In the fast-moving world of data engineering, keeping data pipelines trustworthy and reliable is more critical than ever. As organizations increasingly rely on data-driven decisions, ensuring the consistency and quality of data in ETL (Extract, Transform, Load) pipelines becomes a top priority. One subtle but impactful challenge in maintaining data quality is data drift—a change in the distribution or structure of incoming data over time. If left undetected, data drift can cause downstream models and dashboards to fail silently, resulting in poor business decisions.
To address this, engineers are turning to tools like Great Expectations and Apache Airflow to detect, report, and respond to data drift in automated and scalable ways.
What is Data Drift and Why It Matters –
Data drift occurs when the statistical properties of input data change in unexpected ways. This could involve changes in data schema (e.g., new columns, removed fields), statistical distributions (e.g., a spike in null values or shifts in average salary), or business logic violations (e.g., negative quantities in sales data).
For example, consider a retail company that collects daily sales records. If suddenly the “region” field starts showing unexpected codes or the “price” field contains unusually high values, these are signs of data drift. Left unchecked, such anomalies can degrade machine learning models or skew business reports.
Introducing Great Expectations for Data Validation –
Great Expectations (GE) is an open-source data quality framework that allows teams to define and validate expectations about their data. Expectations are essentially testable assertions—like “column X should never be null” or “values in column Y should fall within a certain range.”
GE supports profiling datasets to auto-generate expectations, executing validations, and rendering human-readable documentation of test results. When embedded in an ETL workflow, it serves as a powerful tool to detect schema changes, distribution shifts, and logical inconsistencies, all of which are symptoms of data drift.
Apache Airflow: Orchestrating ETL Workflows –
Apache Airflow is a widely adopted workflow orchestrator designed for managing and scheduling complex data pipelines. With its DAG-based (Directed Acyclic Graph) architecture, Airflow allows data teams to define tasks and their dependencies programmatically.
Airflow is ideal for integrating data validation steps into existing pipelines. It can schedule and run GE validations as tasks, halt downstream processing when expectations fail, send alerts, and log results for auditing and compliance.
Combining Great Expectations and Airflow for Drift Detection –
By integrating Great Expectations into Airflow-managed ETL pipelines, teams can build automated checks that catch data drift early in the process. Here’s how a typical setup might work:
- Define Expectations with GE: Create a suite of expectations based on historical or profiled data. These may include expectations for column types, null percentages, unique values, and statistical ranges.
- Embed Validation Tasks in Airflow DAGs: Add GE checkpoints as separate tasks in your DAG before loading data into the warehouse or consuming it downstream.
- Configure Alerts and Logging: When an expectation fails (e.g., sudden spike in null values or new category in a column), Airflow can trigger email or Slack alerts, log the failure, and optionally stop the pipeline.
- Monitor Trends Over Time: GE’s Data Docs and validation store allow you to track expectation results over time, helping you identify slow, creeping data drift rather than abrupt failures.
Sample Use Case: Detecting Schema Drift in a Sales Pipeline –
Imagine a pipeline that ingests daily sales data from multiple regions. Over time, a new region gets added without notifying the data engineering team. The “region” field starts showing new values that weren’t present before. Using GE, you can set an expectation that the “region” column must match a predefined set of categories.
When the new value appears, GE will flag the change during validation, and Airflow will halt the pipeline and notify stakeholders. This gives teams time to investigate, update transformations if needed, and maintain the integrity of downstream systems.
Best Practices for Implementation –
- Start with Profiling: Use GE’s
expectation suiteauto-profiler to create baseline expectations from trusted datasets.
- Validate Early and Often: Add validations as early as possible in the ETL pipeline to catch bad data before it contaminates other systems.
- Version and Review Expectations: Store expectations in version control and review them as part of your code review process.
- Automate Reporting: Use GE Data Docs for validation reports and integrate them with Airflow logs or dashboards.
- Add Sensitivity Thresholds: Some drift is natural—tune GE to trigger alerts only when changes exceed acceptable thresholds.
Conclusion –
Detecting data drift in ETL pipelines is not just about maintaining data quality—it’s about ensuring that your data products, analytics, and models stay trustworthy and relevant. By combining Great Expectations and Apache Airflow, organizations can automate drift detection and establish a first line of defense against unexpected data changes. This integration empowers data teams to respond quickly, maintain confidence in their pipelines, and uphold the integrity of their data-driven decisions.
As data pipelines continue to scale and diversify, automated validation and drift detection will become indispensable tools in the modern data engineering toolkit.