Apache Airflow Reviews

In my company, we use Apache Airflow as an orchestrator because we have a lot of business use cases that involve the automation of people's jobs. For example, if someone takes a file and then moves the file from one folder to another, and we have a lot of scripts to do this in PL/SQL or bash pipelines, we decide to move all of this to be orchestrated through one hub application. Instead of having a few things on the database from Oracle while a few things run on local machines, in our company, we wanted this all to be orchestrated through one thing, which is why we chose Apache Airflow.

I like that Apache Airflow is in Python language, making it easy to use and learn. I like Apache Airflow's versatility. Essentially, if you want to do something, there is generally a webhook that you can use with Apache AirFlow, especially if you use solutions from big companies like Google or Microsoft. Many providers are not from Apache since, with Apache Airflow, it is very easy to develop and integrate applications from various developers.

The only thing I would like Apache to do is to introduce an integration of the database from Oracle because it currently supports Postgres primarily in MySQL. Oracle is something that many companies use, like a production database, for which you have to pay since it is not free and offers more extended support. With Apache Airflow, even though it uses Python and Python has modules that include Oracle databases, it'll be safer and more convenient to do it through Apache Airflow and not through Python scripts. I want to see Apache Airflow have more integrations with more production-based databases since it is an area where the product lacks currently.

I have been using Apache Airflow for a year and a half. Our company has a production environment for Apache Airflow since we are familiarizing ourselves with the product currently. I use Apache Airflow Version 2.6.1, which is the most stable one. Regarding Apache Airflow, I don't know if any recent updates were released. I am an end-user of Apache Airflow.

Considering that in my company, we have Apache Airflow deployed on-premises, I rate the stability of the product a ten out of ten since I haven't seen any issues. If Apache Airflow had been deployed on the cloud, then it wouldn't have been very stable.

I rate Apache Airflow's scalability an eight out of ten because you can scale it however you want since it is in Python. There are some limitations in Apache Airflow. Airflow does not process or hold any data, so if you have many scripts running on this tool, then even small variables stored in the database will eventually overflow the database. By design, Airflow is scalable up to a certain point, but I don't imagine anyone will reach that point. The product's scalability has some limitations, so I cannot give it a ten out of ten, though I think it is pretty much a perfect tool.

I use Apache Airflow daily in my company.

My company had directly contacted the technical support team of Apache, but I used Apache's GitHub Pages, along with its documentation, which was very thorough and helpful. Considering the documentation and stuff Apache provides online as support, I would give it ten out of ten, even though I have not personally spoken to Apache's support team.

Positive

The initial setup is simple. Using pip, you type in the version of the Airflow and download it, which is very convenient.

The solution is deployed on an on-premises model.

The solution can be deployed in a couple of minutes.

Almost ten RPA DevOps engineers in my organization use Apache Airflow.

As far as I know, Apache Airflow is a product that is free of licenses, meaning there is no need to buy a license.

Apache Airflow recently introduced a new way of writing scripts, and I quite like it. It's very convenient to write it in taskflow instead of using it with clauses that Python has, so Apache is improving the technology of Airflow as Python improves.

Apache Airflow does require maintenance.

For Apache Airflow, two engineers are involved in the maintenance phase. As we get more servers in our company, we expect the number of people involved in the maintenance phase to increase from two to five.

I recommend the solution to those requiring an orchestrator to manage all of their different scripts. Considering that Apache Airflow is in Python, you don't need to rewrite anything since all you need to do is write a short script in Python that will execute the scripts you already have in bash or PSQL or whatever you want. If someone needs an orchestrator, Apache Airflow is a perfect product.

I rate the overall product a nine out of ten.

Apache Airflow improved workflow efficiency, but we had to find solutions for large workflows. For instance, a monthly workflow with 1200 jobs had to be split into three to four pieces as it struggled with large job numbers. Loading a workflow with 500 jobs could take 10 minutes, which wasn't acceptable.

The most important feature Apache Airflow lacks is support for external configuration files. All classical schedulers like Control-M or Automic allow you to load workflow definitions from YAML, XML, or JSON files, but the tool requires you to write Python programs. Airflow only supports external configuration for variables, not for workflows. To address this, I created a YAML configuration file that I converted into Python programs, but this functionality is missing from Apache Airflow itself.

All of its competitors have this feature. In Control-M, Automic, and IBM's scheduler, you can load workflows from XML, JSON, or YAML files.

I've been familiar with Apache Airflow for about three to four years. I worked on a project at a leading German bank for two years, successfully migrating large applications with hundreds of jobs. However, the leading German bank paused its migration strategy due to issues with the team in India. They're likely waiting for version 3, which is expected next year.

I rate the tool's stability a nine out of ten. 

I rate the product's scalability a seven out of ten. 

Apache Airflow doesn't have its own technical support.

I've been involved in all aspects of Airflow deployment, including building infrastructure using Kubernetes and containers. We faced challenges migrating from enterprise schedulers like Control-M and IBM's scheduler to Airflow, as it lacked some functionality. I had to implement extra features and extensions to support things like individual calendars.

Apache Airflow is open-source and free. Hyperscalers like Google (with Composer), Azure, and AWS offer managed Airflow services.

I recommend Apache Airflow because it's open-source, but you must accept its limitations. However, I wouldn't recommend it to companies in biomedical, chemistry, or oil and gas industries with large workflows and thousands of jobs. For example, genomic analysis at an American multinational pharmaceutical and biotechnology corporation involved workflows with around twenty thousand jobs, which Airflow can't handle. Special schedulers are needed for such cases, as even classical schedulers like Control-M and Automic aren't suitable.

I rate the overall solution a seven out of ten. 

I have used Apache Airflow for various purposes, such as orchestrating Spark jobs, EMR clusters, Glue jobs, and submitting jobs within the DCP data flow on Azure Databricks including ad hoc queries. For instance, if there's a need to execute queries on Redshift or other databases.

If you are working with APIs or databases, you must write SQL queries and formulate the right statements to retrieve everything. But with the UI, it's more like plug-and-play. You go there, select the task you want to see, like logs, and click on it. It will promptly display the details of the logs, automatically showing the returned logs. However, if you're accessing logs manually from the web server, you must write commands and perform additional tasks. These overheads can be efficiently managed using the UI.

Apache Airflow is easy to use and can monitor task execution easily. For instance, when performing setup tasks, you can conveniently view the logs without delving into the job details. All logs are readily accessible within the interface itself. Examining the logs lets you discern which steps and processes are being executed.

You don't have to configure SMTP for everything. You need to configure email settings, such as email on error, failure, or alert access. With Apache Airflow, you can send emails with just a few lines of code. You don't have to write extensive code to configure SMTP; all those configurations can be accomplished within a few lines of code.

I managed a complex workflow for a finance application project. They use Apache Airflow to orchestrate processes, such as retrieving data from SFTP and landing it into S3. From S3, they trigger Glue jobs based on certain conditions. Additionally, they use the Glue catalog in Glusoft for data management, all orchestrated using Airflow. Furthermore, various logics are written in Airflow DAGs to handle scenarios like security mismatches. For instance, files are sent accordingly if there's a missing security.

Apache Airflow triggers a set of tasks based on DAGs. If you have multiple tags, such as raw, transform, and ready layers, instead of manually triggering each DAGs. In that case, you can integrate them to trigger one, automatically triggering the others. Also, you can put conditions.

Airflow should support the dynamic drag creation.

I have been using Apache Airflow for over 8 years.

The solution is stable. 

I rate the solution's stability a nine-point five out of ten.

We were using Apache Airflow on Kubernetes. As more requests came in, it scaled dynamically based on the available ports. There are almost 15 data engineers who are using Apache Airflow.

I rate the solution's scalability a nine out of ten.

The initial setup is straightforward. It will be tricky if you go with an executor or Kubernetes operator.

If you're into plug-and-play convenience, Apache Airflow supports various deployment methods like Docker, Helm, or Kubernetes. If you want to spin up Airflow, it will take more than 10-15 minutes. However, if you're making customizations or prefer not to use existing databases, the setup time could be extended due to customization requests.

You use Apache Airflow to automate your data pipelines. When you have a data pipeline, such as a Spark job or any other job, and want to automate it, triggering the job manually is not always necessary. You need to configure these DAGs accordingly. For instance, Airflow can initiate the job when the data becomes available. We don't need to keep the cluster running all the time, 24/7. We start the cluster using Airflow when we need to submit the job. Once the job is completed, we terminate the cluster.

I recommend the solution.

Overall, I rate the solution a nine out of ten.

Since the solution is programmatic, it allows users to define pipelines in code rather than drag and drop.

There is an area for improvement in onboarding new people. They should make it simple for newcomers. Else, we have to put a senior engineer to operate it.

I have been using Apache Airflow for five years. We are using the latest version of the solution.

I rate the solution’s stability a seven out of ten.

The scalability is good. We have five people working on it for five different projects.

I rate the solution’s scalability a ten out of ten.

We have used open-source Apache NiFi for data flow, Talend, and secret server integration services.

We chose Apache Airflow because it is quite popular, adopted by many people, and has an open-source community and engineers. We moved with the crowd and chose it based on popularity.

