Airflow also offers better visual representation of Throughout this guide, the following terms are used to describe task dependencies: In this guide you'll learn about the many ways you can implement dependencies in Airflow, including: To view a video presentation of these concepts, see Manage Dependencies Between Airflow Deployments, DAGs, and Tasks. And what if I want to branch on different downstream DAGs depending on the results of the previous DAGs? Modify only the highlighted parts in the .cfg file. For example, if trigger_dag_id=target_dag, the DAG with the DAG id target_dag will be triggered. That means if you trigger your target DAG with the TriggerDagRunOperator on the execution date 2022-01-01 00:00 and for whatever reason you want to retry or rerun it on the same execution date, you cant. It will use the configuration specified in airflow.cfg. Very straightforward, this parameter expects the DAG id of the DAG where the task you are waiting for is. If your DAG has only Python functions that are all defined with the decorator, invoke Python functions to set dependencies. The ExternalTaskSensor will only receive a SUCCESS or FAILED status corresponding to the sensed DAG, but not any output value. Required fields are marked *. When the dag-1 is running i cannot have the dag-2 running due to API limit rate (also dag-2 is supposed to run once dag-1 is finished). This feature is controlled by: * ``[core] min_serialized_dag_update_interval = 30`` (s): serialized DAGs are updated in DB when a file gets processed by scheduler, to reduce DB write rate, there is a minimal interval of updating serialized DAGs . This is the code of the downstream DAG: Some important points to notice. One common scenario where you might need to implement trigger rules is if your DAG contains conditional logic such as branching. You can see pods running on the Spot-backed managed node group using kubectl:. So DAGs that are cross-dependent between them need to be run in the same instant, or one after the other by a constant amount of time. To set the dependencies, you invoke the function analyze_testing_increases(get_testing_increase(state)): If your DAG has a mix of Python function tasks defined with decorators and tasks defined with traditional operators, you can set the dependencies by assigning the decorated task invocation to a variable and then defining the dependencies normally. 5 Ways to View and Manage DAGs in Airflow December 7, 2022 C Craig Hubert The Airflow user interface (UI) is a handy tool that data engineers can use to understand, monitor, and troubleshoot their data pipelines. They allow you to avoid duplicating your code (think of a DAG in charge of cleaning metadata executed after each DAG Run) and make possible complex workflows. The role of the check task is to wait for other DAGs to complete before moving forward. Since this DAG is triggered every day at 10:05AM, there is a delta of 5 minutes that we must define. Apache Airflow, Apache, Airflow, the Airflow logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. A notable feature of Apache Airflow is the user interface (UI), which provides insights into your DAGs and DAG runs. How Airflow community tried to tackle this problem. A DAG illustrates tasks and execution flow with vertices and lines. For that, you can use the branch operator and the XCOM to communicate values across DAGs. The example below shows you how to pass an XCom created from the DAG where the TriggerDagRunOperator is to the target DAG. Start a DAG run based on the status of | by Amit Singh Rathore | Dev Genius 500 Apologies, but something went wrong on our end. It allows you to define the execution date (=logical_date,=data_interval_end) to the triggered DAG. Note that child_task1 will only be cleared if Recursive is selected when the The DAG Dependencies view shows a graphical representation of any cross-DAG and dataset dependencies in your Airflow environment. With the all_success rule, the end task never runs because all but one of the branch tasks is always ignored and therefore doesn't have a success state. By the way, this is absolutely needed if you want to backfill your DAG (rerun past already triggered DAG Runs). For example: Two DAGs may have different schedules. Airflow DAG Dependencies Deprecation notice The functionality of this plugin is now part of Airflow - apache/airflow#13199 If you find any critical issues affecting Airflow 1.10.x, feel free to sumbit a PR, but no new features will be added here. On the DAG code in Amazon S3 pane, choose Browse S3 next to the DAG folder field. A dag (directed acyclic graph) is a collection of tasks with directional dependencies. The UI is a useful tool for understanding, monitoring, and troubleshooting your pipelines. States are represented by color. To get the most out of this guide, you should have an understanding of: Basic dependencies between Airflow tasks can be set in the following ways: For example, if you have a DAG with four sequential tasks, the dependencies can be set in four ways: All of these methods are equivalent and result in the DAG shown in the following image: Astronomer