#DataQuality News 📰
In a moment when the tech world is buzzing about massive investments in OpenAI and the future of AI-driven analytics, it’s more important than ever to ensure that data quality is rock-solid. After all, sophisticated AI models are only as reliable as the data they’re trained on.

Introducing DQX by Databricks Labs
If you’re working with massive datasets on Spark and Databricks, you know how crucial data quality checks are for building trust in your analytics and AI pipelines. The newly released DQX (Data Quality eX) from Databricks Labs is the latest open-source framework designed to simplify and automate data quality in Spark environments—especially for Databricks and Delta Lake users.
Key Highlights of DQX
• Databricks-Centric: Built to integrate closely with Delta Lake, Unity Catalog, and potentially Delta Live Tables, giving you a more seamless data-quality workflow on the Databricks platform.
• Scalable Checks: Leverages Spark for distributed validations, meaning it can handle large-scale datasets without slowing down your pipelines.
• Modular Design: Early documentation shows it supports easy creation of custom or out-of-the-box checks, letting you tailor validations to your data.
• Lab Status: Since it’s a Databricks Labs project, expect frequent updates and possible changes in APIs or features as it matures.

But Wait—There Are Other Options 🤚
When it comes to data quality on Spark, DQX isn’t your only choice:
Amazon Deequ ☝️
• Maturity: A widely adopted, production-ready Spark library from AWS.
• Key Feature: Boasts automated “Constraint Suggestion,” which inspects your data and suggests potential quality rules.
• Ideal For: AWS-centric teams or anyone seeking a well-documented, proven Spark-based solution.
Spark-Expectations (Nike) ❇️
• Approach: Uses a decorator pattern to validate data “in-flight” (as the Spark job runs) and “at-rest” (on existing tables).
• Key Features:
• Performs both row-level and aggregated checks in a single framework.
• Automatically quarantines records that fail one or more rules into an _error table, complete with rich metadata (failed rule details, job info, etc.).
• Provides aggregated metrics in a _stats table to avoid recalculation.
• Offers flexible action_if_failed options for each rule (e.g., fail the job, drop the row, or simply log the error).
• Ideal For: Teams needing a PySpark-based framework that does real-time or batch validations and applies immediate actions (like quarantining) based on rule failures. It’s especially useful for those who want to prevent malformed data from ever reaching downstream consumers.
While DQX could become the go-to solution for Databricks users, Amazon Deequ and Spark-Expectations remain strong alternatives—especially if you need a more mature ecosystem or a broader Spark distribution outside Databricks. As always, choose the tool that best aligns with your platform, team expertise, and specific data-quality requirements.
P.S. I mentioned only spark focused tools. For sure Soda and GX has integration with spark as well