![]() ![]() retries: Number of times to retry the DAG in case of a failure.email_on_retry: When True, an email will be sent every time the DAG attempts to retry a failed execution.email_on_failure: When True, a failed execution will email the specified email address with details of the failed job.email: An email address for alert notifications when something goes wrong.start_date: The time at which the DAG should execute.owner: The Airflow user the DAG belongs to (again).args contains high-level configuration values: , dag = dag, ) run_this > task # Įvery DAG starts out with some basic configuration variables. To get started with a barebones Airflow setup, all we need is to install the apache-airflow Python library: Once that's done, you usually need to install and configure three or four different Apache services with obnoxious animal-themed names. In most cases, things start out by installing some highly specific version of Java after getting harassed to create an Oracle account (please kill me). Installing Apache's data services is typically an awful experience. We'll dig deeper into DAGs, but first, let's install Airflow. At various points in the pipeline, information is consolidated or broken out. In the above example, the DAG begins with edges 1, 2 and 3 kicking things off. Interestingly, a "child" edge can also have multiple parents (this is where our tree analogy fails us). ![]() That's it - there's no need for fancy language here.Įdges in a DAG can have numerous "child" edges. Every node has a "parent" node, which of course means that a child node cannot be its parents' parent. If this remains unclear, consider how nodes in a tree data structure relate to one another. The connection of edges is called a vertex. Each "step" in the workflow (an edge) is reached via the previous step in the workflow until we reach the beginning. In computer science, a directed acyclic graph simply means a workflow which only flows in a single direction. Instead, get used to saying DAG.Īirflow refers to what we've been calling "pipelines" as DAGs (directed acyclic graphs). To get started with Airflow, we should stop throwing the word "pipeline" around. Not only can we check the heartbeat of our pipelines, but we can also view graphical representations of the very code we write. Even more impressive is that the code we write is visually represented in Airflow's GUI. By creating our pipelines within Airflow, we gain immediate visibility across all our pipelines to quickly spot areas of failure. Wrangling multiple pipelines which are prone to failure might be the least glorious aspect of any data engineer's job. The more obvious benefits of Airflow are centered around its powerful GUI. By leveraging these tools, engineers begin to see their pipelines abiding by a well-understood format, making code readable to others. Airflow comes with numerous powerful integrations that serve almost any need when it comes to outputting data. In the same way a web framework might help developers by abstracting common patterns, Airflow does the same by providing data engineers with tools to trivialize certain repetitive aspects of pipeline creation. It's not too crazy to group these benefits into two main categories: code quality and visibility.Īirflow provides us with a better way to build data pipelines by serving as a sort of 'framework' for creating pipelines. It won't take much time using Airflow before you wonder how you managed to get along without it.Īirflow provides countless benefits to those in the pipeline business. If you happen to be a data engineer who isn't using Airflow (or equivalent) yet, you're in for a treat. The best part of Airflow, of course, is that it's one of the rare projects donated to the Apache foundation which is written in Python. ![]() It shouldn't take much time in Airflow's interface to figure out why: Airflow is the missing piece data engineers need to standardize the creation of ETL pipelines. It seems like almost every data-heavy Python shop is using Airflow in some way these days. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |