage. Airflow has a more extensive plugin ecosystem and community adoption, while Pachyderm provides its own storage and processing layer and supports task definition using Docker containers.
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Pachyderm
- Containers3
- Versioning1
- Can run on GCP or AWS1
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Pachyderm
- Recently acquired by HPE, uncertain future.1
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