Streamlining the data delivery pipeline is critical for any data-related project. With the help of ETL (extract, transfer, load) processes, you can gather, clean, as well as analyze relevant data later on. With the help of this data, you can increase business process efficiency, optimize costs, or improve the quality of provided services. That is why ETL processes are of prime importance.
There are many popular tools that can help you with this task. In this article, we will focus on the Airflow ETL.
What are the peculiarities of this tool, how to use it effectively, and why choose it over other similar tools? Let’s discover together.
What is ETL Apache Airflow?
Apache Airflow ETL is an open-source platform that creates, schedules, and monitors data workflows. It allows you to take data from different sources, transform it into meaningful information, and load it to destinations like data lakes or data warehouses. This service manages all needed steps before using prepared, clean data for your business needs.
Apache Airflow for ETL is a scalable solution that can bring many benefits to your business. Let’s take a closer look at them.
- Scalability. It makes the platform suitable for businesses of any size, from startups to enterprises;
- No need for rare talent. Workflows in Apache Airflow ETL are defined, planned, and executed using Python codes;
- Prompt development process. Complex data pipelines with many internal dependencies between tasks in the workflow are defined quickly and robustly with the help of Apache Airflow for ETL;
- Efficiency. Numerous functions that simplify monitoring and troubleshooting make the work more effective;
- Ease of customization. ETL Airflow can be extended with various plug-ins, macros, and user-defined classes.
However, to make ETL Airflow work, you need to find top-notch experts that follow Airflow best practices for ETL. What are those? Let’s find out together.
Airflow best practices for ETL
Around 15% of all data projects succeed, while the rest either do not meet the original expectations or fail altogether. To increase your chances for success, you must follow the fundamental best practices of Airflow best practices ETL.
1. Set clear goals for your project
The lack of vision of the aims that project stakeholders want to achieve is the second-most-common reason why many data projects fail. Initiating such projects without clear goals leads to various new requirements or unpredictable challenges. This, in turn, may result in development delays, cost overheads, and, sometimes even in complete project failure. Thus, the kick-off stage of any data project should include outlining your goals, both on the business (e.g., raising profits, predicting equipment failure, etc.) and the technical (automating processes, reducing latency, etc.) sides.
2. Conduct a thorough assessment of your project
Going through an extensive data audit (which is typically a part of the Discovery Phase) before starting the project helps put your data-related project on the right track. This step is typically held by your vendor, who uses their expertise to analyze the infrastructure you have in place and find areas for enhancement.
After conducting the Discovery Phase, your partner can provide you with a clear project roadmap, complete with recommendations on the optimization, tools, and optimal tech stack. Successful completion of the project afterward is just a matter of following the outlined steps.
3. Find the right Airflow ETL services provider
The lack of technical expertise is the most common reason for data project failure. Streamlining data pipelines with Apache Airflow for ETL requires robust technical expertise. Therefore, you have to ensure that you have professionals with the relevant tech skillset to deliver such a project successfully.
Team extension services provide a perfect alternative when finding experts locally happens to be challenging. Finding the right partner, which we will cover in more detail later in the article, will allow you to access a larger talent pool, making it easier to fill any technical gap.
Apart from that, your development team should follow these Airflow best practices for ETL. For instance:
Workflows should be updated regularly:
- Your developers should keep Python-based Airflow workflows up to date for them to run efficiently. To achieve it, you Airflow ETL experts should sync them to the Github repository;
- Bashoperator and pull requests should be synchronized with the directory. It is a good idea to make a pull request at the beginning of a workflow;
- All the files used in workflows (e.g., scripts of machine learning models) should also be synced using Github.
A proper purpose for DAG (Python script containing tasks and dependencies) should be set:
- Before creating a DAG, define and interpret its purpose correctly;
- All the components (e.g., the input of DAG, the resultant output, the triggers, the integrations, third-party tools) should be planned carefully;
- The DAG complexity has to be minimized to achieve easier maintenance. DAG must have a clearly defined motto, e.g., exporting data warehouses or updating models.
Priorities should be established:
- Your developers should use the Priority_weight parameter to control the priority of workflows. Doing so helps to avoid temporary workflows occurring when several workflows compete for execution;
- It is also a good idea to use multiple schedulers to optimize delays of workflows.
Service Level Agreements (SLA) have to be set:
- Your team should define the deadline for the entire task. If the task is not completed, the person in charge is notified, and the event is logged. This activity helps to understand the cause of the delay and avoid such delays in the future.
How to choose a trusted Airflow ETL service provider?
You need a reliable provider to deliver the Apache Airflow ETL project successfully. The vendor you are looking for is the one that follows Airflow best practices ETL, as well as Infosecurity practices and more. Let’s analyze the aspects you should pay close attention to when selecting your partner for streamlining Apache Airflow for ETL processes.
1. Browse through the services and expertise of your vendor-to-be
Ensure that the portfolio of services and technical expertise of your potential partner matches all the requirements of your project. Pay particular attention to the following services:
This expertise is not only crucial for cloud infrastructure. On-premise solutions can be moved to the cloud to optimize performance without compromising security. So, both on-premise and cloud-based infrastructures benefit if you choose to partner with a vendor that has expertise in Azure, Amazon, and Google Cloud.
DevOps expertise is critical when it comes to deploying and optimizing any data delivery pipeline, and ETL pipeline Airflow is no exception. Thus, it is vital that your partner can provide you with DevOps professionals who have experience in CI/CD, automation & orchestration, Infrastructure as a Code, monitoring & logging, and cloud & infrastructure providers.
2. Take a look at the experience of your vendor-to-be
Since streamlining Airflow ETL pipelines is a challenging task, your partner should have solid experience with such projects. Therefore, you need to closely look at their previous Apache Airflow for ETL services partnerships to make sure that they have sufficient experience. To do so, you should browse through the partner’s website for case studies and success stories. You can also use such platforms as The Manifest, Forrester, and Clutch.co, which provide client testimonials.
3. Assess the data protection plan and policies
The team responsible for streamlining the ETL Apache Airflow pipelines has direct access to your data and infrastructure. So, it is critical to ensure that your internal systems are strongly protected.
You must assess the data protection policies of your partner-to-be. Does it have a solid data protection plan? What tools do they use to prevent data breaches? Do they regularly inform their employees about the most recent data protection trends? Does your tech partner comply with the necessary data protection standards? All these questions must be asked and answered before starting the cooperation.
Apache Airflow for ETL offers the possibility to integrate cloud data with on-premises data easily. The platform is vital in any data platform and cloud and machine learning projects.
ETL Airflow is highly automated, easy to use, and provides benefits, including increased security, productivity, and cost-optimization.
Why choose N-iX for streamlining Airflow ETL processes?
- N-iX Data Unit unites 140+ experts that have experience developing solutions in the domains, such as big data, data analytics, data science, AI & ML;
- We have over 45+ DevOps professionals on board who have delivered over 50 projects of different complexity successfully;
- N-iX has solid cloud expertise and partners with top cloud vendors such as is Microsoft Gold Partner, Amazon Consulting Partner, and Google Cloud Partner;
- With 20+ years of experience providing IT outsourcing services, including services of Airflow ETL;
- We deliver projects across many domains, such as manufacturing, telecom, fintech, and more.