Modern-day companies carry out numerous tasks daily to manage their products, offerings and hence keep their business up & running and profitable in the market. In today’s data-driven world of cut-throat competition, creating, executing, and monitoring different tasks and large volumes of data is no small feat. Most companies, hence need an automated solution, that will help them manage their daily tasks.
Apache Kafka and Airflow are two such open-source task management platforms that help companies create seamlessly functioning workflows to organize, execute and monitor their tasks. Although these platforms seem to perform related tasks, some crucial differences between the two set up them apart.
This article aims at introducing you to these industry-leading platforms by Apache and providing you with an in-depth comparison of Apache Kafka vs Airflow, focussing on their features, use cases, integration support, and pros & cons of both platforms.
Table of Contents
What is Apache Kafka?
Apache Kafka is one of the most popular open-source software that provides users with a framework to store, read, and analyze streaming data. Being open-source, it is available free of cost to users and, hence it houses a broad network of developers & users that help contribute to new features, updates, support functionalities, etc.
Apache Kafka runs on a distributed environment that makes use of multiple servers, allowing it to leverage the processing power and storage capabilities of numerous systems. Its distributed nature and streamlined mechanism of managing incoming data, make it one of the most robust tools that a business can rely upon to carry out real-time data analysis.
For further information on Apache Kafka, you can check the official website here.
What is Apache Airflow?
Apache Airflow is a robust platform that allows users to automate tasks with the help of scripts. It makes use of a scheduler that helps execute numerous jobs with the help of an array of workers while following a set of specified dependencies. Apache Airflow houses rich command-line utilities that allow users to work with DAGs, that help companies order and manage their tasks with ease.
It also has a rich user interface that makes it easy to monitor progress, visualize pipelines, and troubleshoot issues when necessary.
Some key features of Apache Airflow
- Dynamic: Apache Airflow allows you to develop data pipelines dynamically by writing configuration code in Python.
- Extensible: With Apache Airflow, you can define executors, operators and extend your libraries to match the level of abstraction suitable for your business needs.
- Elegant: Apache Airflow houses a Jinja templating engine, that allows users to parameterize configuration scripts and hence create lean & explicit data pipelines.
For further information on Apache Airflow, you can check the official website here.
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Prerequisites
- Working knowledge of Apache Kafka.
- Working knowledge of Apache Airflow.
- A general idea about ETL, data pipelines, etc.
Comparing Apache Kafka and Apache Airflow
Apache Kafka and Airflow are some of the best-in-class open-source platforms available in today’s market that help companies simplify the job of managing large volumes of data and numerous tasks daily. It allows them to not only create and execute but also monitor their tasks programmatically in an automated manner.
While it may seem that both platforms perform the same task, in reality, they have a lot of differences as well that make them unique from one another. You can learn more about these from the following sections:
Apache Kafka vs Airflow: Some Critical Differences
The following are some of the most critical differences that set Apache Kafka and Airflow apart:
| Apache Kafka | Apache Airflow |
| Apache Kafka is a messaging platform that uses a publish-subscribe mechanism, operating as a distributed commit log. | It is a platform that helps programmatically create, schedule and monitor robust data pipelines. |
| It allows you to monitor messages, keep track of errors, and helps you manage logs with ease. | It provides robust support for carrying out operations such as ETL (Extract, Transform and Load), data pipelining and workflow automation. |
| The Apache Kafka Manager only supports integrating with Apache Kafka. | It supports integration with tools like Couler and HostedMetrics. |
| Various companies such as Yahoo, Kaidee, DNSFilter, XYZ Technology, IgnitionOne, Veloxity Inc, etc. have Apache Kafka in place. | Various companies such as Slack, Robinhood, Freetrade, 9GAG, Square, Walmart, etc. have Apache Airflow in place. |
Apache Kafka vs Airflow: Disadvantages of Apache Kafka
The following are some of the disadvantages of the Apache Kafka platform:
- Apache Kafka doesn’t provide support for wildcard topic selection. It only allows you to match the exact topic name.
- Apache Kafka doesn’t house a complete set of monitoring tools by default.
- Users often face numerous issues associated with the tweaking of messages, resulting in the performance reducing significantly.
- Apache Kafka doesn’t allow using message paradigms such as request/reply, point-to-point queues, etc.
Apache Kafka vs Airflow: Disadvantages of Apache Airflow
The following are some of the disadvantages of the Apache Airflow platform:
- Apache Airflow has a very high learning curve and, hence it is often challenging for users, especially beginners, to adjust to the environment and perform tasks such as creating test cases for data pipelines that handle raw data, etc.
- Apache Airflow requires you to rename your DAGs, every time you make a change to your schedule intervals, to ensure that your previous task instances align with the new time interval.
- Apache Airflow doesn’t house a version control mechanism for its data pipelines and, hence whenever you decide to delete a task from your DAG code and then redeploy it, all the metadata associated with the operation is removed by default.
Conclusion
This article introduces you to two leading task management platforms by Apache and provides you with a comprehensive comparison of Apache Kafka vs Airflow. It provides in-depth knowledge about their features, use cases, integration support, their disadvantages, etc. to help you make the right choice for your business. If you’re looking for an all-in-one solution, that will not only help you transfer data but also transform it into analysis-ready form, then Hevo Data is the right choice for you! It will take care of all your analytics needs in a completely automated manner, allowing you to focus on key business activities.
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Tell us about your experience of going through our in-depth comparison of Apache Kafka vs Airflow. Let us know in the comments section below!
Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects. He has written more than 150+ blogs on databases, processes, and tutorials that help data practitioners solve their day-to-day problems.
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