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Advice on Airflow and Azure Data Factory
Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 96.2K views

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Apache Spark
Luigi
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Airflow

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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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 122.7K views
Recommends
Cassandra

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 Azure Data Factory
  • 43
    Features
  • 13
    Task Dependency Management
  • 12
    Beautiful UI
  • 11
    Cluster of workers
  • 10
    Extensibility
  • 5
    Open source
  • 4
    Python
  • 4
    Complex workflows
  • 3
    K
  • 2
    Dashboard
  • 2
    Good api
  • 2
    Custom operators
  • 1
    Apache project
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    Cons of Airflow
    Cons of Azure Data Factory
    • 1
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward
    • 1
      Running it on kubernetes cluster relatively complex
    • 1
      Observability is not great when the DAGs exceed 250
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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 Azure Data Factory?

      It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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      What are some alternatives to Airflow and Azure Data Factory?
      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.
      See all alternatives