Learn Apache Airflow in 10 Minutes | High-Paying Skills for Data Engineers
Summary
TLDRThis script introduces Apache Airflow, a popular open-source tool for managing complex data pipelines. It explains how Airflow, initiated by Airbnb and incubated by Apache, allows for the creation, scheduling, and execution of workflows as code, using a Directed Acyclic Graph (DAG) structure. The video also covers the simplicity of using Python scripts for data tasks and the scalability issues that arise with numerous pipelines, which Airflow addresses. It highlights Airflow's user-friendly interface, customizable nature, and community support, encouraging viewers to explore an end-to-end project for hands-on experience.
Takeaways
- 😀 Data Engineers often build data pipelines to transform and load data from multiple sources.
- 🔧 Initially, simple Python scripts can be used for data pipeline tasks, but managing multiple pipelines can be challenging.
- ⏰ Cron jobs can schedule scripts to run at specific intervals, but they are not scalable for hundreds of data pipelines.
- 🌐 The vast amount of data generated in recent years drives the need for efficient data processing and pipelines in businesses.
- 🌟 Apache Airflow is a popular open-source tool for managing data workflows, created by Airbnb and now widely adopted.
- 📈 Airflow's popularity stems from its 'pipeline as code' philosophy, allowing customization and scalability.
- 📚 Apache Airflow is a workflow management tool that uses Directed Acyclic Graphs (DAGs) to define tasks and their dependencies.
- 🛠️ Operators in Airflow are functions used to create tasks, with various types available for different operations like running Bash commands or sending emails.
- 💡 Executors in Airflow determine how tasks run, with options for sequential, local, or distributed execution across machines.
- 📊 The Airflow UI provides a visual representation of DAGs, tasks, and their statuses, making it easy to manage and monitor data pipelines.
- 🚀 For practical learning, building a Twitter data pipeline using Airflow is recommended as a project to understand real-world applications of the tool.
Q & A
What is a data pipeline in the context of data engineering?
-A data pipeline in data engineering is a process that involves extracting data from multiple sources, transforming it as needed, and then loading it into a target location. It's a way to automate the movement and transformation of data from one place to another.
Why might a simple Python script be insufficient for managing data pipelines?
-A simple Python script might be insufficient for managing data pipelines, especially as the number of pipelines grows, because it can become complex and difficult to manage. Tasks might need to be executed in a specific order, and handling failures or scheduling can be challenging.
What is a Cron job and how is it used in data pipelines?
-A Cron job is a time-based job scheduler in Unix-like operating systems. It is used to schedule scripts to run at specific intervals. In the context of data pipelines, Cron jobs can be used to automate the execution of scripts at regular times, but it can become cumbersome when managing many pipelines.
What is Apache Airflow and why is it popular?
-Apache Airflow is an open-source workflow management tool designed to schedule and monitor data pipelines. It became popular due to its 'pipeline as code' philosophy, which allows data pipelines to be defined in Python scripts. It is widely adopted because it is open source, customizable, and supports complex workflows.
What does the term 'DAG' stand for in Apache Airflow?
-In Apache Airflow, 'DAG' stands for Directed Acyclic Graph. It is a collection of tasks that are defined in a way that they are executed in a specific order, with no loops, making it a blueprint for the workflow.
How does Apache Airflow handle the execution of tasks?
-Apache Airflow uses executors to determine how tasks are run. Different types of executors are available, such as Sequential Executor for sequential task execution, Local Executor for parallel task execution on a single machine, and Celery Executor for distributing tasks across multiple machines.
What is an operator in Apache Airflow and what role does it play?
-An operator in Apache Airflow is a function provided by Airflow to create tasks and perform specific actions. Operators can be used to execute tasks like running Bash commands, calling Python functions, or sending emails, making it easier to manage different types of tasks in a pipeline.
How can one define a DAG in Apache Airflow?
-In Apache Airflow, a DAG is defined using the DAG function from the Airflow library. You provide parameters such as the name, start date, schedule, and other parameters to configure the DAG. Tasks are then added to the DAG using operators like PythonOperator or BashOperator.
What is the significance of the 'pipeline as code' concept in Apache Airflow?
