![]() Automate your Queries, Python Code:Īirflow is armed with several operators set up to execute code. It is also possible to trigger & clear DAGs runs or tasks. One can have an immediate overview of the various task statuses. Monitoring & Management Interface:Īirflow comes with a monitoring & management interface. It is even capable of handling task dependency concepts like branching. 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. 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. Xcom and Sub-DAGs facilitate the creation of dynamic & complex workflows.įor example, Dynamics Dags can be easily set up depending on the connections or variables that are defined in the Airflow UI. Key Features Of Apache Airflow: Programmatic Workflow ManagementĪirflow provides options to set up programmatic workflows. Workflows are continuous and consistent, making them simple to handle. One can easily visualize the pipelines, tracks, and repair bugs. It helps in executing tasks on DAGs, thanks to its modern UI loaded with the best visualization elements. etc), it isn’t the best option to execute streaming operations. While Apache Airflow is sufficient for a majority of day-to-day operations(such as running ETL jobs & ML pipelines, distributing data. It can also be used to train the ML(Machine Learning) models, send notifications, monitor systems, and power functions within different APIs. Owing to its modular architecture, it can be quickly scaled up.Īirflow was designed to serve as a highly versatile task scheduler. It has an easy-to-use interface that offers simple visualization. An open-source ETL technology, it can be easily incorporated with different cloud services(like Azure, GCP, and AWS). Apache AirflowĪpache Airflow is a new-age platform that is utilized to design, build and monitor workflows. Apache NiFi facilitates a fine-tuned flow of specific configurations to address these concerns. In other scenarios, the data needs to be processed & distributed within seconds or else it will lose its value. There are data flow points where data is not that critical & has less intolerance. Flow Specific QoS (Latency Vs Throughput, Loss Tolerance, etc ) It is essential when the data has to be backed up on various destinations. After the data stream is processed, the flow can be routed to multiple destinations utilizing the processor of Nifi. Parallel Stream to Multiple DestinationsĪpache NiFi can easily relocate data to various destinations simultaneously at any point in time. This information proves to be very valuable in strengthening troubleshooting, compliance, optimization, or other scenarios. It happens as objects progress through systems. NiFi can automatically track, index, and pave the way for provenance data. 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). It is attainable through the effective utilization of a well-built steadfast write-ahead log backed by a content repository. Guaranteed delivery in Apache Nifi is a must, even at an extremely high scale. This has been the core philosophy of NiFi. Key Features of Apache NiFi Guaranteed Delivery Users can command & control it visually.Assistance to both cluster and standalone mode.It’s very simple as you can visualize the data storage or processing. Data provenance is a linked service that is capable of recording almost each & everything in the dataflows. Another nice functionality it offers is the ability to utilize various queue policies such as LIFO, FIFO, etc. Images, audios, videos, and binary data can be quickly processed. Nifi is not confined to CSV format files. However, few difficulties might be faced during the setup. It can be used to enhance, sort, modify, combine, split, and verify data.Īpache NiFi helps you to create long-term jobs and is ideal for processing both streaming data & periodic batches. It can operate with a variety of sources that includes JDBC query, RabbitMQ, Hadoop, etc. So, it’s perfect for someone with no coding experience. It helps in assembling programs from boxes visually & execute the same without any requirement of coding. Let’s take a deeper look at them! Apache NifiĪpache Nifi is a free-to-use & open-source ETL application. On the other hand, Apache Nifi is a top-notch tool that can handle data ingestion/transformation from several sources efficiently. It’s main function is to schedule and execute complex workflows. ![]() However, it is more of a workflow orchestrator. Apache Airflow is a platform to schedule workflows in a programmed manner.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |