Apache Airflow vs. Talend Data Integration

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Airflow
Score 8.7 out of 10
N/A
Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.N/A
Talend Data Integration
Score 8.9 out of 10
N/A
The Talend Integration Suite, from Talend, is a set of tools for data integration.N/A
Pricing
Apache AirflowTalend Data Integration
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache AirflowTalend Data Integration
Free Trial
NoNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Features
Apache AirflowTalend Data Integration
Workload Automation
Comparison of Workload Automation features of Product A and Product B
Apache Airflow
9.8
10 Ratings
17% above category average
Talend Data Integration
-
Ratings
Multi-platform scheduling9.910 Ratings00 Ratings
Central monitoring9.910 Ratings00 Ratings
Logging9.910 Ratings00 Ratings
Alerts and notifications9.910 Ratings00 Ratings
Analysis and visualization9.910 Ratings00 Ratings
Application integration9.010 Ratings00 Ratings
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Airflow
-
Ratings
Talend Data Integration
9.5
10 Ratings
13% above category average
Connect to traditional data sources00 Ratings10.010 Ratings
Connecto to Big Data and NoSQL00 Ratings9.09 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Airflow
-
Ratings
Talend Data Integration
9.0
10 Ratings
10% above category average
Simple transformations00 Ratings9.010 Ratings
Complex transformations00 Ratings9.010 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Airflow
-
Ratings
Talend Data Integration
9.0
10 Ratings
13% above category average
Data model creation00 Ratings9.09 Ratings
Metadata management00 Ratings10.09 Ratings
Business rules and workflow00 Ratings8.08 Ratings
Collaboration00 Ratings9.09 Ratings
Testing and debugging00 Ratings9.010 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Airflow
-
Ratings
Talend Data Integration
8.5
9 Ratings
4% above category average
Integration with data quality tools00 Ratings7.09 Ratings
Integration with MDM tools00 Ratings10.09 Ratings
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Apache AirflowTalend Data Integration
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User Ratings
Apache AirflowTalend Data Integration
Likelihood to Recommend
9.0
(10 ratings)
9.9
(19 ratings)
Usability
10.0
(1 ratings)
9.0
(2 ratings)
Support Rating
-
(0 ratings)
6.6
(4 ratings)
User Testimonials
Apache AirflowTalend Data Integration
Likelihood to Recommend
Apache
For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.
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Qlik
This tool fits all kinds of organizations and helps to integrate data between many applications. We can use this tool as data integration is a key feature for all organizations. It is also available in the cloud, which makes the integration more seamless. The firm can opt for the required tools when there are no data integration needs.
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Pros
Apache
  • In charge of the ETL processes.
  • As there is no incoming or outgoing data, we may handle the scheduling of tasks as code and avoid the requirement for monitoring.
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Qlik
  • Talend Data Integration allows us to quickly build data integrations without a tremendous amount of custom coding (some Java and JavaScript knowledge is still required).
  • I like the UI and it's very intuitive. Jobs are visual, allowing the team members to see the flow of the data, without having to read through the Java code that is generated.
  • Dynamically table creation from new source.
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Cons
Apache
  • they should bring in some time based scheduling too not only event based
  • they do not store the metadata due to which we are not able to analyze the workflows
  • they only support python as of now for scripted pipeline writing
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Qlik
  • Pricing for sure can be the area for improvement.
  • Real time processing is slow as compared to other tools like Abinitio.
  • While developing batches, it crashes a lot. It may be the issue with me, but I wanted to highlight it.
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Usability
Apache
Easy to learn Easy to use Robust workflow orchestration framework Good in dependent job management
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Qlik
We use Talend Data Integration day in and day out. It is the best and easiest tool to jump on to and use. We can build a basic integration super-fast. We could build basic integrations as fast as within the hour. It is also easy to build transformations and use Java to perform some operations.
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Support Rating
Apache
No answers on this topic
Qlik
Good support, specially when it relates to PROD environment. The support team has access to the product development team. Things are internally escalated to development team if there is a bug encountered. This helps the customer to get quick fix or patch designed for problem exceptions. I have also seen support showing their willingness to help develop custom connector for a newly available cloud based big data solution
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Alternatives Considered
Apache
There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.
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Qlik
In comparison with the other ETLs I used, Talend is more flexible than Data Services (where you cannot create complex commands). It is similar to Datastage speaking about commands and interfaces. It is more user-friendly than ODI, which has a metadata point of view on its own, while Talend is more classic. It has both on-prem and cloud approaches, while Matillion is only cloud-based.
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Return on Investment
Apache
  • A lot of helpful features out-of-the-box, such as the DAG visualizations and task trees
  • Allowed us to implement complex data pipelines easily and at a relatively low cost
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Qlik
  • It’s only been a positive RoI with Talend given we’ve interfaced large datasets between critical on-Prem and cloud-native apps to efficiently run our business operations.
  • 40K+ plots data, covering 1K+ crop varieties.
  • 3K+ Customer & their credit data, 3K+ product inventory & pricing.
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