Benefits of Data Pipelines with Apache Airflow
Airflow is a tool that permits scheduling and monitoring your data pipeline. This tool is written in Python and it is an open source workflow management platform. Airflow can be used to write a machine learning pipelines, ETL pipelines, or in general to schedule your jobs. So thanks Airflows we can automate workflows and avoid many boring and manual tasks.
In Apache Airflow within a workflow we have various tasks that form a graph. The tasks are linked with a relationship of dependency. The arrow that connects a task with another task has a specific direction and there are no cycles, for this reason in Airflow we have DAGs that means Directed Acyclic Graphs.
Part I: How to create a DAG and the operators to perform tasks? When we want to create a DAG, we have to give the name, the description, the start date and the interval as you can see the below.
Operators are the building blocks of DAG. They define the actual work that a DAG will perform.
We need to parametrise the operators by setting the task_id, the python_callable and the dag. Other common operators are:. Schedules are optional and by default, if we omit the schedule interval, our DAG will run once a day from the start day.
Anyway, below you can find all the alternatives for setting up your schedule interval. Part II: Task Dependencies and Airflow Hooks. In Airflow every Directed Acyclic Graphs is characterized by nodes(i.e tasks) and edges that underline the ordering and the dependencies between tasks.
We can describe the dependencies by using the double arrow operator ‘>>’. a>>b means a comes before b. a<
Belowan example on how it looks like. Above you can observe that I imported a PostgresHook, and I initiated it by giving the name ‘example’.
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Then with the function db_hook.get_pandas_df(‘SELECT * FROM my_table’) I ask to give me a pandas data frame. So Airflow go off and run the SQL statement and finally the result is loaded into a pandas data frame and returned to Airflow. However, Airflow have other hooks like:. Part III: Context and Templating. Airflow provide several context variables specific to the execution of a given DAG and a task at runtime. Context variables are useful in the process of accessing or segmenting data before processing.
You can find here a list of variables that can be included as kwargs. Below, we have a function called my_func. Into the function we have *args and **kwargs(i.e. the list of keyword arguments). In addition, you can notice that in the task the provide_context is set as True.
Now, you have the fundamentals to build your pipeline with Airflow. I think that the best way to understand these concepts is to set up your airflow environment and try on your own.
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Originally created at Airbnb in 2014, Airflow is an open-source data orchestration framework that allows developers to programmatically author, schedule, and monitor data pipelines.
Airflow experience is one of the most in-demand technical skills for Data Engineering (another one is Oozie) as it is listed as a skill requirement in many Data Engineer job postings.
In this blog post, I will explain core concepts and workflow creation in Airflow, with source code examples to help you create your first data pipeline using Airflow. Here are the basic concepts and terms frequently used in Airflow:.
DAG: In Airflow, a DAG (Directed Acyclic Graph) is a group of tasks that have some dependencies on each other and run on a schedule. Each DAG is equivalent to a logical workflow. A DAG Run is a specific run of the DAG. Operator: An operator is a Python class that acts as a template for a certain type of job, for example:.
BashOperator: execute a bash command. PythonOperator: run a Python function. PythonVirtualenvOperator: run a function in a virtual environment that is created and destroyed automatically.
BigQueryOperator: query and process data in BigQuery. PapermillOperator: execute a Jupyter Notebook.
Task: Once an operator is instantiated with specific arguments, it becomes a task. Task Instance: A task instance represents a specific run of a task and it has a state, for example: “running”, “success”, “failed”, “skipped”, “up for retry”, etc.
A DAG (aka a workflow) is defined in a Python file stored in Airflow’s DAG_FOLDER and contains 3 main components: the DAG definition, tasks, and task dependencies. Default Arguments. When the default_argsdictionaryis passed to a DAG, it applies to all tasks belonging to the DAG:.
