Are you wondering why people are shifting to Apache Airflow? Why are they trying to acquire Apache solutions and services? And is this beneficial for you as well?
Keep reading, your answer is right inside the article.
ETL was the traditional way of data integration. So before moving further let’s discuss the problems associated with ETL Data.
Introduction To ETL
ETL is a data integration process. It is a process that extracts, transforms, and loads data from multiple sources. And take it to a data warehouse or other unified data repository. It provides the foundation for data analytics and machine learning workstreams.
Traditional ETL Data Pipeline
ETL has 3 different phases which are
- Extracting data from different source systems.
- Transformation is where the core business logic comes into the picture.
- Loading is the process of loading data into your target system.
But again, ETL also provides certain benefits which include
- Easy to use.
- Better for complex rules and transformations.
- Inbuilt error handling functionality.
- Advanced Cleansing functions.
- Save cost.
- Generates higher revenue.
- Enhances performance.
ETL, even after being easy to use, has some drawbacks which are:
- Running all three steps just because there is some issue with one step could be a problematic situation. This consumes a lot of time.
- Another problem associated with this is how we can schedule it.
- How can you notify the end-user?
- How can you monitor the deployed data pipeline?
- Hence in the traditional ETL data pipeline, there are a lot of problems and it is for batch processing basically.
Apache Airflow has successfully overcome all the above drawbacks of ETL. Soon you will come to know-how.
Is Airflow an ETL Tool?
Airflow is a workflow management system (not an ETL tool). Where you can automate your existing or new ETL pipeline.
It is built on top of Directed Acyclic Graph (DAG) which is used to create our pipelines.
Important Features of Apache Airflow
What is DAG?
In computer science and mathematics, a directed acyclic graph (DAG) refers to a directed graph. DAG has no directed cycles. This means that it is impossible to traverse the entire graph starting at one edge.
The edges of the directed graph only go one way. The graph is a topological sorting, where each node is in a certain order.
Advantages of using DAG technology
- Speed, is perhaps its greatest advantage. Unlike blockchain the more transactions it has to process its the response speed will be faster.
- Higher level of scalability. By not being subject to limitations on block creation times, a greater number of transactions can be processed. This is particularly attractive in the application of the Internet of Things.
Apache Airflow is an open-source tool to programmatically author, schedule, and monitor workflows. In Apache Airflow we can create an Airflow Pipeline using python (deeply integrated with python).
Ok, let’s see this in a well-defined manner so first let’s understand what a pipeline is.
A Data Pipeline consists of a sequence of actions that can ingest raw data from multiple sources.
Which then transform them and load them to a storage destination. A Data Pipeline may also provide you with end-to-end management. And has features that can fight against errors and bottlenecks.
Schedulers
Schedulers are the time when an ETL data pipeline starts executing.
The Apache airflow scheduler monitors all tasks and all DAGs. It also triggers the task instances whose dependencies have been met.
Behind the scenes, it monitors and stays in sync with a folder for all DAG objects it may contain, and periodically (every minute or so) inspects active tasks to see whether they can be triggered.
Airflow Scheduler Task
The Airflow Scheduler reads the data pipelines. This is represented as Directed Acyclic Graphs (DAGs). This helps in scheduling the contained tasks, monitors the task execution, and then triggers the downstream tasks.
These all are done once their dependencies are met.
Historically, Airflow has had excellent support for task execution. Which is ranging from a single machine to Celery-based distributed execution. This is on a dedicated set of nodes, to Kubernetes-based distributed execution on a scalable set of nodes.
Executors
One that makes Airflow strong in the data engineering market are the Executors.
Executors are the mechanism by which task instances get run. They have a common API and are “pluggable”. This means you can swap executors based on your installation needs.
And thus, Airflows are highly scalable
One of Apache Airflow’s biggest strengths is its ability to scale with good supporting infrastructure.
Another way to scale Airflow is by using operators to execute some tasks remotely.
Hence, we can say that Airflow is a distributed system, that is highly scalable, and can be connected to various sources making it flexible.
Now you are somewhere aware of the basics of airflow. But do you know where you can use Airflow? Well to know this keep reading.
We can use it in a batch ETL pipeline.
You can use Airflow transfer operators together with database operators to build ELT pipelines.
Airflow provides a vast number of choices to move data from one system to another. This can be ok if your data engineering team is proficient with Airflow. Along with this, they must know the best practices around data integration.
Machine learning pipelines train/test pipelines.
An ML pipeline allows you to automatically run the steps of a Machine Learning system. Done from data collection to model serving (as shown in the photo above).
It will also reduce the technical debt of a machine learning system.
Airflow is not just for data engineering it is also for science engineers. This is a really important point to consider.
Airflow is for batch ETL pipelines. Hence, Airflow is not for real time data which means it is not for streaming.
When you want to install Airflow there are two major components Of Airflow
- The database
- Airflow
So, you can choose the database but if you are not choosing a database there will be a default one which is SQLite.
This default database has some issues that it will have a single read and single write. Hence you cannot run the multiple data flows.
Single Source of Data
Metadata is the place where all the data is stored. How many times is the resulting successful? And how many times it is a failure?
It is the single source of data regarding everything you did. From scheduling to the number of tasks running, when are you going to execute your next task, your logs, etc.
Web Server
Now since you installed Apache Airflow but what about monitoring the logs?
If you want to know the success and failure and the upcoming execution etc. for this, we have a very fantastic and decent UI.
This will talk to your metadata and give you all the required information for the DAGs.
You can also run the DAG from the UI.
There is a default scheduler in Apache Airflow that talks to your metadata. Since metadata has all the information.
Executer is the core component of Apache Airflow. In simple words, the executor is the guide that runs your ETL pipeline and also collects the status.
Workers
To turn Apache into a multi-process, multi-threaded web server Apache also has the worker MPM.
It has different python files like one for hitting the data, and another for doing some data transfer which means workers are the place where the ETL pipeline runs.
These above components were for standalone which is nothing but local executors.
But Why One Must Go For Apache Airflow?
Here is a list of benefits associated with Apache Airflow
Besides Apache web server, there are many other popular options. Each web server application has been created for a different purpose.
While Apache web server is the most widely used, it has quite a few alternatives and rivals.
An Apache web server can be an excellent choice to run your website on a stable and versatile platform. The reasons for this are as follows:
- Open-source and free, even for commercial use.
- Reliable, stable software.
- Frequently updated security patches.
- Flexible due to its module-based structure.
- Easy to configure, beginner-friendly.
- Cross-platform (works on both Unix and Windows servers).
- Optimal deliverability for static files and compatibility with any programming language (PHP, Python, etc.)
- Works out of the box with WordPress sites.
- Huge community and easily available support in case of any problem.
Is Apache Installation an easy task? No, Apache Airflow installation and integration is a complex process and thus requires expertise for this.
Apache is the latest technology meant to ease your work, and implementing it as your workflow management system could really benefit you in 2022.
Stay tuned and keep reading the articles if you wish to know about the Apache installation process.