-The 'pipeline as code' concept in Apache Airflow allows data pipelines to be defined in code, typically Python scripts. This makes it easier to version control, test, and modify pipelines, as well as collaborate on them, similar to how software development works.
How can one visualize the workflow in Apache Airflow?
-The workflow in Apache Airflow can be visualized through the Airflow UI, which provides a graphical representation of DAGs. This visual representation helps in understanding the sequence of tasks, their dependencies, and the overall structure of the data pipeline.
What is an example project that can be done using Apache Airflow?
-An example project that can be done using Apache Airflow is building a Twitter data pipeline. This involves extracting data from a Twitter API, performing transformations, and then loading the data into a storage system like Amazon S3. Although the Twitter API mentioned is not valid anymore, similar projects can be done with other APIs.
Outlines
🔧 Introduction to Data Pipelines and Apache Airflow
This paragraph introduces the concept of building data pipelines as a Data Engineer, which involves extracting data from various sources, transforming it, and loading it into a target location. It discusses the use of Python scripts for this purpose and the limitations of using Cron jobs for scheduling tasks, especially when dealing with a large number of data pipelines. The paragraph also highlights the importance of data in modern businesses and the role of data pipelines in personalized recommendations and advertisements. It concludes with an introduction to Apache Airflow, a data pipeline tool developed by Airbnb, which became popular due to its 'pipeline as code' philosophy and open-source nature, allowing for customization and scalability.
🛠 Understanding Apache Airflow's Core Components
The second paragraph delves into the specifics of Apache Airflow, explaining its components and how it simplifies the management of complex data pipelines. It starts by discussing the Cron job's inadequacy for managing numerous pipelines and introduces the Directed Acyclic Graph (DAG) concept, which is the core of Airflow's workflow management. The paragraph explains that a DAG is a blueprint defining tasks and their dependencies. It also introduces operators as functions provided by Airflow to create tasks for different operations, such as running Bash commands or Python functions. The paragraph further explains the role of executors in determining how tasks are run, with options for sequential, local, or distributed execution across machines.
📊 Practical Overview of Airflow's UI and DAG Execution
This paragraph provides a practical overview of Apache Airflow's user interface and the execution of DAGs. It describes how to declare a DAG in Python, including setting parameters like name, start date, and schedule. The paragraph illustrates the use of the Dummy Operator and the creation of task dependencies to ensure tasks execute in a specific sequence. It also explains how to view and manage DAGs through the Airflow console, including monitoring their status such as queued, running, successful, or failed. The paragraph concludes with an example of enabling and manually running a DAG, and observing its progression and outcome within the Airflow UI.
🐦 Building a Twitter Data Pipeline with Apache Airflow
The final paragraph discusses a project involving the creation of a Twitter data pipeline using Apache Airflow. Although the Twitter API mentioned is no longer valid, the paragraph suggests using alternative free APIs for a similar project. It provides a brief explanation of the code involved in the project, which includes defining a function to extract data from the Twitter API, perform transformations, and store the data on Amazon S3. The paragraph also outlines the structure of the 'twitter_dag.py' file, detailing how to define a DAG, tasks, and dependencies within Airflow. It concludes by recommending a project for beginners to gain hands-on experience with Airflow and to solidify their understanding of its practical applications.
Mindmap
Keywords
💡Data Pipeline
💡Cron Job
💡Apache Airflow
💡Directed Acyclic Graph (DAG)
💡Operators
💡Executors
💡Workflow Management Tool
💡Tasks
💡Data Transformation
💡Twitter Data Pipeline
Highlights
Building a data pipeline involves taking data from multiple sources, transforming it, and loading it onto a target location using Python scripts.
Cron jobs can schedule scripts to run at specific intervals but are not efficient for managing hundreds of data pipelines.
90% of the world's data was generated in the last 2 years, highlighting the importance of data processing in business.
Apache Airflow is a highly used data pipeline tool introduced by Airbnb engineers in 2014 and open-sourced in 2016.
Airflow's popularity stems from its 'pipeline as code' philosophy, allowing for customization and open-source accessibility.
Airflow is a workflow management tool that uses Directed Acyclic Graphs (DAGs) to define tasks and their dependencies.
