Apache Airflow: Is It a Good Tool for Data Quality Checks?

This article explains an alternate option for Apache Airflow to manage or run data quality checks.

Airflow is an open-source platform that has become increasingly popular for managing data pipelines.

And why not, it provides a convenient way to automate and organize the workflow of tasks related to data processing, making it easier for organizations to manage their data pipelines efficiently.

However, while Airflow has proven to be a powerful tool for managing data pipelines, it has significant limitations regarding managing data quality. Needless to say, compromised data quality is of no good to the organization.

In this blog, we’ll discuss some challenges while using Airflow and why you might need to reconsider this choice for data quality-related tasks. By the end of the blog, we hope you’ll determine if Airflow is the right fit for your data needs.

Data quality is an essential aspect of any data pipeline, and it's critical to ensure that data is accurate, consistent, and free from errors. The data quality must be monitored and maintained throughout the entire data pipeline, from the source to the final destination.

Interestingly, Airflow does not have built-in features for managing data quality, so organizations must use additional tools and techniques to maintain the quality of their data.

Let us discuss some of Airflow's limitations when managing data quality and why it's not the best choice for organizations that need to ensure the accuracy and consistency of their data.

We’ll also discuss alternative tools and techniques organizations can use to manage data quality in their pipelines.

By understanding the limitations of Airflow and the importance of data quality management, organizations can make informed decisions about how they manage their data pipelines and ensure that they use the right tools for the job.

So, let us see why Airflow might not be the best choice for your data quality needs.

Reasons Not to Use Airflow for Data Quality:

1. Lack of Built-in Data Quality Control Features: Airflow was designed to automate and manage data pipelines, but it does not have built-in features for managing data quality. Without these features, it becomes difficult to detect errors and inconsistencies in the data, leading to incorrect insights and decision-making based on inaccurate data.

This would mean that organizations must use additional tools to validate their data and ensure that it's accurate and consistent.

2. Inefficient Data Validation Processes: Data validation is a crucial step in data quality management. This leads to a higher risk of errors and inconsistencies in the data, which can compromise the accuracy and consistency of the data.

The inefficient data validation processes in Airflow can also lead to a longer time to complete data processing tasks, impacting the organization's ability to make timely and informed decisions.

3. High Risk of Data Errors and Inconsistencies: The lack of built-in data quality control features and the inefficient data validation processes in Airflow increases the risk of data errors and inconsistencies.

These errors and inconsistencies can compromise the accuracy and consistency of the data, leading to incorrect insights and decision-making based on inaccurate data.

This can have serious consequences for the organization, including lost revenue, damaged reputation, and decreased customer trust.

4. Data lineage is an important aspect of data quality management as it provides a clear understanding of the origin, transformation, and flow of data throughout a pipeline. With Data lineage, data engineers derive the what, where, and how of the data.

What data is being processed, where it comes from, and how it's transformed?

In Airflow, the lack of built-in lineage information can make it difficult for data engineers to fully understand the impact of any changes to data quality checks or the data pipeline as a whole. This can make it difficult to trace the root cause of data quality issues.

Furthermore, without lineage information, data engineers may struggle to identify dependencies between different data quality checks and the impact of changing those checks. This can lead to errors in the data pipeline and negatively impact data quality.

Now that we understand its incapability in data lineage and the overall data quality, let us see the major challenges of using Airflow in a distributed system.

Challenges in Managing Data Quality in a Distributed System:

Data quality management in distributed systems presents unique challenges that require specialized features. Some of the challenges include the following:

1. Lack of coordination across distributed teams: Airflow does not have the built-in capabilities to manage the coordination of data quality checks across multiple teams.

Imagine a software company with a distributed data team responsible for different data sources, such as user data, product data, and usage data.

Each department has its own data quality checks and procedures in place, but there is no centralized tool or process to coordinate data quality management across the teams.

This results in data quality issues slipping through the cracks and inconsistent data quality across the different data sources.

2. Real-time data quality management: Airflow may not have the ability to keep up with the speed and scale of data processing in these systems, leading to gaps in data quality management.

A SaaS company that processes large amounts of user data in real time would need robust speed and scale for data processing. Airflow is not capable of keeping up with this. As a result, data quality issues are not identified and corrected in a timely manner, leading to gaps in data quality management.

3. Inconsistency managing multiple data sources: Airflow may not have the necessary tools to manage data quality consistency across multiple data sources in a distributed system.

A software company has multiple data sources, including user data from its SaaS platform, product data from its APIs, and usage data from its analytics tools. Each data source has different data quality standards, making it difficult to maintain consistent data quality across all sources.

4. Data quality Issues while handling distributed systems: Airflow may not have the built-in tools to efficiently address data quality issues in a timely manner.

When data quality issues are identified, fixing them can be difficult and time-consuming, especially in a system with multiple teams and data sources.

Airflow does not have the built-in tools to efficiently address data quality issues in a timely manner, making it a challenge to maintain high-quality data in the system.

Airflow is not designed for data quality. Let's see which alternatives can assist you if you are looking for a solution to all these challenges.

Better Alternatives to Ensure Data Quality:

There are several alternative tools and techniques that organizations can use to manage data quality in their data pipelines. These tools are specifically designed for data quality management and provide data validation, profiling, and cleansing features. It's all about choosing the one that suits best your needs. Here we suggest the most popular alternatives:

• Talend provides a user-friendly interface with drag-and-drop functionality, making it easy for data engineers to create and execute data quality checks. It also offers a range of data quality tools to ensure accurate and consistent data and can be integrated with multiple data sources, unlike Airflow.

• Microsoft SQL Server Integration Services (SSIS), with scalability and performance to handle large volumes of data, offers built-in data quality tools for cleaning, standardizing, and validating data. Its capability to seamlessly integrate with top technologies such as Azure, and Power BI makes it the go-to choice for organizations seeking to maximize their data capabilities while minimizing integration challenges.

• Our favorite: dbt-test! It's a testing framework specifically designed for data engineers and data scientists. It's simple to use, with a minimal learning curve, and provides a range of tests to validate data quality and accuracy. Unlike Airflow, dbt-test is designed specifically for data quality management, making it a more efficient and effective solution. With its ability to handle complex data quality scenarios, dbt-test is the preferred choice for many organizations looking to manage data quality in their data pipelines. Our favorite is the combination of Airflow and dbt-test.

Why you may ask.

Airflow and dbt together can enable each member of the data team to focus on their specific areas of expertise and have a clear understanding of who should be involved in any troubleshooting when issues arise.

How does that happen?

Airflow and dbt each serve a specific purpose in the data engineering workflow, with Airflow being responsible for orchestrating jobs related to data extraction, loading, and machine learning processes, while dbt focuses on data transformation using SQL.

When used together, these two tools can complement each other to provide the best results for data teams.

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So, always evaluate the alternatives and make a wise decision in choosing the right tool since it's going to make or break the data quality.

Adapting the Right Tool Resolves Data Quality Issues:

While Airflow has some features that make it useful for data engineering, it's not the best choice for data quality management.

The lack of built-in tools and the difficulty in coordinating and managing data quality across distributed teams make it an inefficient solution for data quality management.

Alternatives provide more comprehensive data quality management capabilities, making them a better choice for data quality management in distributed systems.

Airflow with dbt-test, in particular, stands out for its simplicity and specificity to data quality management, making it an efficient and effective solution for organizations since this combination enables data teams to work collaboratively and deliver reliable data to end-users, ensuring high-quality data in their pipelines.

What steps are you taking to ensure bullet-proof data quality in your distributed systems?

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