How do you use Apache Airflow for data quality?

Apache Airflow, or simply Airflow, is an open-source orchestration platform that empowers engineers to build patterns that fit their needs. Airflow is an excellent tool for replacing expensive tools such as SSIS and Informatica and even new ones like Azure ADF. The web UI for monitoring DAGs is well-suited for use in support rotations. A strength of Airflow is its ability to integrate it tightly into specific observability platforms and practices, both niche and well-known, is a strength. A weakness of Airflow is that the GUI-driven development of the aforementioned ETL tools is not supported. To get the most out of Airflow, a moderate ability in Python is required, but it stands tall amongst its peers, and I would recommend it.

One thing I've found helpful is using Airflow to standardize data quality checks across different teams. This avoids duplicated efforts and encourages reusability. I disagree with relying solely on Airflow for data quality. It's still important to validate data quality at the source systems and have decent data governance overall. Airflow complements these efforts.

Airflow is Python, so any library, whether your own custom one or a FOSS one like Soda Core, can be used to extend the platform. Airflow's extensibility is why I recommend Airflow, as it empowers engineers to build patterns that fit their needs. That said, regarding Airflow's ability to support data quality, both the engineering of the tooling and its regular measurement is something that if you can code it, Airflow can do it, and I personally like the sode-core data quality stack to handle my data quality needs across SQL, pandas, and Spark. So, I believe Apache is great for what is designed to do and that is batch data movement and orchestration when paired with a solid data quality management framework.

An example I've seen work well is having a base DAG template for standard data quality tasks like schema and SLA validation. Teams can extend this base template for their specific needs. Sub-DAGs are great but try to avoid going too nested. It can make troubleshooting failures tricky if DAGs are too complex. Keep things modular but resist over-engineering.

As mentioned, Airflow leverages a concept called operators that simplify integrations into other platforms like K8s, AWS, Azure, GCP, etc. However, being open-source means Airflow operators do not come at an additional cost as is customary with paid platforms. That said, Airflow is just another tool to support data quality where the hard work that comes with defining and maintaining a high degree of data quality comes with aligning a data team towards a common philosophical goal focusing on data governance that drives and improves customer value. In conclusion, Airflow supports data quality, but you need more than Airflow to do data quality the right way in order for it to be sustainable.

I encourage teams to leverage custom or community-built operators when possible vs building everything from scratch. This saves time and benefits from shared best practices. That said, overusing 3rd party operators as black boxes can lead to lack of ownership and understanding. Require documentation on any custom or external operators used.

Make sure to set up alerting integrated with monitoring tools like Slack, PagerDuty or email. Don't just rely on the Airflow UI. Alert fatigued teams may miss issues. Perform regular DAG health checks and audits to identify opportunities to optimize performance, reliability, costs. Airflow usage often decays over time without oversight. Institutional knowledge transfer is critical when troubleshooting. Require documentation and knowledge sharing across teams to mitigate key person dependencies.

The key idea here is the Airflow UI is insufficient for the monitoring needs of your data pipeline, especially for data quality. Therefore, all Airflow instances need to configure alerting integration with third-party tools like MS Teams, OpsGenie, Jira, or email. You have to walk a fine line to avoid alert fatigue within your teams but also maintain high observability, so trigger alerts based on SLA requirements and failures when they occur. As for data quality using a package like Great Expectations to establish measures and controls to perform regular data health checks, the same is true for pytest for regular DAG health checks and audits to identify opportunities to optimize performance, reliability, and costs.

Implement monitoring, alerting & on-call for rapid issue detection/response. Use canary deployments & feature flags to test changes gradually. Perform chaos testing to uncover weaknesses. Handle failures gracefully. Log metrics like task runtimes, DAG durations, SLAs to quantify performance. Document runbooks for common issues. Maintain incident knowledge base. Fail fast if possible, retry for transient errors. Decouple DAG dependencies. Profile tasks over time to optimize bottlenecks. Cache intermediary data. Customer-facing DAGs demand 24/7 robustness. Analytical DAGs focus on efficiency. But both require solid observability, testing and documentation for easy troubleshooting

Airflow is not a panacea for your data pipeline and orchestration needs, especially when your data movements need streaming; Apache Flink fits that need. That said, Airflow allows you to engineer a robust solution for your data pipeline orchestration needs and allows you to do batch data movement with some extension. Therefore, Airflow will enable you to roll your data platform for no licensing costs when your team has the skillset to build and support it but do not discount the SaaS Airflow solutions like Astronomer and Amazon Managed Workflows for Apache Airflow to strike a balance between upfront costs and recurring costs that fits your team and budget.