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Airflow vs Talend: What are the differences?

Introduction

Airflow and Talend are both popular tools used in data integration and processing. While they share some similarities, there are also key differences that set them apart.

Key Differences between Airflow and Talend

  1. Architecture and Design Approach: Airflow follows a task-oriented approach where workflows are defined as a directed acyclic graph (DAG). It focuses on managing dependencies between tasks and offers a flexible and scalable architecture. On the other hand, Talend adopts a more traditional ETL (Extract, Transform, Load) approach with a visual-based design. It provides a drag-and-drop interface for building data integration jobs.

  2. Extensibility and Customization: Airflow offers a rich set of pre-built operators that can be used out of the box, and users can also create their own custom operators for specific use cases. It supports a variety of integrations with external systems and services. Talend, on the other hand, provides a wide range of connectors and components that can be used to integrate with different data sources and perform various transformations. It also supports custom code snippets and user-defined functions.

  3. Scalability and Performance: Airflow is designed to handle large-scale workflows and provides features like parallel execution, task retries, and distributed scheduling. It can be easily scaled horizontally to meet increasing data processing demands. Talend, on the other hand, leverages native push-down optimization techniques and parallel processing to improve performance. It allows for data partitioning, parallel execution, and caching to optimize the execution of data integration jobs.

  4. Monitoring and Alerting: Airflow comes with built-in monitoring capabilities that provide visibility into task execution, workflow status, and system resources. It also supports integration with external monitoring tools like Prometheus and Grafana. Talend provides a comprehensive monitoring and auditing framework that allows users to track job status, performance metrics, and error logs. It also supports email notifications and integration with external monitoring systems.

  5. Community and Ecosystem: Airflow has a strong open-source community and a large ecosystem of plugins and extensions. It offers a marketplace for sharing and discovering reusable workflows and components. Talend also has a vibrant community and provides an extensive library of connectors and components for various data sources and systems. It offers a marketplace for sharing and reusing integration jobs and provides enterprise support and training services.

  6. Deployment and Management: Airflow can be deployed on-premises or in the cloud, and it supports various deployment options like standalone mode, distributed mode, and Kubernetes. It provides features for managing authentication, access control, and high availability. Talend can be deployed on-premises or in the cloud and supports different deployment models like standalone, distributed, and cloud-native. It provides a centralized management console for deploying, monitoring, and managing data integration jobs.

In summary, Airflow and Talend have distinct architectural approaches, extensibility options, scalability and performance optimizations, monitoring capabilities, community support, and deployment models that cater to different data integration and processing needs.

Advice on Airflow and Talend
karunakaran karthikeyan
Needs advice
on
DremioDremio
and
TalendTalend

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

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Replies (1)
Rod Beecham
Partnering Lead at Zetaris · | 3 upvotes · 62.6K views
Recommends
on
DremioDremio

Hi Karunakaran. I obviously have an interest here, as I work for the company, but the problem you are describing is one that Zetaris can solve. Talend is a good ETL product, and Dremio is a good data virtualization product, but the problem you are describing best fits a tool that can combine the five styles of data integration (bulk/batch data movement, data replication/data synchronization, message-oriented movement of data, data virtualization, and stream data integration). I may be wrong, but Zetaris is, to the best of my knowledge, the only product in the world that can do this. Zetaris is not a dashboarding tool - you would need to combine us with Tableau or Qlik or PowerBI (or whatever) - but Zetaris can consolidate data from any source and any location (structured, unstructured, on-prem or in the cloud) in real time to allow clients a consolidated view of whatever they want whenever they want it. Please take a look at www.zetaris.com for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.

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Needs advice
on
AirflowAirflowLuigiLuigi
and
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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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 259.2K views
Recommends
on
CassandraCassandra

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 Talend
  • 51
    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
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    Cons of Airflow
    Cons of Talend
    • 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
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      Sign up to add or upvote consMake informed product decisions

      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 Talend?

      It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.

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