Introduction:
Large enterprises, which have more than one business unit, usually have more than one data platform environment. In fact, companies trying to be agile to make quick decisions, all have business unit-specific data platforms.
With the computational advancements and rapid growth in data analytics and science, data integration tasks for data platforms are increasing exponentially as they need to support transactional as well as batch processing jobs.
Here, the challenge is choosing the orchestration tool, as data integrations have to work in conjunction with different platforms.
Though there are several tools in the market, in the article, we will cover how Apache airflow became the most trusted tool for the orchestration industry and its advantages.
ETL:
Traditional ETL (Extract, Transform, and loading) tools like Informatica, Ab Initio, and Talend all come with their own orchestration process. But as they are proprietary tools, it's expensive as well.
Cloud service providers Azure, AWS (Amazon Web Services), and Google Cloud are all trying cash in the orchestration space so Azure has Synapse and AWS has Glue, Step function, etc.
Basics:
Orchestration is a simple or complex series of workflows defining a process. Workflows depend on one another to perform a business function or a series of business func‐tions calling other services. Then what is the process?
A process is a business function or set of business functions achieved by executing one or more tasks. Then what is the task?
A task is a step in a process. A task may call a service to perform a particular data-related function. Then what is the Service?
A service is software like Azure Synapse Analytics, Python data frame, or AWS DynamoDB that performs tasks.
Common Problems in the Legacy Tools:
The most common problem in the orchestration tools is their ability to successfully implement and maintain processes across the organization. Engineers and managers want to choose a tool that addresses the below issues:
Dependency management:
Lack of dependency management results in maintaining process-to-process and task-to-tasks dependency checks within the codebase along with business logic. This increases development time based on the number of processes or tasks involved in a workflow.
Task modification to existing processes:
Workflow is defined as each step within the code. Embedding the workflow in the code requires manual effort to fully review the code to make changes to one or more tasks resulting in additional time involved in project enhancements and testing.
Troubleshooting:
Troubleshooting a particular process should not require the need to understand the dependencies, as it results in additional support time.
Restart:
It should be fairly easy to restart the failed task or trigger an ad-hoc task. Orchestration should not warrant the need to rerun the entire process if a particular task within the process failed.
User-friendly interface to track the progress:
Orchestration tools should provide best in class user interface with the details to visualize the tasks involved within the processes in one place. Engineers investigating the issue should have a UI to understand the flow of a particular process.
UI should display the current process in execution, the time it's taking, etc.
Apache Airflow
Airflow is a platform to programmatically author, schedule, and monitor workflows geared at data pipelining. Airflow is used to author workflows as DAGs of tasks. The Airflow scheduler executes tasks on an array of workers while following the specified dependencies.
The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
Code Development:
Authoring a data pipeline using existing tasks involves writing a Python module and deploying it to the Airflow installation. Adding a new kind of task to an airflow installation involves writing and deploying Python code in packages called plugins.
This aligns with the primary coding skill set of Data Engineers and scientists, which is Python.
Benefits
At a high level, process dependency management is easy to develop and maintain via user interface (UI) and support in Airflow, which helps immensely to save time for developers and supporting resources.
DAG creation is all in Python scripting language which is the primary coding skillset for Data Engineers. Changes within Airflow can be captured using the GIT repository enabling version controls and change management practices.
Airflow has good documentation and an active internal community for answering questions.
What are the skillsets required to develop and maintain Airflow?
Python is a major skill required to use Airflow. Initial learnings are required to set up Airflow and understand the architecture so it's evident for future maintenance.
Can Engineers visualize the Jobs?
Yes, Airflow has a Web interface to start and stop processes and also to monitor the current status of the processes.
Does Airflow support any integration with Data Quality checkers?
DataDog can be integrated with Airflow.
How do we make ensure Airflow’s availability?
Airflow workers and Web servers can be load balanced. Airflow Scheduler is a single point of failure.
How does Airflow scale?
Airflow Web server can be scaled based on the number of requests. Airflow workers can be scaled up and down based on requirements.