I rate the initial setup five on a scale of one to ten, one being difficult and ten being easy.

The deployment required a senior engineer and took a week to complete.

The solution is cheap.

A new user has to be prepared to adopt a new paradigm and treat the data baseline as code rather than drag and drop. An organization should have a dedicated or experienced person looking into this.

Overall, I rate the solution an eight out of ten.

It serves as a versatile tool for data ingestion, enabling various tasks including data transformation from one type or format to another. It facilitates seamless preparation and processing of data, supporting diverse operations such as format conversion, type transformation, and other related functions.

We leverage Apache Airflow to orchestrate our data pipelines, primarily due to the multitude of data sources we manage. These sources vary in nature, with some delivering streaming data, while others follow different protocols such as FTP or utilize landing areas. We utilize Airflow for orchestrating tasks such as data ingestion, transformation, and preparation, ensuring that the data is formatted appropriately for further processing. Typically, this involves tasks like normalization, enrichment, and structuring the data for consumption by tools like Spark or other similar platforms in our ecosystem.

The scheduling and monitoring functionalities enhance our data processing workflows. While the interface could be more user-friendly, proficiency in scheduling and monitoring can be attained through practice and skill development.

The scalability of Apache Airflow effectively accommodates our increasing data processing demands without issue. While occasional server problems may arise, particularly in this aspect, overall, the product remains reliably stable.

It offers a straightforward means to orchestrate numerous data sources efficiently, thanks to its user-friendly interface. Learning to use it is relatively quick and straightforward, although some experimentation, practice, and training may be required to master certain aspects.

Our data workflow management is greatly streamlined by the use of Apache Airflow, which proves highly beneficial. Its user-friendly interface makes it straightforward to operate, offering a plethora of features for data preparation, buffering, and format conversion. With its extensive capabilities, Airflow serves as a comprehensive tool for managing our data workflows effectively.

The current pricing of Apache Airflow is considerably higher than anticipated, catching us off guard as it has evolved from its initial pricing structure. It would be beneficial to improve the pricing structure. Also, enhancing the interface furthermore would be highly beneficial.

We have been using it for approximately two years.

While the stability of the system is satisfactory, maintaining stability requires vigilance and attention to various factors. During usage, occasional issues may arise, particularly when operating on-premises configurations. For instance, a single hard disk failure on a physical node can pose a challenge, necessitating the node's shutdown for disk replacement. However, the process of switching off and on the node is intricate and requires careful handling.

Scalability is achievable, but it comes with its challenges, particularly in terms of temporary downsizing due to failures or other unforeseen circumstances. While scaling up is feasible, each additional node introduced into the cluster adds complexity and raises the likelihood of potential failures. Dealing with failures involves following standard procedures, yet reinstating the cluster to its fully operational state can be a demanding task.

Approximately ten technical staff members and an equivalent number of data scientists utilize the platform. Additionally, a segment of the network team employs it for network quality analysis, leveraging reporting tools built on top of Impala, which is integrated into the cluster.

The setup process is notably intricate, particularly considering our cluster configuration consisting of twelve data nodes and various additional components. Furthermore, unforeseen issues may arise, such as disk space constraints for Airflow or similar challenges, necessitating vigilance and attention to detail to avoid complications.

The initial phase of the deployment process involves creating a comprehensive plan outlining the setup of our cluster, considering all nodes involved. Since we're deploying on-premises, we need to determine which components will reside on physical machines and which can be accommodated on virtual machines or clusters. This assessment will guide the allocation of resources to each server, ensuring an optimal configuration. Following this, the configuration phase begins, taking into account the specific requirements of our organization and stringent security measures. Access to the clusters must be carefully managed, categorized, and restricted as per security protocols. It's imperative to prepare everything meticulously prior to deployment to ensure a smooth and successful implementation. We've undertaken the deployment process partially in-house and with the assistance of the system integrator dedicated to this project. For maintenance and deployment tasks, we rely on a team of ten technical personnel. Typically, only two or three individuals are needed to monitor operations and address issues as they arise. Moreover, we have the backing of our system integrator for additional support if necessary.

The pricing is on the higher side.

I would confidently recommend Apache Airflow to others, assuring them of its benefits. In my opinion, it's a mature and efficient product that delivers reliable performance. Overall, I would rate it nine out of ten.

We use the product for scheduling and defining workflows. It helps us extensively to manage complex workflows within Cloudera's ecosystem, particularly for handling and processing data.

The solution has been beneficial in automating and managing our data workflows efficiently. It has integrated well with our Cloudera environment, enabling us to handle complex workflows with greater ease and reliability.

The solution's most valuable feature is its ability to run workflows without saving changes. It allows us to execute tasks without permanently altering our configurations, which is useful for temporary adjustments and testing.

