Data integration and migration are becoming more and more crucial for businesses across all industries as more companies use big data and the cloud.
Users can now concentrate on the data while scheduling, monitoring, and controlling ETL/ELT pipelines with a single view thanks to the efficient solutions provided by Airflow and Azure Data Factory.
What is Azure Data Factory and how does it work?
Azure Data Factory is Azure's cloud ETL tool for scale-out serverless data integration and data transformation. It offers a code-free UI for simple authoring and single-pane management.
In order to orchestrate and automate data transfer and transformation via the cloud, Microsoft offers Azure Data Factory, a cloud-based integration service that enables you to develop data-driven workflows.
What is the mechanism?
Azure Data Factory has the capability to connect to all the data and processing sources you'll need, in addition to SaaS applications, file sharing, and other web services.
With the help of the Data Factory service, it is possible to create data pipelines that transmit data and schedule them to run at set intervals. This demonstrates that there is a choice among employing a scheduled pipeline mode as well as a one-time pipeline mode.
What is Apache Airflow and what are its main services?
Apache Airflow is a platform for batch-oriented workflow development, planning, and monitoring that is open-source. You may create workflows integrating with almost any technology using the flexible Python framework provided by Airflow.
Using Apache Airflow's workflow engine, your intricate data pipelines can be easily scheduled and carried out. It will make sure that every activity in your data pipeline is finished on time, with enough resources, and in the correct sequence.
Is it possible to replace Azure Data Factory with Apache Airflow?
Azure Data Factory can be replaced with Apache Airflow.An Airflow workflow is composed of directed acyclic graphs, or DAGs, which are defined by Python code.
With Airflow's cutting-edge user interface, it's easy to observe currently-used pipelines, monitor their development, and address problems as they come up.
What are the key features that Azure Data Factory and Apache Airflow offer?
The following techniques can be used by Azure Data Factory to address these issues with moving data to or from the cloud:
platform | Azure Data Factory |
|---|---|
Control Flow | Although certain services, such as Azure Scheduler, Azure Automation, SQL VM, etc., are available for data transfer, Azure Data Factory's task scheduling features are superior to them. |
Scalability | Large amounts of data can be handled by Azure Data Factory thanks to its design. |
Security | Azure Data Factory always performs automatic encryption on all data in transit between the cloud and on-premises. |
platform | Apache Airflow |
|---|---|
DAG File | Collection of DAG files that the scheduler and executor can view. |
Web Server | It offers a convenient user interface for inspecting, triggering, and debugging DAG jobs and behavior. |
Scheduler | It deals with starting workflows on schedules and sending tasks to the executor for execution. |
How does Azure Data Factory compare to Apache Airflow in terms of pricing?
The cost of the data pipeline offered by Azure Data Factory is determined by the number of pipeline orchestration runs, the number of compute hours required for flow execution and debugging, and the number of Data Factory operations, such as pipeline monitoring.
The Apache License 2.0 governs Airflow, which is free and open source. There are no upfront restrictions or minimum fees.
You are charged for both the time that your Airflow Environment is running and any additional auto-scaling that is necessary to increase the number of workers or web servers.
Azure Data Factory vs Apache Airflow: Pros and Cons of each tool
1. Azure Data Factory Pros and Cons
Pros
Cons
2. Apache Airflow Pros and Cons
Pros
Cons
How to choose wisely between Azure Data Factory and Apache Airflow for ETL?
Azure Data Factory | Apache Airflow |
|---|---|
Transformations | |
It offers a wide variety of transformation functions and supports both pre- and post-transformations. Power QueryOnline or the GUI can be used to apply transformations without the need for coding. | A topological illustration called a DAG demonstrates how data moves within a system. Job failures, retirements, and alarms are supported, and Apache Airflow maintains the execution dependencies between jobs in a DAG. |
Support, information, and instruction | |
ADF offers online forums and a help request form. It provides authentic, thorough documentation. Customers can also get in touch with you by phone or email. Additionally, it provides printable training materials in digital format. | The documentation for Apache Airflow contains a fast start and how-to manual. Additionally, it offers assistance to the Slack community. On its main page, it also offers some tutorials. |
Sources and destinations of data through connectors | |
There are around 80 data sources that Azure Data Factory (ADF) can link with, including SaaS platforms, SQL and NoSQL databases, general protocols, and a variety of file kinds. | Tasks, which are collections of actions, can be executed using operators, templates for tasks that Python functions or scripts can produce. |
Conclusion
The greatest features of both tools can be utilized by combining ADF and Airflow. Airflow DAG allows for the execution of ADF jobs and extends the scope of Airflow orchestration beyond the ADF.
As a result, companies may build their jobs using ADF without difficulty and use Airflow as the control plane for the orchestration.
Hooks and Operators, which are capable of simple interaction and ADF pipeline execution, are the fundamental components of Airflow.