recommends using a single method consistently. The conf parameter is very useful as it allows you to pass information/data to the triggered DAG. A task may depend on another task on the same DAG, but for a different execution_date Amit Singh Rathore 1.4K Followers Staff Data Engineer @ Visa Writes about Cloud | Big Data | ML The TaskFlow API, available in Airflow 2.0 and later, lets you turn Python functions into Airflow tasks using the @task decorator. Astronomer 2022. I received countless questions about DAG dependencies, is it possible? Using both bitshift operators and set_upstream/set_downstream in your DAGs can overly-complicate your code. Basically, you must import the corresponding Operator for each one you want to use. Each generate_files task is downstream of start and upstream of send_email. This inelasticity limits Airflow's capability as a parallel data execution engine, and restricts the use-cases of how our users can write DAGs. DAG integrity test. Dependencies are a powerful and popular Airflow feature. a weekly DAG may have tasks that depend on other tasks on a daily DAG. The airflow scheduler monitors all tasks and all DAGs, triggering the task instances whose dependencies have been met. Why? Files can now be found on S3. The trigger_dag_id parameter defines the DAG ID of the DAG to trigger. However, always ask yourself if you truly need this dependency. There is no need for you to use Airflow RBAC in addition to Astronomer RBAC. Here is a simple DAG below: from airflow. Managing your Connections in Apache Airflow. You have four tasks - T1, T2, T3, and T4. Otherwise, it doesnt work. DAG dependencies can quickly become hard to manage. user clears parent_task. As such, its always important to define the right poke_interval, poke_method, and timeout. Airflow dag dependencies Ask Question Asked 1 year, 10 months ago Modified 1 year, 1 month ago Viewed 71 times 1 I have a airflow dag-1 that runs approximately for week and dag-2 that runs every day for few hours. For more details, check the documentation of ExternalTaskSensor. In my opinion, stick with external_task_ids. If you change the trigger rule to one_success, then the end task can run so long as one of the branches successfully completes. When set to true, the TriggerDagRunOperator automatically clears the already triggered DAG Run of the target DAG. To set these dependencies, use the Airflow chain function. As I mentioned before, the Airflow GUI can be used to monitor the DAGs in the pipeline. class SerializedDagModel (Base): """A table for serialized DAGs. To access the DAG dependencies view, go to Browse -> DAG Dependencies. In Apache Airflow we can have very complex DAGs with several tasks, and dependencies between the tasks. The schedule and start date is the same as the upstream DAGs. The key is the identifier of your XCom which can be used to get back the XCOM value from a given task. DAG Dependencies in Apache Airflow might be one of the most popular topics. Pay attention to "# apache airflow DAG" if you will not have 2 words airflow and DAG in your file, this file will be not parsed by Airflow. Choose the environment where you want to run DAGs. Use XCom with BranchPythonOperator. trigger_dag_id is also a templated parameter. none_failed: The task runs only when all upstream tasks have succeeded or been skipped. By default, every 60 seconds. However, it is sometimes not practical to put all related However, the name execution_date might be misleading: it is not a date, but an instant. The DAG on the right is in charge of cleaning this metadata as soon as one DAG on the left completes. This means that the job instance is started once the period it covers has ended. But what if we have cross-DAGs dependencies, and we want to make a DAG of DAGs? In the following example, a set of parallel dynamic tasks is generated by looping through a list of endpoints. Each column represents a DAG run and each square represents a task instance in that DAG run. In these cases, one_success might be a more appropriate rule than all_success. An introduction to the Airflow UI A notable feature of Apache Airflow is the user interface (UI), which provides insights into your DAGs and DAG runs. This view is particularly useful when reviewing and developing a DAG. Add tags to DAGs and use it for filtering in the UI, ExternalTaskSensor with task_group dependency, Customizing DAG Scheduling with Timetables, Customize view of Apache Hive Metastore from Airflow web UI, (Optional) Adding IDE auto-completion support, Export dynamic environment variables available for operators to use. , Airflow DAG run , Task . It allows you to have a task in a DAG that triggers another DAG in the same Airflow instance. If you generate tasks dynamically in your DAG, you should define the dependencies within the context of the code used to dynamically create the tasks. Astronomer 2022. The Security tab links to multiple pages, including List Users and List Roles, that you can use to review and manage Airflow role-based access control (RBAC). Airflow TaskGroups The TaskGroup Basics TaskGroup Default Arguments Nested TaskGroups Task Groups Without The Context Manager Dynamically Generating Task Groups Task Group Factory The Decorator TaskGrous in Action! WebServer UI . Airflow Connections Connections are a way to store the information needed to connect to external systems. In order to create a Python DAG in Airflow, you must always import the required Python DAG class. But sometimes you cannot modify the DAGs, and you may want to still add dependencies between the DAGs.

