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Airflow vs Google Cloud Dataflow: What are the differences?
Airflow and Google Cloud Dataflow are both popular tools for data processing and workflow management. Let's explore the key differences between the two:
Execution Model: Airflow is based on a Directed Acyclic Graph (DAG) model, where users define workflows as a series of tasks and dependencies. Each task is independent and can run on any machine, making it easier to distribute workloads across multiple machines. On the other hand, Google Cloud Dataflow uses a data parallel model, where data is divided into chunks and processed in parallel across a distributed system. This makes it well-suited for large-scale computations and data processing.
Scalability: While both Airflow and Google Cloud Dataflow can scale horizontally to handle increasing workloads, they have different approaches to achieving scalability. Airflow relies on task parallelism, where multiple tasks can be executed simultaneously, while Google Cloud Dataflow leverages data parallelism, which allows processing multiple chunks of data in parallel. This makes Google Cloud Dataflow highly scalable for processing large datasets.
Integration with Cloud Services: Google Cloud Dataflow is tightly integrated with Google Cloud Platform (GCP). It can seamlessly process data from various GCP services like BigQuery, Cloud Storage, and Pub/Sub. It also provides connectors for other cloud and on-premises data sources. On the other hand, Airflow is a more agnostic tool and can integrate with a wide range of services and platforms, including cloud providers like AWS and Azure.
Programming Language Support: Airflow supports a wide range of programming languages, including Python, Java, and SQL, allowing users to write custom functions and tasks in their language of choice. Google Cloud Dataflow primarily supports Java and Python, with limited support for other languages. This difference in language support may influence the choice of tool based on the programming language preferences of the team.
Data Processing Models: Airflow primarily focuses on task orchestration and workflow management, where each task represents a discrete unit of work. It provides a rich set of operators for data ingestion, transformation, and analysis. Google Cloud Dataflow, on the other hand, is specifically designed for large-scale data processing and analytics. It provides advanced data processing capabilities like windowing, streaming, and stateful processing, which may be critical for certain use cases.
Ease of Use and Learning Curve: Airflow offers a web-based UI and a user-friendly interface for creating and managing workflows. It has a relatively shallow learning curve and is easy to use for developers, data engineers, and data scientists. Google Cloud Dataflow, on the other hand, has a steeper learning curve due to its distributed processing nature and the need to write code using the Dataflow SDK. It may require more technical expertise to fully utilize its capabilities.
In summary, Airflow and Google Cloud Dataflow differ in their execution models, scalability approaches, integration with cloud services, programming language support, data processing capabilities, and ease of use.
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
- Features51
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Google Cloud Dataflow
- Unified batch and stream processing7
- Autoscaling5
- Fully managed4
- Throughput Transparency3
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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 Google Cloud Dataflow
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What is Airflow?
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