Some useful parameters:. start_date: The execution_date for the first DAG run. end_date: The date the DAG should stop running (usually none).
execution_timeout: The maximum times a task can run. retries: The number of retries that can be performed before the task fails.
retry_delay: The delay time between retries.
depends_on_past: When it is set to true, a task instance will only run if the previously scheduled task instance succeeds. on_failure_callback: The function to be called when a task instance fails. Some useful parameters for DAG constructor:.
schedule_interval: A cron expression to specify how often the DAG should run. catchup: Turning catchup off is recommended if your DAG performs backfill internally. DAG files need to be evaluated quickly (in seconds) since the scheduler will execute them periodically (around every minute) to reflect the workflow changes, if any.
Thus, don’t perform actual data processing in DAG files. Task Dependency. Currently, there are two main ways to set dependencies between tasks:.
Python’s bitshift operators (>> and <<). set_upstream() and set_downstream() methods. You can also define dependencies among multiple tasks using Python’s list:.
Use chain() function to define a sequential dependency:. Use cross_downstream() to set dependencies between two groups of tasks:. You just learned how to create a data workflow using Airflow.
You can simply deploy your workflow by adding the DAG file and any dependency files to Airflow’s DAG_FOLDER and Airflow will automatically pick it up. Then, you can use Airflow’s built-in web UI to monitor and debug your workflow at any time (which is very straightforward so I won’t cover details here).
Ready to learn more about Airflow? Check out Airflow Tips and Best Practices! Want to learn more about Data Engineering? Check out my Data Engineering 101 column on Towards Data Science:.
Today with the rapid surge in information, managing data can be tricky.
Moreover, trying to control and monitor all the data-related processes consumes excess resources and time, both of which are precious for any organization. To overcome these bottlenecks, businesses nowadays are relying on Data Pipelines to automate their data collection and transformation tasks.
Companies are either setting up their in-house Data Pipelines or are hiring the services of a third party to manage their data needs. Apart from managing data, another concern that businesses face, is with regards to Data Monitoring and Error-Detection in Projects.
Apache Airflow is a popular tool that provides organizations with a solution for both of these issues.
It is an open-source platform that supports companies in automating their lengthy workflows.
Furthermore, Apache Airflow also offers Data Pipeline facilities to its users.
This article will introduce you to Apache Airflow and Data Pipelines along with their key features.
It will then provide you with 5 easy steps using which you can build Data Pipelines with Apache Airflow. Read along to learn these steps and understand the benefits of using Apache Airflow as a Data Solution! What is Apache Airflow? What is a Data Pipeline? Steps to Build Data Pipelines with Apache Airflow.
Apache Airflow is a workflow automation platform that is popular for its open-source availability and scheduling capabilities. You can utilize this tool to programmatically author, schedule, and monitor any number of workflows.
Businesses today use Airflow to organize complex computational workflows, build data processing pipelines, and easily perform ETL processes. Airflow operates on DAG (Directed Acyclic Graph) to construct and represent its workflow, and each DAG is formed of nodes and connectors.
These Nodes depend on Connectors to link up with the other nodes and generate a dependency tree that manages your work efficiently.
Apache Airflow contains the following unique features which have led to its immense popularity:.
Dynamic Integration: Airflow implements Python Programming Language for its backend processing required to generate dynamic pipelines.
Python provides certain Operators and Connectors that can easily create DAGs and use them to generate workflows.
Extensible: Airflow being an open-source platform allows you to customize its operators & executors.
Moreover, you can also extend its libraries to make it fit for the level of abstraction that your work requires.
Elegant User Interface: Airflow relies on the Jinja templates for building pipelines, and hence can develop lean and explicit workflows. Furthermore, with Apache Airflo, you can parameterize your scripts in a hassle-free manner.
Scalable: Airflow can scale up to infinity. This implies you can define any number of dependent workflows. Airflow also provides a message queue that can orchestrate these workflows easily.
To learn more about Apache Airflow, visit here. Your business generates and works with vast quantities of data. Now, to get any real insight from this sea of data, you have to: . Extract relevant data from numerous data sources that are related to your business.
Clean and transform the extracted data and make it analysis-ready.
Load the huge datasets into a Data Lake or Data Warehouse to create a single source of truth.
Now, depending on the order of these steps you can carry out ETL (Extract Transform Load)or ELT (Extract Load Transform) processes and make your data fit for analysis.