DAGs in Airflow are a visual representation of tasks with directed, acyclic movement, ensuring no looping.
Operators in Airflow are functions used to create tasks, with different types available for various operations like Bash commands or Python functions.
Executors in Airflow determine how tasks run, with options for sequential, local, or distributed task execution.
Airflow's UI provides a centralized place to manage, monitor, and visualize data pipelines.
The Airflow UI displays the status of DAGs, including queued, running, successful, failed, and more.
Airflow allows for the creation of complex data pipelines with multiple dependencies and tasks.
The video provides an example of building a Twitter data pipeline using Airflow, demonstrating practical application.
The presenter offers a project for beginners to build a Twitter data pipeline using Airflow to understand its real-world application.
Airflow's simplicity and the presenter's aim to demystify technical concepts make it accessible for learners.
The video concludes with a call to action for viewers to subscribe and like for more simplified technical content.
Transcripts
One of the tasks you will do as a Data Engineer is to build a data pipeline. Basically, you take data
from multiple sources, do some transformation in between, and then load your data onto some
target location. Now, you can perform this entire operation using a simple Python script. All you
have to do is read data from some APIs, write your logic in between, and then store your data
onto some target location. There is something called a Cron job. So, if you want to run your
script at a specific interval, you can schedule it using Cron job. It looks something like this.
But here's the thing: you can use Cron job for, let's say, two to three scripts,
but what if you have hundreds of data pipelines? We know that 90% of the world's data was generated
in just the last 2 years, and businesses around the world are using this data to improve their
products and services. The reason you see the correct recommendation on your YouTube page or the
correct ads on your Instagram profile is because of all of these data processing. There are more
than thousands of data pipelines running in these organizations to make all of these things happen.
So today, we will understand how all of these things happen behind the scene,
and we will understand one of the highly used data pipeline tools in the market,
called Apache Airflow. So, are you ready? Let's get started.
At the start of this video, we talked about the Cron job. As the data grows, we will have
to create more and more data pipelines to process all of these data. What if something fails? What
if you want to run all of these operations in a specific order? So, in a data pipeline,
we have multiple different operations coming. So, one task might be to extract data from RDBMS,
APIs, or some other sources. Then the second script will aggregate all of these data,
and the third script will basically store this data onto some location. Now, all of
these operations should happen in a specific sequence only, so we will have to make sure
we schedule our Cron job in such a way that all of these operations happen in proper sequence.
Now, doing all of these operations using a simple Python script and managing them is a headache. You
might need to put a lot of engineers on each and individual task to make sure everything
runs smoothly. And this is where, ladies and gentlemen, Apache Airflow comes into the picture.
In 2014, engineers at Airbnb started working on a project, Airflow. It was brought into the Apache
Software Incubator program in 2016 and became open source. That basically means anyone in
the world can use it. It became one of the most viral and widely adopted open-source projects,
with over 10 million pip installs over a month, 200,000 GitHub stars, and a Slack
community of over 30,000 users. Airflow became a part of big organizations around the world.
The reason Airflow got so much popularity was not because it was funded or it had a
good user interface or it was easy to install. The reason behind the popularity of Airflow was
"pipeline as a code." So before this, we talked about how you can easily write our data pipeline
in a simple Python script, but it becomes very difficult to manage. Now, there are other options,
such as you can use enterprise-level tools such as Alteryx, Informatica,
but these software are very expensive. And also, if you want to customize based on your use case,
you won't be able to do that. This is where Airflow shines. It was open source, so anyone
can use it, and on top of this, it gave a lot of different features. So, if you want to build,
schedule, and run your data pipeline on scale, you can easily do that using Apache Airflow.
So now that we understood why Apache Airflow and why we really need it in the first place,
let's understand what Apache Airflow is. So, Apache Airflow is a workflow management tool.
A workflow is like a series of tasks that need to be executed in a specific order. So, talking about
the previous example, we have data coming from multiple sources, we do some transformation in
between, and then load that data onto some target location. So, this entire job of extracting,
transforming, and loading is called a workflow. The same terminology is used in Apache Airflow,
but it is called a DAG (Directed Acyclic Graph). It looks something like this.
At the heart of the workflow is a DAG that basically defines the collection of different
tasks and their dependency. This is the core computer science fundamental. Think
of it as a blueprint for your workflow. The DAG defines the different tasks that should
run in a specific order. "Directed" means tasks move in one direction, "acyclic" means
there are no loops - tasks do not run in a circle, it can only move in one direction,
and "graph" is a visual representation of different tasks. Now, this entire
flow is called a DAG, and the individual boxes that you see are called tasks. So,
the DAG defines the blueprint, and the tasks are your actual logic that needs to be executed.
So, in this example, we are reading the data from external sources and API, then we aggregate data
and do some transformation, and load this data onto some target location. So, all of these
tasks are executed in a specific order. Once the first part is completed, then only the second part
will execute, and like this, all of these tasks will execute in a specific order.
Now, to create tasks, we have something called an operator. Think of the operator as a function
provided by Airflow. So, you can use all of these different functions to create the task and do the
actual work. There are many different types of operators available in Apache Airflow. So,
if you want to run a Bash command, there is an operator for that, called the Bash Operator. If
you want to call a Python function, you can use a Python Operator. And if you want to send an email,
you can also use the Email Operator. Like this, there are many different operators available for
different types of jobs. So, if you want to read data from PostgreSQL, or if you want to store your
data to Amazon S3, there are different types of operators that can make your life much easier.
So, operators are basically the functions that you can use to create tasks,
and the collection of different tasks is called a DAG. Now, to run this entire DAG,
we have something called executors. Executors basically determine how your tasks will run. So,
there are different types of executors that you can use. So,
if you want to run your tasks sequentially, you can use the Sequential Executor. If you want to
run your tasks in parallel in a single machine, you can use the Local Executor. And then, if you
want to distribute your tasks across multiple machines, then you can use the Celery Executor.
This was a good overview of Apache Airflow. We understood why do we need Apache Airflow
in the first place, how it became popular, and what are the different components in
Apache Airflow that make all of these things happen. So, I will recommend an
end-to-end project that you can do using Apache Airflow at the end of this video. But for now,
let's do a quick exercise of Apache Airflow to understand different components in practice.
So, we understood the basics about Airflow and what are the different components that are
attached to Airflow. Now, let's look at a quick overview of what the Airflow UI really looks
like and how these different components come together to build the complete data pipeline.
Okay, so we already talked about DAGs, right? So, Directed Acyclic Graph is a core concept in
Airflow. Basically, a DAG is the collection of tasks that we already understood. So,
it looks something like this: A is the task, B is the task, D is the task, and sequentially
it will execute and it will make the complete DAG. So, let's understand how to declare a DAG.
Now, it is pretty simple. You have to import a few packages. So, from Airflow,
you import the DAG, and then there is the Dummy Operator that basically does nothing. So,
with DAG, this is the syntax. So, if you know the basics of Python, you can start with that. Now,
if you don't have the Python understanding, then I already have courses on Python,
so you can check that out if you want to learn Python from scratch.
So, this is how you define the DAG. With DAG, then you give the name, you give the start date,
so when you want to run this particular DAG, and then you can provide the schedule. So, if
you want to run daily, weekly, monthly basis, you can do that. And there are many other parameters
that this DAG function takes. So, based on your requirement, you can provide those parameters,
and the DAG will run according to all of those parameters that you have provided.
So, this is how you define the DAG. And if you go over here, you can use the Dummy Operator,
where you give basically the task, the task name, or the ID, and you provide the DAG that
you want to attach this particular task to. So, as you can see it over here, we define the DAG,
and then we provide this particular DAG name to the particular task. So, if you are using the
Python Operator or Bash Operator, all you have to do is use the function and provide the DAG name.
Now, just like this, you can also create the dependencies. So, the thing that we talked about,
right? I want to run my, uh, all of these tasks in the proper sequence. So, as you can see,
I provide the first task, and then you can use something like this. So, what will happen,
the first task will run, and it will execute the second and third tasks together. After the third
task completes, the fourth task will be executed. So, this is how you create the basic dependencies.
Now, uh, this was just documentation, and you can always read about it if you want
to learn more. So, let's go to our Airflow console and try to understand this better.
Okay, once you install Apache, it will look something like this. You will be redirected
to this page, and over here, you will see a lot of things. So, first is your DAGs. These are the
example DAGs that are provided by Apache Airflow. So, if I click over here, and if I go over here,
you will see, uh, this is the DAG, which basically contains one operator, which is the Bash Operator.
Just like this, if you click onto DAGs, you will see a lot of different examples. If you want to
understand how all of these DAGs are created from the backend, over here, you will get the
information about the different runs. If your DAG is currently queued, if it's successful,
running, or failed, this will give you all of the different information about the recent tasks.
So, I can go over here, I can just enable this particular DAG. Okay, I can go inside this,
and I can manually run this from the top. Okay, so I will trigger the DAG,
and it will start running. So, currently, it is queued. Now it starts running,
and if I go to my graph, you will see it is currently running. Uh,
if you keep refreshing it, so as you can see, this is successful. So, our DAG ran successfully.
Now, there are other options, such as like failed, queued, removed,
restarting, and all of the different statuses that you can track if you want to do that. So,
this is what makes Apache Airflow a very popular tool because you can do everything
in one place. You don't have to worry about managing these things at different places. So,
at one single browser, you will be able to do everything.
So, all of the examples that you see it over here are just basic templates. So,
if I go over here and check onto example_complex, you will see a graph which is this complicated,
right? You will see a lot of different things. So, we have like entry group,
and then entry group is, uh, dependent on all of these different things. So, the graph
is pretty complex. So, you can create all of these complex pipelines using Airflow.
Now, one of the projects that you will do after this is build a Twitter data pipeline. Now,
Twitter API is not valid anymore, but you can always use different
APIs available in the market for free and then create the same project. So,
I'll just explain to you this code so that you can have a better understanding.
So, I have defined the function as run_twitter_etl, and the name
of the file is twitter_etl, right? Uh, this is the basic Python function. So,
what we are really doing is extracting some data from the Twitter API,
doing some basic transformation, and then storing our data onto Amazon S3.
Now, this is my twitter_dag.py. So, this is where I define the DAG of my Airflow. Okay,
so as you can see it over, we are using the same thing. From Airflow,
import DAG. Then, from PythonOperator, I'm using the PythonOperator because I want to
run this particular Python function, which is run_twitter_etl, using my Airflow DAG. Okay,
so I first defined the parameters, which is like the owner, start time, emails,
and all of the other things. Then, this is where I define my actual DAG. So, this is my DAG name,
these are my arguments, and these are my description. So, you can write whatever you want.
Now, I define one task. So, in this example, I only have one task. So,
PythonOperator, I provide the task ID, Python callable, I provide the function name. Now,
this function is, I import it from the twitter_etl, which is the second file, uh,
this one. So, twitter_etl, from twitter_etl, I import the run_twitter_etl function,
and I call it inside my PythonOperator. So, I call that function using my PythonOperator,
and then I attach it to the DAG. And then, at the end, I just provide the run_etl.
Now, in this case, if I had like different operators, such as I can have like run_etl1,
run_etl2, something like this, okay? So, I can do something like this: run_etl1,
run_etl2. And then, I can create the dependencies also. So, then etl1, then etl2. So, this will
execute in a sequence manner. So, once this executes, then this will execute, this and this.
So, I just wanted to give you a good overview about Airflow. Now,
if you really want to learn Airflow from scratch and how to install and each and everything,
I already have one project available, and the project name is the Twitter data
pipeline using Airflow for beginners. So, this is the data engineering project that
I've created. I will highly recommend you to do this project so that you will
get a complete understanding of Airflow and how it really works in the real world.
I hope this video was helpful. The goal of this video was not to make you a master of
Airflow but to give you a clear understanding of the basics of Airflow. So, after this,
you can always do any of the courses available in the market, and then you can easily master
them because most of the people make technical things really complicated. And the reason, uh,
I started this YouTube channel is to simplify all of these things.
So, if you like these types of content, then definitely hit the subscribe button,
and don't forget to hit the like button. Thank you for watching. I'll see you in the next video.
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