Building Trust in Data II: A Guide to Effective Data Testing Tactics

[Razi_Bayati]

Introduction

Building a reliable data pipeline goes beyond setting up a functional workflow — it requires meticulous testing to ensure data accuracy, integrity, and quality across every stage of the process. In this second part of our series on data testing (first part here), we’ll focus on the specific challenges and strategies involved in testing a data ingestion layer. Using practical examples, we’ll dive into testing tactics that address common risks, such as schema changes, data inconsistencies, and transformation errors. By implementing these targeted testing techniques, data teams can create a more robust and resilient data pipeline, setting a solid foundation for data-driven decision-making and enabling seamless data delivery to end users.

Scenario

Imagine you’re part of a data engineering team responsible for delivering high-quality data to data consumers, such as data analysts. Your primary goal is to maintain the accuracy, integrity, and quality of data transformations within your pipeline — a task complicated by high data volumes, intricate transformation logic, diverse data types, and evolving schema and business logic requirements.

To ensure a smooth and reliable data migration, a comprehensive testing strategy is essential. This strategy should encompass multiple test types to support a test-driven approach, helping to identify and address potential defects early on and preventing missing capabilities. The following table outlines key testing types that can facilitate a seamless data migration experience, covering every critical aspect of the pipeline.

reference: https://www.databricks.com/glossary/medallion-architecture

Testing strategy

Building a robust data ingestion layer requires a strategic approach to testing, rooted in well-defined objectives, scope, and risk assessments, as outlined in the testing framework from the first article. Here’s a breakdown of key considerations to guide an effective testing strategy:

1. Define High-Level Objectives
   At a high level, the main objectives are to ensure data accuracy, integrity, and quality throughout the transformation process in the data pipeline. These objectives form the foundation for all subsequent testing efforts, focusing on preventing errors and ensuring data readiness for consumption.
2. Scope of Testing
   For this scenario, the scope includes the entire data pipeline, from raw ingestion (Bronze layer) to transformation (Silver layer) and final delivery (Gold layer). Since the primary end users are data analysts relying on this data for reporting, the Gold layer must be in a format that’s clean, consistent, and ready for analysis. Testing should target each granular component within this architecture, verifying that each layer operates correctly and that data is transformed and delivered as intended.
3. Identify Potential Risks and Associated Costs
   Various issues can arise within the pipeline, each with its own potential costs:

• Transformation Errors: Incorrect transformations could lead to inaccurate data insights.
• Schema Changes: Unexpected schema alterations in the source data could disrupt the pipeline, causing delays or data corruption.
• Data Quality Issues: This includes missing or malformed data, unexpected outliers, incorrect data formats, and changes in measurement units or data distribution.
• Performance Expectations: Ensuring that the final data in the Gold layer meets performance standards and is in a usable format for analysts.

Addressing these risks early through targeted testing helps minimize potential disruptions and allows for faster, more reliable data processing.

4. Define the Finish Line
Success for this testing strategy is defined by delivering reliable, refreshed data to end users at agreed intervals (e.g., every X minutes). Clear data contracts between the data engineering team and data analysts specify expected behavior, transformation rules, and alert protocols for any significant changes. This ensures all stakeholders have a mutual understanding of data quality requirements and can respond promptly if adjustments are needed.

Given the complexity and variability in data pipelines, an incremental approach to testing — starting with fundamental checks and expanding coverage over time — allows for continuous improvement in data quality. With the objectives, scope, risks, and finish line established, the team can prioritize and refine the tests needed to safeguard the pipeline effectively.

Test plan

The test plan translates the strategy into actionable tactics, considering dependencies, timelines, and prioritizing key tasks to achieve maximum impact efficiently. Below is an example test plan, starting with unit tests and expanding to include integration and end-to-end testing.

Unit test

Each unit test targets a specific part of the data pipeline, supporting modular and incremental quality improvements. Databricks provides capabilities to streamline these tests, making it easier to validate data integrity at each layer.

Example of unit tests for data ingestion

Databricks offers a range of capabilities to streamline and strengthen the unit testing process. Delta Live Tables (DLT) allows users to define quality expectations directly within tables, making it easier to verify and track data quality throughout the ingestion and transformation stages. These expectations can flag issues such as schema incompatibility or unexpected changes in data distribution, enabling early error detection. Later in the pipeline, you can use Databricks SQL (DBSQL) to build dashboards that monitor data quality and trigger alerts if data inconsistencies arise, enhancing visibility into pipeline health. For a practical guide, check out the Databricks demo, Unit Testing Delta Live Table for Production-Grade Pipelines, which illustrates how DLT can support both unit and integration testing for robust, adaptable pipelines.

Lakehouse Monitoring adds another layer of quality assurance by allowing teams to profile, diagnose, and enforce data quality directly within the Databricks platform. This proactive tool detects issues before they impact downstream processes, helping to maintain data integrity. For an in-depth example, the Lakehouse Monitoring tutorial demonstrates how to monitor data in Unity Catalog, with insights into data volume, integrity, and distribution changes. The tutorial walks through setting up a monitor for retail transaction data and best practices for tracking data trends and anomalies, generating an automated dashboard that flags quality issues such as changes in numerical and categorical distributions.