', 'https://covidtracking.com/api/v1/states/', Gets totalTestResultsIncrease field from Covid API for given state and returns value, # Invoke functions to create tasks and define dependencies, Uploads validation data to S3 from /include/data, # Take string, upload to S3 using predefined method, Manage Dependencies Between Airflow Deployments, DAGs, and Tasks. You define a workflow in a Python file and Airflow manages the scheduling and execution. This post explains how to create such a DAG in Apache Airflow In Apache Airflow we can have very complex DAGs with several tasks, and dependencies between the tasks. The first step is to import the necessary classes. A small play icon on a DAG run indicates that a run was triggered manually, and a small dataset icon shows that a run was triggered via a dataset update. For example, the Connections page shows all Airflow connections stored in your environment. The main interface of the IDE makes it easy to author Airflow pipelines using blocks of vanilla Python and SQL. The TriggerDagRunOperator is perfect if you want to trigger another DAG between two tasks like with SubDAGs (dont use them ). If you're not already using Airflow and want to get it up and running to follow along, see Install the Astro CLI to quickly run Airflow locally. It may end up with a problem of incorporating different DAGs into one pipeline. Notice that the DAG target_dag and the DAG where the TriggerDagRunOperator is implemented must be in the same Airflow environment. In summary, we need alignment in the execution dates and times. Click + to add a new connection. All other products or name brands are trademarks of their respective holders, including The Apache Software Foundation. Read Airflow UI config file in python code and use the values as parameter python Airflow UI 2022-11-15 23:16:53 DAG.py BashOperator python latest_only import LatestOnlyOperator from airflow . The vertices are the circles numbered one through four, and the arrows represent the workflow. The Airflow user interface (UI) serves as an operational dashboard to schedule, monitor and control any scripts or applications. In Addition, we can also use the ExternalTaskSensor to make tasks on a DAG Variables Airflow DAG . Within the book about Apache Airflow [1] created by two data engineers from GoDataDriven, there is a chapter on managing dependencies.This is how they summarized the issue: "Airflow manages dependencies between tasks within one single DAG, however it does not provide a mechanism for inter-DAG dependencies." Like with the TriggerDagRunOperator, make sure both DAGs are unpaused. Use the ExternalTaskSensor to make tasks on a DAG In the end, we just run the function of the DAG. utils. Airflow uses directed acyclic graphs (DAGs) to manage workflow. What does it mean? The dependencies between the task group and the start and end tasks are set within the DAG's context (t0 >> tg1 >> t3). It is simple but useful, it allows you to wait for the triggered DAG to complete before moving to the next task in your DAG where the TriggerDAGRunOperator is. The DAGs on the left are doing the same steps, extract, transform and store but for three different data sources. should be used. The upstream DAG would have to publish the values in the XCOM, and the downstream DAG needs to provide a callback function to the branch operator. Airflow dag dependencies #airflow #big_data Often Airflow DAGs become too big and complicated to understand. Most Airflow users are already familiar with some of the insights the UI provides into DAGs and DAG runs through the popular Graph view. If you dont know what Im talking about take a look at the article I made here. The TriggerDagRunOperator is the easiest way to implement DAG dependencies in Apache Airflow. The parameter allowed_states expects a list of states that mark the ExternalTaskSensor as success. For example, in the following DAG there are two dependent tasks, get_testing_increases and analyze_testing_increases. As you trigger the DAG, Airflow will create pods to execute the code included in the DAG. dependencies for tasks on the same DAG. There are two major ways to create an XCOM variable in the airflow dag. This one is particularly important. Use Airflow to author workflows as Directed Acyclic Graphs (DAGs) of tasks. For more information on working with RBAC, see Security. While your code should live in source control, the Code view provides a quick insight into what is going on in the DAG. Well, that looks confusing isnt it? Because you want to process data on the same data interval. Mix-and-match your way to a perfect fall getaway. Step 1: Make the Imports. ExternalTaskSensor also provide options to