Spark SQL offers equivalents to all of the operations that may be present within your queries, so the transition will definitely be seamless. I also recommend relying on helper functions so that you don’t find yourself copy-pasting the same bits of code within different tasks. I strongly recommend using the PySpark module and then using Airflow’s PythonOperator for your tasks that way you get to execute your Spark jobs directly within the Airflow Python functions. Now all you need to do is to use Spark within your Airflow tasks to process your data according to your business needs. And through Spark SQL, it allows you to query your data as if you were using SQL or Hive-QL. Spark no longer needs an introduction, but in case you’re unfamiliar with the distributed data-processing framework that took the world by storm since it was open sourced in 2013, this 15-minute tutorial by Simplilearn will surely get you up to speed.Īs long as you’re running it on a cluster adequate to the size of your data, Spark offers ridiculously fast processing power. Now you can use its powerful capabilities to manage your data pipelines by conceiving your pipelines via Airflow’s DAGs system. Having an Airflow server and scheduler up and running is a few commands away and in a few minutes you could find yourself navigating the friendly user interface of your own Airflow web-server, which is quite easy to master:Ĭonnecting Airflow to your data management systemĪnd that’s literally all you need to do to have an up and running Airflow server integrated within your data architecture. With Airflow that can be easily done through the use of X-COM functions that rely on Airflow’s own database to store data you need to pass from one task to another

X-COM: in a wide array of business cases, the nature of your pipeline may require that you pass information between the multiple tasks.Scheduler: unlike other workflow management tools within the Big Data universe (notably Luigi), Airflow has its own scheduler which makes setting up the pipeline even easier.

Tasks: this is where all the fun happens Airflow’s DAGs are divided into tasks, and all of the work happens through the code you write in these tasks (and yes, you can literally do anything within an Airflow task).

DAGs ( Directed Acyclic Graphs): Airflow uses this concept to structure batch jobs in an extremely efficient way, with DAGs you have a big number of possibilities to structure your pipeline in the most suitable way.

The workflow management system that was first introduced by Airbnb back in 2015 has gained a lot of popularity thanks to its powerful user interface and its effectiveness through the use of Python.Īirflow relies on four core elements that allow it to simplify any given pipeline:

First, let Airflow organize things for youĪpache Airflow is one of those rare technologies that are easy to put in place yet offer extensive capabilities. One pipeline that can be easily integrated within a vast range of data architectures is composed of the following three technologies: Apache Airflow, Apache Spark, and Apache Zeppelin. No matter which technology you use to store data, whether it’s a powerful Hadoop cluster or a trusted RDBMS (Relational Database Management System), connecting it to a fully-functioning pipeline is a project that’ll reward you with invaluable insights. Luckily for us, setting up a Big Data pipeline that can efficiently scale with the size of your data is no longer a challenge since the main technologies within the Big Data ecosystem are all open-source. Maximizing the exploitation of this new black gold is the fastest way towards success, because data offers an enormous amount of answers, even to questions you still haven’t thought of yet. Every bit of data stored on the systems of your company, no matter its field of activity, is valuable. In this data-driven era, there is no piece of information that can’t be useful. Click to expand.“black tunnel interior with white lights” by Jared Arango on Unsplash

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