All ETL tool comparison

Apache NiFi vs Airflow vs Airbyte

This article will explore the key differences and comparisons between Apache NiFi vs Airflow vs Airbyte.

Check the comparison spreadsheet
Apache NiFi
Apache NiFi
VS
Airflow
Airflow
VS
Apache NiFi
Airbyte

Data is valuable for your organization if it is present in a structured format and can be utilized to draw meaningful insights. To facilitate seamless data analytics and visualization, you need to integrate data from various sources and orchestrate data pipelines. Several platforms, such as Airbyte, Airflow, and NiFi, have provided solutions to fulfill your data integration and workflow management needs. 

In this article, you will explore the key features of the popular platforms Apache NiFi vs Airflow vs Airbyte, along with a detailed comparison between them.

Apache NiFi Overview

Apache NiFi

Apache NiFi is an open-source framework designed to facilitate data transfer between systems. The platform, which is built in Java, can manage large volumes of data and automate data flow. It has a simple and effective data distribution and processing system that enables the generation of scalable directed graphs for data routing and transformation. 

Some of the unique features of Apache NiFi include:

  • It provides a simple drag-and-drop graphical user interface for managing, creating, and monitoring data in real time.
  • For data retrieval from a queue, NiFi lets you set prioritization schemes. In addition to the usual order of oldest first, there are several options for how data is retrieved, such as newest first, largest first, or any other preferred order.
  • With Apache NiFi, you can track the complete provenance of your dataset and follow its path from beginning to end. You can view all the instances in which your data was extracted, joined, cloned, changed, and finally transferred to the designated location at once.

Airflow Overview

Airflow

In 2014, Airbnb introduced the Airflow platform to streamline and manage business operations. It is an open-source workflow management tool that allows you to schedule, arrange, and monitor batch-oriented pipeline tasks. To facilitate pipeline orchestration, you can employ built-in or custom operators that provide logic for every stage of data processing in Python classes. 

In addition to managing your workflows, you can also schedule them by determining their frequency and time. Custom triggers, intervals, or cron expressions can be utilized to optimize workflows based on your needs.

Some of the unique features of Airflow are:

  • A Directed Acyclic Graph (DAG) is a graphical depiction of a sequence of tasks that can be generated using Airflow. This feature makes it easy to create and monitor complicated tasks logically.
  • To support robust data integration, Airflow offers various plug-and-play operators to execute your tasks on AWS, Microsoft Azure, Google Cloud Platform, and many other third-party services.
  • For professionals with diverse backgrounds, Airflow's intuitive interface facilitates the simple and seamless orchestration of data workflows.

Airbyte Overview

Airbyte

Airbyte is a powerful data integration platform that allows you to extract data from diverse sources such as flat files, SaaS applications, and databases. Once the data is collected, you can seamlessly load it into a destination like data lakes and warehouses. In addition, it also supports data sources that manage structured, semi-structured, and unstructured data types. 

Apart from these features, you can leverage its data replication capabilities using the CDC functionality. This feature enables you to identify changes in the source data and replicate them in the target system to ensure data consistency across multiple systems.

Some of the unique features of Airbyte are:

  • You can employ PyAirbyte, Airbyte’s open-source Python library, to fulfill advanced data integration needs. It empowers you to extract data seamlessly using connectors present in Airbyte with Python programming skills.
  • Airbyte offers different methods for monitoring ELT pipelines. For instance, you can integrate with Datadog, which allows you to easily monitor and analyze your data pipelines directly within Datadog’s dashboard.
  • It offers a large and vibrant community of data practitioners and developers who contribute to its open-source platform. You can engage with others to discuss the best data integration practices and resolve queries during data ingestion.

Apache NiFi vs Airflow vs Airbyte: Key Features

Here’s a table representing the key features of Apache NiFi vs Airflow vs Airbyte:

Attributes NiFi Airflow Airbyte
Focus Data ingestion and data flow management solutions. Workflow management and orchestration. Data integration and replication.
Connectors Availability Use the Connection to provide a link between processors. Provides built-in operators. 350+ to popular sources and destinations
Custom Connector Not available. Users can define and build their operators. Using CDK.
DAG Available Available Not available.
Community Support Diverse community having contributors, committers, and other volunteers. Large and open-source community. A vibrant community with 800+ contributors.
Compliance Certifications SSH and TLS encryption. Not available. ISO 27001, HIPAA, GDPR, CCPA, SOC 2.
Purchase Process Open-source Provides an open-source version. Open-source and two pricing plans along with a 14-day free trial.

{{COMPARISON_CTA}}

Apache NiFi vs Airflow vs Airbyte: Major Comparisons

In this section, you will go through the major comparisons between Apache NiFi vs Airflow vs Airbyte:

Apache NiFi vs Airflow vs Airbyte: Connector Feature

Apache NiFi is specifically designed to perform and manage data flow between systems. Unlike data integration tools that depend on connectors, it utilizes a Processor interface containing various processors, such as Attributes to CSV, Attributes to JSON, ConsumeAMQP, and many more. These processors are utilized to extract, ingest, egress, modify, and route data in addition to controlling access to flow files, their attributes, and information. However, to leverage these functions, you need to use a Connection to provide a link between processors, thereby allowing streamlined data flow.

Airflow does not offer a connector facility as it is not a data integration tool. Nonetheless, you can employ its multitude of operators to orchestrate data pipelines built on various platforms. Some of the popular built-in operators include Python, KubernetesPod, Snowflake, and Bash. Using these operators, you can interact with different data sources such as databases, cloud services, and APIs. In addition, you can design custom operators to manage specific data integration needs. This allows you to have greater control and flexibility with Airflow, enabling you to set up and manage the platform manually.

With Airbyte, you can leverage an extensive catalog of 350+ pre-built connectors, such as Redshift, Snowflake, MySQL, Salesforce, and many more. These connectors allow you to efficiently manage your integration process and automate data pipelines within minutes. If you are unable to find a connector of your choice, you can also build a custom connector in less than 10 minutes using its Connector Development Kit. In addition to these features, Airbyte enables you to request a new connector by contacting their sales team.

Apache NiFi vs Airflow vs Airbyte: Security and Compliance

NiFi uses encryption-enabled protocols, like 2-way SSL, to ensure data security throughout a dataflow. It also facilitates sharing keys and other techniques on both sides of the sender/recipient equation to encrypt and decrypt information. In addition to these measures, NiFi offers a self-service dataflow management model called multi-tenant authorization. Using this feature, you can manage workflow with complete awareness of the flow. 

On the other hand, to assure data integrity and confidentiality, Airflow offers a variety of security solutions. These features consist of OAuth authentication, SSL, encryption, impersonation, and access controls. However, you are in charge of configuring these features. Here, your ability to successfully apply security measures will impact the level of security for data management operations in Airflow. 

In contrast, Airbyte is equipped with all the security features you need to safeguard your data. These measures include audit logging, access control, authentication mechanisms, encryption in transit, credential management, and regular security assessments. Beyond its security features, Airbyte also complies with the best industry standards and certifications, such as ISO 27001, HIPAA, GDPR, and SOC 2 Type 2.

Apache NiFi vs Airflow vs Airbyte: Pricing Model

Although Apache NiFi is an open-source platform, maintaining and operating the infrastructure on which it runs may incur expenses. You can download this application directly on your system, configure it, and easily manage your data. It is fundamentally designed to be extensible, making it suitable for repeatable execution and interaction of dataflow processes. The various extensions include prioritizers, reporting tasks, controller services, processors, and customer user interfaces.

Airflow is also an open-source platform with an active and engaging community that provides insightful information and assistance. You can tailor the data orchestration requirements with Airflow to your specific needs with the help of this open-source ecosystem, which includes all the necessary operators, plugins, libraries, and documentation. It is crucial to remember that you would be in charge of setting up and managing the server instances and storage systems.

Comparatively, Airbyte is known for its transparent pricing plans—Airbyte Cloud, Airbyte Self-Managed, and Powered by Airbyte. The Self-Managed version offers an open-source plan. But, if you want to utilize Airbyte's capabilities without having to deal with server maintenance and management, the cloud version would be more appropriate. It provides a pay-as-you-go model. The Powered by Airbyte version lets you sync your data and offers pricing based on syncing frequency duration.

Final Word

This article vividly describes the unique features of NiFi, Airflow, and Airbyte platforms and their major differences. Each platform is designed to meet your specific business requirements, like data integration, orchestration, and data flow between systems. For instance, if you want to automate dataflow for large volumes of data or optimize workflow, then you can go for Apache NiFi or Airflow.

However, Airbyte is the recommended solution for streamlined data integration tasks. Its rich pre-built connector library, data replication capabilities, and efficient monitoring features make it a great choice. Sign in on the Airbyte platform today to explore its dynamic features.

Want to know the benchmark of data pipeline performance & cost?

Discover the keys to enhancing data pipeline performance while minimizing costs with this benchmark analysis by McKnight Consulting Group.

Get now

Compare Airbyte's pricing to other ELT tools

1 minute cost estimator

Don't trust our word, trust theirs!

No items found.

What Airbyte users say

“Airbyte saved us two months of engineering time by not having to build our own infrastructure. We can count on the stability and reliability of Airbyte connectors. Plus, with Airbyte it’s simple to build custom pipelines.”
“With Airbyte, we don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
"I used Airbyte's connector builder to write 2 connectors. The experience was amazing, the setup was straightforward, and in almost no time I was able to develop a new connector and get it running.”
“Using Airbyte makes extracting data from various sources super easy! I don't have to spend time maintaining difficult data pipelines. Instead, I can use that time to generate meaningful insights from data.”
"Airbyte does a lot of things really well. We just had to set it up, and it ran from there. Even moving 40GB worth of data works just fine without needing to worry about sizing up.”