Apache Airflow is a platform to schedule workflows in a programmed manner. It does not handle data flow for real. However, it is more of a workflow orchestrator. It’s main function is to schedule and execute complex workflows. On the other hand, Apache Nifi is a top-notch tool that can handle data ingestion/transformation from several sources efficiently. Let’s take a deeper look at them!
Apache Nifi is a free-to-use & open-source ETL application. It helps in assembling programs from boxes visually & execute the same without any requirement of coding. So, it’s perfect for someone with no coding experience. It can operate with a variety of sources that includes JDBC query, RabbitMQ, Hadoop, etc. It can be used to enhance, sort, modify, combine, split, and verify data.
Apache NiFi helps you to create long-term jobs and is ideal for processing both streaming data & periodic batches. However, few difficulties might be faced during the setup.
Nifi is not confined to CSV format files. Images, audios, videos, and binary data can be quickly processed. Another nice functionality it offers is the ability to utilize various queue policies such as LIFO, FIFO, etc. Data provenance is a linked service that is capable of recording almost each & everything in the dataflows. It’s very simple as you can visualize the data storage or processing.
Benefits of Apache NiFi
- A live batch streaming
- Assistance to both cluster and standalone mode
- Greatly extensible & scalable platform
- Users can command & control it visually
- Great error handling
Key Features of Apache NiFi
This has been the core philosophy of NiFi. Guaranteed delivery in Apache Nifi is a must, even at an extremely high scale. It is attainable through the effective utilization of a well-built steadfast write-ahead log backed by a content repository.
It is possible to buffer all the queued data along with the capability to produce back pressure as the data breaches its specified limit(or attains the specified age).
Provenance of data
NiFi can automatically track, index, and pave the way for provenance data. It happens as objects progress through systems. This information proves to be very valuable in strengthening troubleshooting, compliance, optimization, or other scenarios.
Parallel Stream to Multiple Destinations
Apache NiFi can easily relocate data to various destinations simultaneously at any point in time. After the data stream is processed, the flow can be routed to multiple destinations utilizing the processor of Nifi. It is essential when the data has to be backed up on various destinations.
Flow Specific QoS (Latency Vs Throughput, Loss Tolerance, etc )
There are data flow points where data is not that critical & has less intolerance. In other scenarios, the data needs to be processed & distributed within seconds or else it will lose its value. Apache NiFi facilitates a fine-tuned flow of specific configurations to address these concerns.
Apache Airflow is a new-age platform that is utilized to design, build and monitor workflows. An open-source ETL technology, it can be easily incorporated with different cloud services(like Azure, GCP, and AWS). It has an easy-to-use interface that offers simple visualization. Owing to its modular architecture, it can be quickly scaled up.
Airflow was designed to serve as a highly versatile task scheduler. It can also be used to train the ML(Machine Learning) models, send notifications, monitor systems, and power functions within different APIs. While Apache Airflow is sufficient for a majority of day-to-day operations(such as running ETL jobs & ML pipelines, distributing data. etc), it isn’t the best option to execute streaming operations.
It helps in executing tasks on DAGs, thanks to its modern UI loaded with the best visualization elements. One can easily visualize the pipelines, tracks, and repair bugs. Workflows are continuous and consistent, making them simple to handle.
Benefits of Apache Airflow
- Programmatic Workflow Management
- Task Dependency Management
- Monitoring & Management Interface
- Extendable Model
- Easy Interface To Interact With Logs
Key Features Of Apache Airflow:
Programmatic Workflow Management
Airflow provides options to set up programmatic workflows. Xcom and Sub-DAGs facilitate the creation of dynamic & complex workflows.
For example, Dynamics Dags can be easily set up depending on the connections or variables that are defined in the Airflow UI.
One can easily define the executors, operators, and also extend the library in such a way that it is suitable for the abstraction level required by a specific environment.
Task Dependency Management:
It’s excellent in handling various kinds of dependencies, whether it’s dag running status, task completion, or file/partition presence via a particular sensor, etc. It is even capable of handling task dependency concepts like branching.
Monitoring & Management Interface:
Airflow comes with a monitoring & management interface. One can have an immediate overview of the various task statuses. It is also possible to trigger & clear DAGs runs or tasks.
Automate your Queries, Python Code:
Airflow is armed with several operators set up to execute code. It comes with operators for a majority of databases. As it is set up in Python, its PythonOperator allows for fast porting of python code to production.
So, that’s the basic difference between Apache Nifi and Apache Airflow. Let’s hope that the blog has helped you understand the way these two function. If you are looking forward to implementing them(or any of them), contact our experts from Ksolves.
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