One area for improvement would be to address specific functionalities removed in recent updates that were previously useful for our operations.

Additional features that could enhance the product include more flexibility in parameterization and improved tools for managing and debugging workflows.

I have been working with Airflow for approximately a year and a half, focusing on the current version for the past eight months.

The product has been stable in our environment.

The product is scalable. 

The technical support team has been responsive and helpful. They addressed issues related to removed functionalities and ensured critical features were restored in subsequent updates.

Positive

We previously used Hortonworks but switched to Cloudera CDP. We also used other Cloudera tools but found Airflow to be a better fit for our current needs due to its capabilities in workflow management.

The initial setup was complex due to the integration with various data sources and configuration requirements, but once properly set up, it has proven effective.

The implementation was carried out with guidance from Cloudera's support team, who provided valuable assistance in configuring the solution to meet our requirements.

We evaluated other data workflow solutions but found Airflow the most suitable due to its integration with Cloudera and comprehensive feature set for managing complex workflows.

Airflow integrates well with Cloudera and effectively supports complex operations. However, users should be aware of changes in functionality between versions and plan accordingly.

Overall, I rate it a nine out of ten. 

The main use case is orchestration. We use it to schedule our jobs.

The best thing about the product is its UI. The tool is user-friendly. We can divide our work into different tasks and groups. It gives a graphical representation of the whole flow. It also creates a graph of the complete pipeline. The UI is beautiful. Whenever there is a failure, we can see it at the backend. We can retry at the point where the failure happened. We do not have to redo the whole flow. The user interface is pretty good. It provides details about the jobs. It also provides monitoring features. We can see the metrics and the history of the runs. The administration features are good. We can manage the users.

The solution lacks certain features. We cannot run real-time jobs in the solution. It supports only batch jobs. If we are using ETL pipelines, it can either be a batch job or a real-time job. Real-time jobs run continuously. They are not scheduled. Apache Airflow is for scheduled jobs, not real-time jobs. It would be a good improvement if the solution could run real-time jobs. Many connectors are available in the product, but some are still missing. We have to build a custom connector if it is not available. The solution must have more in-built connectors for improved functionality.

I have been using the solution for four to five years.

The tool has stability issues that are present in open-source products. It has some failures or bugs sometimes. It is difficult to troubleshoot because we do not have any support for it. We have to search the community to get answers. It would be good if there were a support team for the tool.

We have 5000 to 10,000 users in our organization.

The installation is relatively easy. It doesn't have much configuration. It is straightforward. Some companies provide custom installations. It is easier, but it will be a costly paid service. We generally use the core product. We also have AWS Managed Services. It is a better option if we do not want to do the configuration ourselves.

Apache Airflow is a better option for batch jobs. My advice depends on the tools people use and the jobs they schedule. Databricks has its own scheduler. If someone is using Databricks, a separate tool for scheduling would be useless. They can schedule the jobs through Databricks.

Apache Airflow is a good option if someone is not using third-party tools to run the jobs. When we use APIs to get data or get data from RDBMS systems, we can use Apache Airflow. If we use third-party vendors, using the in-built scheduler is better than Apache Airflow. Overall, I rate the solution a nine out of ten.

We utilize Apache Airflow for two primary purposes. Firstly, it serves as the tool for ingesting data from the source system application into our data warehouse. Secondly, it plays a crucial role in our ETL pipeline. After extracting data, it facilitates the transformation process and subsequently loads the transformed data into the designated target tables.

One of its most valuable features is the graphical user interface, providing a visual representation of the pipeline status, successes, failures, and informative developer messages. This graphical interface greatly enhances the user experience by offering clear insights into the pipeline's status.

Enhancements become necessary when scaling it up from a few thousand workflows to a more extensive scale of five thousand or ten thousand workflows. At this point, resource management and threading, become critical aspects. This involves optimizing the utilization of resources and threading within the Kubernetes VM ecosystem.

I have been working with it for five years.

I would rate its stability capabilities nine out of ten.

While it operates smoothly with up to fifteen hundred pipelines, scaling beyond that becomes challenging. The performance tends to drop when dealing with five thousand pipelines or more, leading to the rating of five out of ten.

I would rate the customer service and support nine out of ten.

Positive

The initial setup is straightforward. I would rate it nine out of ten.

The deployment process requires approximately four hours, and the level of involvement from individuals depends on the quantity of pipelines intended for deployment.

The cost is quite affordable. I would rate it two out of ten.

If you have around two thousand pipelines to execute daily within an eight to nine-hour window, Apache Airflow proves to be an excellent solution. I would rate it nine out of ten.