set if the Task on a remote DAG succeeded or failed If you need to implement dependencies between DAGs, see Cross-DAG dependencies. How? If you need to branch depending on the values calculated in a task, you can use the BranchPythonOperator (https://airflow.apache.org/docs/stable/concepts.html#branching). Therefore, always, always define the failed_states parameters with the value state.FAILED as shown below: Those parameters are very important. Different teams are responsible for different DAGs, but these DAGs have some cross-DAG If the start dates differ by a constant amount of time, you can use the execution_delta parameter of ExternalTaskSensor. The @task decorator#. When you set dependencies between tasks, the default Airflow behavior is to run a task only when all upstream tasks have succeeded. They allow you to avoid duplicating your code (think of a DAG in charge of cleaning metadata executed after each DAG Run) and make possible complex workflows. In this article, we will walk through the Airflow User Interface its web view and understand the . Click a square in the grid to view more details about the task instance and access links to additional views and actions. Thats exactly what reset_dag_run allows you. Notice the @dag decorator on top of the function EXAMPLE_simple.The function name will also be the DAG id. If you run a DAG on a schedule_interval of one day, then the run stamped 2016-01-01 will trigger after 2016-01-01T23:59. If not, then you must define the delta with execution_delta or execution_date_fn (not both), so they match. The following are the additional DAG views that are available, but not discussed in this guide: The Dataset tab was introduced in Airflow 2.4 in support of the new dataset driven scheduling feature. TriggerDagRunOperator is an effective way to implement cross-DAG dependencies. This is what information you want to share between tasks. 2 . Following the DAG class are the Operator imports. This view shows all dependencies between DAGs in your Airflow instance. none_skipped: The task runs only when no upstream task is in a skipped state. In. The DAG runs and task instances pages are the easiest way to view and manipulate these objects in aggregate. DAG dependencies in Apache Airflow are powerful. via allowed_states and failed_states parameters. Mysql Azure SQLDAG,mysql,azure,triggers,airflow,Mysql,Azure,Triggers,Airflow,azure sqlinsertDAG sql dbsql db . Turn on the Dag. By default, you cannot run twice the same DAG on the same execution_date unless it is cleared first. A Medium publication sharing concepts, ideas and codes. The Airflow scheduler is designed to run as a persistent service in an Airflow production environment. Using SubDagOperator creates a tidy parent-child relationship between your DAGs. A dag also has a schedule, a start date and an end date (optional). The tasks are defined in Python, and the execution along with scheduling is managed by Airflow. Now you've learned enough to start building your DAG step-by-step! from airflow . This is a nice feature if those DAGs are always run together. The Calendar view is available in Airflow 2.1 and later. all_skipped: The task runs only when all upstream tasks have been skipped. one_failed: The task runs as soon as at least one upstream task has failed. Each task is a node in the graph and dependencies are the directed edges that determine how to move through the graph. airflow/example_dags/example_external_task_marker_dag.py[source]. To get it started, you need to execute airflow scheduler. models import DAG from airflow. astronomer/airflow-covid-data: Sample Airflow DAGs to load data from the CovidTracking API to Snowflake via an AWS S3 intermediary. The architecture of Airflow is built in a way that tasks have complete separation from any other tasks in the same DAG. You must define one of the two but not both at the same time. The Admin assigns users to appropriate roles. The more DAG dependencies, the harder it to debug if something wrong happens. In case you are proposing a fundamental code change, you need to create an Airflow Improvement Proposal ( AIP ). If it does not exist, that doesnt raise any exceptions. The upload_data variable is used in the last line to define dependencies. Click on the Trigger Dag button. I tend to use it, especially for cleaning metadata generated by DAG Runs over time. Variables key-value , key value . Note that if you run a DAG on a schedule_interval of one day, the run stamped 2020-01-01 will be triggered soon after 2020-01. DAG, which is usually simpler to understand. dependencies. Here is how to add the current execution date of your DAG: reset_dag_run is a boolean parameter that defines whether or not you want to clear already triggered target DAG Runs. Failed_states expects a list of failed states to indicate to the TriggerDagRunOperator that the triggered DAG has failed, otherwise it would wait forever. DAG dependencies in Apache Airflow are powerful. and the list goes on. Subsequent DAG Runs are created by the scheduler process, based on your DAG 's schedule_interval, sequentially. That being said, since Airflow 2.1, a new view has been introduced: The DAG Dependencies view. In this case, you would have a variable target_dag_version with the values 1.0, 2.0, etc. Click a specific task in the graph to access additional views and actions for the task instance. That's only for the sake of this demo. Click on the DAG to have a detailed look at the tasks. Bases: airflow.dag.base_dag.BaseDag, airflow.utils.log.logging_mixin.LoggingMixin. airflow/example_dags/example_external_task_marker_dag.py. The operator allows to trigger other DAGs in the same Airflow environment. Go to your airflow .cfg file and scroll down to the SMTP section. one_success: The task runs as soon as at least one upstream task has succeeded. These are the nodes and. Usually, it implies that the targer_dag has a schedule_interval to None as you want to trigger it explicitly and not automatically. Each section of this guide corresponds to one of the tabs at the top of the Airflow UI. If the task you are waiting for fails, your sensor will keep running forever. Another important thing to remember is that you can wait for an entire DAG Run to complete and not only Tasks by setting those parameters to None. Find the dag from the dag_id you created. Let's see an example. The second upstream DAG is very similar to this one, so I don't show the code here, but you can have a look at the code in Github. .. Airflow UI Task DAG Task . By default, if you dont set any value it is defined as [State.FAILED] which is what you usually want. . With the trigger tasks! Maybe, but thats another question At the end of this article, you will be able to spot when you need to create DAG Dependencies, which method to use, and what are the best practices so you dont fall into the classic traps. Execute DAG in Airflow UI. Like execution_delta, execution_date_fn expects a timedelta which is returned by a function in this case. If you need to re-run tasks in multiple DAG runs, you can do so from this page by selecting all relevant tasks and clearing their status. Airflow uses Directed Acyclic Graphs (DAGs) for orchestrating the workflow. used together with ExternalTaskMarker, clearing dependent tasks can also happen across different wait for another task on a different DAG for a specific execution_date. Dependencies: when you have more than one task or operator, you need to define dependencies to establish the relationship inside a DAG, for example first trigger Task T1 and then T2. For more information, see Managing your Connections in Apache Airflow. You could as state.SKIPPED as well. This guide is an overview of some of the most useful features and visualizations in the Airflow UI. However, always ask yourself if you truly need this dependency. . To open the /dags folder, follow the DAGs folder link for example-environment. This is crucial for this DAG to respond to the upstream DAGs, that is, to add a dependency between the runs of the upstream DAGs and the run of this DAG. Pause/unpause a DAG with the toggle to the left of the DAG name. You absolutely need to take care of something with the ExternalTaskSensor the execution date! Airflow UI provide statistical information about jobs like the time taken by the dag/task for past x days, Gantt Chart, etc. Description Here are some details about my PR, including screenshots of any UI changes: (key/value mode) step 3. exchange tasks info by airflow xcom model. Solution: verify in Airflow worker logs that there are no errors raised by Airflow . But, if you carefully look at the red arrows, there is a major change. E.g. Ready? However, you can set another execution date if you want. The DAG below has the ExternalTaskSensor and waits for task end in target_dag to complete. This can. When doing this (in the GCS dag folder of the cloud compose environment) however, [] Directed Acyclic Graphs (DAGs) are collections of tasks users are able to execute; organized in a way that reflects their relationships and dependencies. As usual, let me give you a very concrete example: In the example above, you have three DAGs on the left and one DAG on the right. Conclusion Use Case E.g. If there were multiple DAG runs on the same day with different states, the color is a gradient between green (success) and red (failure). For example: With the chain function, any lists or tuples you include must be of the same length. ). If the committers decide that the full tests matrix is needed, they will add the label 'full tests needed'. This sensor will lookup past executions of DAGs and tasks, and will match those DAGs that share the same execution_date as our DAG. Dependencies? Step 4: configure SMTP for EmailOperator. What is Airflow Operator? Ideal when a DAG depends on multiple upstream DAGs, the ExternalTaskSensor is the other way to create DAG Dependencies in Apache Airflow. If you're using an older version of the UI, see Upgrading from 1.10 to 2. Airflow represents data pipelines as directed acyclic graphs (DAGs) of operations. Next, we'll put everything together: from airflow .decorators import dag , task from airflow .utils.dates import days_ago from random import random # Use the DAG decorator from Airflow # `schedule_interval='@daily` means the >DAG will run everyday at midnight. Notice that each DAG on the left has the trigger task at the end. To configure DAG-level permissions in Airflow UI: The Admin creates empty roles for grouping DAGs. Then it can execute tasks #2 and #3 in parallel. If DAG A triggers DAG B, DAG A and DAG B must be in the same Airflow environment. However, the failed_states has no default value. Your email address will not be published. Thats why the arrows are opposite, unlike in the previous example. Each DAG object has method "add_task" and "add_tasks" to manual adding tasks to DAG object from different places (without use 'dag' attribute inside task and without defining task in . However, it is sometimes not practical to put all related tasks on the same DAG. An Airflow DAG can become very complex if we start including all dependencies in it, and furthermore, this strategy allows us to decouple the processes, for example, by teams of data engineers, by departments, or any other criteria. A connection id (conn_id) is defined there, and host-name / login / password / schema information attached to it. If you are running Airflow on Astronomer, the Astronomer RBAC will extend into Airflow and take precedence. Wow, this one, I LOVE IT. If your start_date is 2020-01-01 and schedule_interval is @daily, the first run will be created on 2020-01-02 i.e., after your start date has passed. Minimize as much as possible the number of DAG dependencies. Similarly, the XComs page shows a list of all XComs stored in the metadata database and allows you to easily delete them. For example, if the execution_date of your DAG is 2022-01-01 00:00, the target DAG will have the same execution date so you process the same chunk of data in both DAGs. When working with task groups, it is important to note that dependencies can be set both inside and outside of the group. Hit accessible trailsand trainsfor foliage views; forge new traditions at one-of-a-kind festivals; and even hit the beach, while the weather lasts. That means you can inject data at run time that comes from Variables, Connections, etc. So, how to set the delta if the two DAGs dont run on the same schedule interval? For example, in the following DAG code there is a start task, a task group with two dependent tasks, and an end task that needs to happen sequentially. none_failed_min_one_success: The task runs only when all upstream tasks have not failed or upstream_failed, and at least one upstream task has succeeded. Figure 1: The Cloud IDE pipeline editor, showing an example pipeline composed of Python and SQL cells. in production mode, user input their parameter in airflow web ui->admin->variable for certain DAG. Implementation of the TriggerDagRunOperator for DAG Dependencies, The ExternalTaskSensor for Dag Dependencies, Implementation of the ExternalTaskSensor for DAG dependencies, ShortCircuitOperator in Apache Airflow: The guide, DAG Dependencies in Apache Airflow: The Ultimate Guide. It not, it fails immediately. If it is desirable that whenever parent_task on parent_dag is cleared, child_task1 The Graph view shows a visualization of the tasks and dependencies in your DAG and their current status for a specific DAG run. Why DAG dependencies? Thats what you can see in the execution_delta parameter. This might lead to a situation where an Airflow task is marked as Failed and there is no log from its execution. Its funny because it comes naturally to wonder how to do that even when we are beginners. Training model tasks Choosing best model Accurate or inaccurate? You bring the DAG to life by writing the tasks in Python with the help of Airflow operators and Python modules. Workplace Enterprise Fintech China Policy Newsletters Braintrust shaw brothers movies for sale Events Careers imagination stage bethesda maryland The sub-DAGs will not appear in the top-level UI of Airflow, but rather nested within the parent DAG, accessible via a Zoom into Sub DAG button. Users can easily define tasks, pipelines, and connections without knowing Airflow. all_failed: The task runs only when all upstream tasks are in a failed or upstream. Currently the non zero exit code logs as INFO instead of ERROR like this: [2020-09-14 11:02:46,167] {local_task_job.py:102} INFO - Task exited with return code 1. Conclusion Use Case To better illustrate a concept, let's start with the following use case: DAG Example. It is very efficient platform to schedule the data processing jobs which can be. Extremely useful if its actually not the last task to execute, like: TASK A -> TriggerDagRunOperator -> Task B, In addition to this parameter, dont hesitate to set the poke_interval parameter that defines the interval of time to check if the triggered DAG is completed or not. For that, we can use the ExternalTaskSensor. For example, you might only re-train your ML model weekly, even though it uses data that's updated hourly. A DAG (Directed Acyclic Graph) is the core concept of Airflow, collecting Tasks together, organized with dependencies and relationships to say how they should run.

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