Additional Databricks features, like Auto Loader for streamlined ingestion and schema enforcement for maintaining data accuracy, further enhance data reliability. Delta Lake’s constraint management and ACID compliance add consistency and reliability to data handling, while Databricks SQL simplifies the creation and validation of complex calculations. Together, these capabilities support both unit and integration testing, contributing to a resilient, end-to-end data pipeline that adapts to real-world complexities.

Integration Testing and End-to-End Testing (E2E)

Integration testing ensures smooth data flow between layers and verifies that transformations are correctly applied throughout the pipeline. End-to-End (E2E) testing, on the other hand, validates the entire workflow, ensuring it meets business requirements and user expectations. Databricks provides powerful tools to support both types of testing:

• Migration Validation: Confirms that data migrated from legacy systems to Databricks is accurate, complete, and consistent. Delta Lake enables row count comparisons and transformation validations for effective migration checks.
• Code Conversion Validation: Ensures that SQL queries and transformation logic perform as expected after migration, preserving data quality and functionality.
• Pipeline Validation: Verifies that each stage of the pipeline, from extraction to loading, operates seamlessly. Databricks Jobs and Delta Live Tables automate and monitor pipeline tasks, including error handling and recovery, to enhance pipeline resilience.
• User Acceptance Testing (UAT): Engages end users, such as data analysts, to verify that the final data output meets business needs. Databricks Notebooks allow for hands-on data validation within the Gold layer, and profiling data regularly can help identify new data quality tests as understanding deepens.
• Governance and Observability: Implements centralized monitoring and alerting through Unity Catalog, which manages data lineage and access governance. Row-level and column-level filters allow for precise data access control, ensuring that sensitive information is protected and that user interactions are recorded.

These testing types together create a comprehensive framework, enhancing data quality, reliability, and usability across the pipeline. This approach ensures data consumers receive accurate, trustworthy insights. Keep in mind that while column-level validations in production offer robust data checks, they may impact performance. It’s best to monitor performance, start with simpler tests, and expand gradually as pipeline stability is confirmed.

Ownership

Assigning testing responsibilities can vary significantly across teams and organizations, and that’s perfectly acceptable. The key is to establish a clear RACI (Responsible, Accountable, Consulted, Informed) matrix to define ownership at each stage of the testing process. Here is a commonly used approach to testing ownership:

• Data Engineers: Typically, data engineers are responsible for designing and executing unit tests. Their proximity to data ingestion, transformations, schema management, calculations, and consistency checks makes them well-suited for handling these foundational tests.
• Testing Team: If a dedicated testing team or data quality specialists are available, they can add value by supporting test automation, setting up testing frameworks, and performing validations for schema changes and data consistency. This team helps ensure that testing is efficient and scalable.
• Data Analysts: Data analysts play a critical role in verifying transformation outputs and key calculations within the Gold layer. They assess data accuracy from a business logic and reporting perspective, ensuring that the final data meets end-user requirements.

This distribution of responsibilities enables each team member to leverage their expertise, contributing to a larger testing framework that includes integration and end-to-end (E2E) testing. Together, this collaborative approach ensures data quality incrementally — from ingestion through to final delivery — ultimately creating a reliable data pipeline for all users.

Other references

For those looking to enhance their data testing toolkit, the following resources offer specialized tools and insights for testing within Databricks and broader data environments:

• PyTest Fixtures for Databricks: PyTest Fixtures provide a structured way to manage test setup and teardown in Python. Developed as part of the Unity Catalog Automated Migrations project, these Databricks-specific fixtures simplify integration testing for Databricks environments. GitHub Repository
• PyLint Plugin for Databricks: PyLint performs thorough checks on code quality, from line length to module usage and variable naming. This plugin extends PyLint with Databricks and Spark-specific checks, making it a valuable tool for maintaining code quality in Databricks projects. GitHub Repository
• Chispa for DataFrame Equality: Chispa provides efficient DataFrame equality checks, enhancing accuracy and validation capabilities for testing data transformations and ensuring consistency in results. GitHub Repository
• Testing ETL Pipelines with PyTest: This resource covers using PyTest to test logic correctness and quality in ETL pipelines, offering guidance on best practices for comprehensive data validation. ETL Testing Guide
• Data Quality with Apache Spark: This high-level guide discusses strategies for maintaining data quality in Apache Spark environments, focusing on practical approaches for real-world applications. Article
• Building End-to-End Testing Pipelines: This tutorial demonstrates how to create an end-to-end testing pipeline with DBT on Databricks, covering tools, frameworks, and setup. Medium Article
• Introduction to ETL Pipeline Testing: A customer story video guide that covers best practices for efficiently testing ETL pipelines, from setup to execution. YouTube Video

These resources collectively offer powerful tools, frameworks, and insights to assure data quality, pipeline robustness, and code reliability within Databricks and beyond.