Apache Airflow vs. Talend Open Studio (discontinued)

Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.

Incentivized

For quick daily integrations Talend is a very good tool and it makes development time so short and easy. Citizen developers who are not great programmers can pick up and start using Talend Open Studio within weeks. It's well suited for all kinds of data migration between various systems. It is less appropriate for smaller synchronous services where you need to trace the complete transaction and how data moved between them. It's also less appropriate for small data movements where other tools can be easier to use and manage.

Incentivized

• Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
• Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
• Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.

Incentivized

• Your developers will be able to design SOA services graphically, and it is very easy to document and implement the code.
• Talend Open Studio is based on Eclipse IDE, so your developers will be very comfy using it
• Open is Key in Talend Open Studio = Open Source

Incentivized

• UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
• Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
• Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.

Incentivized

• The community is not that up to date and forum is not that great in response. Probably we should make people aware of the tool more on how to use and its implementations.
• Talend crashes when transforming a lot of data (millions of rows).
• Proper training documentation is a must for talend which is currently lagging. This will help users to learn more about Talend and use it effectively.

Incentivized

There is no licence requirement for Talend Open Studio. So, this is not relevant question. However, if you are asking whether we will use Talend in future. Yes. We will continue to use it. It's very powerful free tool which caters to all our extra, transform, load capabilities. We just love Talend for it's great functionality and ease of use.

Incentivized

For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.

Incentivized

Talend Open Studio is based on Eclipse and is full of redundant procedures to do one thing, like when installing libraries. Sometimes I cannot manually download the libraries that it can't find.

Many times, Talend freezes. When you give a cancel command, it takes several minutes to stop. It also takes a great toll on our PC with 16 GB of ram and I7 CPU, even in idle status. If you are downloading Maven Jar/Libraries, you cannot do anything and have to wait until the task is finished.

Talend Open Studio is free and we are not using the enterprise version which comes with licence and support. So, mostly depend on the open source community for any issues that we face. The document is good and we didn't have to use any support so far. We did evaluate the enterprise version and so far sticking to the free version.

Incentivized

Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.

Incentivized

Informatica has a limited number of components that you can use. This places a heavy limitation on the capabilities of Informatica. On the other hand, Talend allows you to create your own custom components using Java. For businesses that need to perform a wide variety of data operations, it can be quite useful to have the option of creating your own custom components to satisfy business needs.

Incentivized

• Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
• Donot use it if you have very less usecases

Incentivized

• I delivered projects the client did not believe were possible, and I provided intermediate value by providing visibility to hidden data problems in their systems they could not detect before.
• I was able to work 3 projects at a time, pausing gracefully in one while switching to the other, with minimal effort.

Incentivized