Apache Airflow Airflow is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity. Apache Kafka is an open-source distributed event streaming platform used by many companies to develop high-performance data pipelines, perform streaming analytics and data integration. It is a distributed streaming platform that lets you publish and subscribe to streams of records (similar to a message queue).
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 organise, 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
- Comparing Apache Kafka and Apache Airflow
Introduction to Apache Kafka
Apache Kafka is one of the most popular open-source software that provides users with a framework to store, read, and analyse 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 towards 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.
Introduction to 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.
Airflow Kafka Operator
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 executers, 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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Hevo Data, a No-code Data Pipeline, helps to integrate data from 100+ sources and load it in a data warehouse of your choice to visualize it in your desired BI tool. Hevo is fully-managed, and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using BI tools.
Check out what makes Hevo amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Simplify your data analysis with Hevo today! Sign up here for a 14-day free trial!
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
Apache Airflow Kafka
The following are some of the most critical differences that set Apache Kafka and Airflow apart:
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 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.
Airflow Kafka Operator
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 executers, 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.
Simplify your data analysis with Hevo's No-code Data Pipelines
Hevo Data, a No-code Data Pipeline, helps to integrate data from 100+ sources and load it in a data warehouse of your choice to visualize it in your desired BI tool. Hevo is fully-managed, and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using BI tools.
Check out what makes Hevo amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Simplify your data analysis with Hevo today! Sign up here for a 14-day free trial!
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
Apache Airflow Kafka
The following are some of the most critical differences that set Apache Kafka and Airflow apart:
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 Airlfow platform:
Airflow Apache
- 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 introduced you to two leading task management platforms by Apache and provided you with a comprehensive comparison of Apache Kafka vs Airflow. It provided 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! Atlassian integration. 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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Apache Airflow Kafka Free
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!
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