What is Apache Airflow?
The primary use of Apache airflow is managing the workflow of a system. It is an open-source and still in the incubator stage. It was initialized in 2014 under the umbrella of Airbnb since then it got an excellent reputation with approximately 500 contributors on GitHub and 8500 stars.
How Apache Airflow works?
It accomplishes the tasks by taking DAG(Directed Acyclic Graphs) as an array of the workers, some of these workers have particularized contingencies. It results in the formation of DAG in Python itself which make these DAGs used easily further for the other processes. This results in the changing of a workflow into a well-defined code which further makes a workflow testable, maintainable, Co-operative and Versionable.
DAG(Directed Acyclic Graphs)
In computer science and mathematics, a directed acyclic graph (DAG) is a directed graph that has no directed cycles. In graph theory, a graph refers to a set of vertices connected by lines called edges. In a directed graph, each edge is associated with a direction from a beginning vertex to an end vertex. If we travel along the edges' direction and find that no closed loops are formed along any path, it is said that there are no directed cycles. And graph formed is a DAG
Apache Airflow Architecture
Apache Airflow is a platform for programmatically defining, scheduling, and monitoring workflows. It uses a directed acyclic graph (DAG) to represent the workflow, which is a collection of tasks that are executed in a specific order
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During all of the above procedures tasks not permitted to exchange the data, but with this fact, it is also true that metadata transferred. It’s not considered as a streaming solution concerning data. The working process of Apache Airflow is not likely to be similar with "Spark Streaming" or "Storm" space. However, it can be taken as similar to the "Azkaban" or Oozie.
Benefits of Apache Airflow
- Dynamic - The pipeline constructed by Airflow dynamic, constructed in the form of code which gives an edge to be dynamic.
- Extensible - The another good thing about working with Airflow that it is easy to initiate the operators, executors due to which the library boosted so that it can suit to the level of abstraction to support a defined environment.
- Elegant - A pipeline developed with the help of Airflow is angular and unambiguous because Jinja template engine used to parameterize the scripts built into the core of Airflow.
- Scalable - The architecture of Airflow composed of standardized units which also use messaging technique for queuing the number of workers and moreover it is scalable to infinity.
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Importance of Apache Airflow
These are the main reasons which signify the Importance of Apache Airflow -
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The most important advantage of it is that it provides the power of scheduling the analytics workflow and Data warehouse also managed under a single roof so that a comprehensive view accessed to check the status.
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The logs entries of execution concentrated at one location.
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The use of Airflow also matters as it has a strength to automate the development of workflows as it has an approach to configure the workflow as a code.
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It can also give a reporting message through slack if an error comes due to the failing of DAG.
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Within the DAGs, it provides a clarion picture of the dependencies.
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The ability to generate the metadata gives an edge of regenerating distinctive uploads.
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Steps to integrate Apache Airflow:
Steps to to adopt Apache Airflow are listed below:
- DAG - Graphs of tasks/ usages.
- OPERATOR - The operator refers to the transformation step further divided into
- Sensor - This type of operator performs a function to polls with frequency/timeout.
- Executor - This type of operator performs trigger operations, for example, HiveOperator, Pig Operator.
- TASK - Task is the main entity of the DAG. The main thing here is the task instance considered to run of a task at a point of time.
- HOOK - It is considered as the Interface for the external System such as a hook of JDBC and HTTP.
How is Apache Airflow utilized in practice?
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First of all, do the setting of all configuration related options.
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Initialize the database at the backend.
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Initialized the use of the operators. These main operators include PythonOperator, BashOperator, Google Cloud Platform Operators.
Manage the connections by following steps -
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Develop a connection with the User Interface.
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Edit the connection with the User Interface.
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Develop a connection with variables related to the environment.
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Configure the type of connections.
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Configure the Apache Airflow to write the logs.
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Scale out it first with Celery then with Dask and with Mesos.
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Run Airflow with systemd and with upstart.
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For testing always use the test mode configuration.
Best Practices of Apachr Airflow
| Things to be Considered | Best Practices |
| The composition of the Management | - Give concern on the definition of Built-ins such as Connections, Variables. - There are also other tools which are non-python and present in Airflow; forget their usability also. - Target single source of configuration. |
| Fabricating and Cutting the Directed Acyclic Graph | - There should be one DAG per data source, one DAG per project and one DAG per data sink. - The code should be kept in template files. - The Hive template used for the Hive. - For the template search, the template search path is used. - The template files are kept "Airflow agnostic." |
| Generating Extensions and Plugins | - It is easy to write plugins and extensions, but it is a needed thing - Extension paths which should be concerned are operators, hooks, executors, macros, UI adaption (views, links). - Writing of plugins and extensions should be started from existing classes, and the adapt it. |
| Generating and Expanding Workflows | - For this point, Database should be considered at three levels Personal level, Integration level, Productive level. - The personal level is handled by Data engineers or Data scientists and at this level, testing should be done by "airflow test." - At the integration level, Performance testing and Integration testing considered. - At productive level monitoring handled. |
| Accommodating with the enterprise | - The existing workflow tools considered for scheduling. - There are tools which are there in Airflow for integration, considering them is a nice practice. |
Apache AIrflow Use cases
Apache Airflow is an open-source platform used for programmatically defining, scheduling, and monitoring workflows. Here are some common use cases for Apache Airflow:
1. ETL (Extract, Transform, Load): Airflow can be used to automate ETL processes, extracting data from various sources, transforming it into a usable format, and loading it into a target system.
2. Data Pipelines: Airflow can be used to create data pipelines that automate data processing tasks, such as data ingestion, processing, and delivery.
3. Machine Learning: Airflow can be used to automate machine learning workflows, including data preprocessing, model training, and model deployment.
4. Job Scheduling: Airflow can be used to schedule jobs to run at specific times or intervals, making it a great tool for automating routine tasks.
5. Batch Processing: Airflow can be used to automate batch processing tasks, such as processing large datasets or running complex calculations.
6. Data Warehousing: Airflow can be used to automate the process of loading data into data warehouses, such as Amazon Redshift or Google BigQuery.
7. Cloud-based Data Processing: Airflow can be used to automate cloud-based data processing tasks, such as processing data in AWS S3 or Google Cloud Storage.
8. Data Integration: Airflow can be used to integrate data from multiple sources and systems, such as integrating data from multiple APIs or databases.
9. DevOps Automation: Airflow can be used to automate DevOps tasks, such as deploying code changes or running automated tests.
Conclusion
Apache Airflow is Dependency Management Supportable, extensible, scalable and Open Source. The main functions of it are to schedule workflow, monitor and author. These functions achieved with Directed Acyclic Graphs (DAG) of the tasks.
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