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Airflow vs dbt: What are the differences?
Introduction
This post compares Airflow and dbt and highlights the key differences between the two tools.
Scalability: Airflow is a workflow orchestration tool that allows the scheduling and execution of complex workflows, making it highly scalable. On the other hand, dbt is a data transformation tool that focuses on building data transformations for analytics purposes. While dbt can handle large datasets, it is not designed for scaling to the same extent as Airflow.
Flexibility: Airflow provides a flexible platform for building custom workflows using Python, allowing users to create complex pipelines with ease. Additionally, it supports different types of tasks and operators, making it highly versatile. In contrast, dbt is primarily focused on transforming data stored in a database and is less flexible when it comes to building custom workflows.
Architecture: Airflow follows a distributed architecture that enables high availability and fault tolerance. It uses a central scheduler and executor model, allowing multiple workers to execute tasks concurrently. In contrast, dbt follows a more simplistic architecture, with transformations executed in a linear fashion.
Monitoring and Alerting: Airflow provides built-in monitoring and alerting capabilities, allowing users to track the progress of their workflows and receive notifications when issues occur. These features enable better visibility and proactive management of workflows. On the other hand, dbt does not have native monitoring and alerting functionalities, requiring users to rely on external tools to achieve similar capabilities.
Community and Ecosystem: Airflow has a large and active community, with a rich ecosystem of plugins and integrations that extend its functionality. This makes it easy to find support, share knowledge, and leverage existing solutions. While dbt also has a growing community, it may not offer the same breadth of resources and integrations as Airflow.
Purpose: Airflow is primarily focused on orchestrating and scheduling workflows, allowing users to define dependencies and manage complex pipelines. It is widely used in data engineering and data warehousing scenarios. On the other hand, dbt focuses on transforming and modeling data specifically for analytics purposes, providing a cleaner way to manage data transformation pipelines for business intelligence.
In Summary, Airflow is a scalable and flexible workflow orchestration tool with a distributed architecture, monitoring capabilities, and a strong community, while dbt is a data transformation tool with a simpler architecture, primarily focused on analytics data transformations.
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 dbt
- Easy for SQL programmers to learn5
- CI/CD2
- Schedule Jobs2
- Reusable Macro2
- Faster Integrated Testing2
- Modularity, portability, CI/CD, and documentation2
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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 dbt
- Only limited to SQL1
- Cant do complex iterations , list comprehensions etc .1
- People will have have only sql skill set at the end1
- Very bad for people from learning perspective1
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What is Airflow?
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