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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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Apache SparkApache Spark

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.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 279.6K views
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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

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Pros of Airflow
Pros of dbt
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
    Dashboard
  • 5
    Easy for SQL programmers to learn
  • 2
    CI/CD
  • 2
    Schedule Jobs
  • 2
    Reusable Macro
  • 2
    Faster Integrated Testing
  • 2
    Modularity, portability, CI/CD, and documentation

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Cons of Airflow
Cons of dbt
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
  • 1
    Only limited to SQL
  • 1
    Cant do complex iterations , list comprehensions etc .
  • 1
    People will have have only sql skill set at the end
  • 1
    Very bad for people from learning perspective

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What is Airflow?

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 dbt?

dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.

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What companies use Airflow?
What companies use dbt?
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What tools integrate with Airflow?
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Blog Posts

What are some alternatives to Airflow and dbt?
Luigi
It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
Jenkins
In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
AWS Step Functions
AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
Pachyderm
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
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