Ensure Trustworthy Data with Alation’s Data Quality Processor: Webinar Highlights

Imagine making a critical business decision based on flawed data—launching a product, entering a new market, or approving a million-dollar budget—only to discover that the data informing that decision was riddled with errors. The impact could be disastrous. In fact, poor data quality is more common than you might think, costing U.S. businesses an astounding $3.1 trillion annually, according to a study by IBM.

For these reasons, high-quality data isn’t just a "nice-to-have"; it’s a powerful advantage. Accurate, reliable data fuels better decisions, drives operational efficiency, and ensures compliance with ever-evolving regulations. In an era where AI and analytics power a competitive edge, data quality can be the deciding factor between success and costly mistakes.

Alation's Data Quality Processor (DQP) is a tool that delivers this advantage. The DQP allows businesses to import data quality information from other sources (including Snowflake and Databricks) and apply rules to determine if their data is high quality. 

In a recent webinar, Gene Arnold, Product Architect at Alation, highlighted how the DQP supports trustworthy data and simplifies data management, particularly with Snowflake and Databricks. Below, we’ll explore some key takeaways from the session.

What is the use case for the DQP?

While many data platforms, such as Snowflake, AWS Glue, and Databricks, already provide data profiling functionalities, organizations still face challenges when it comes to unifying the results from these diverse tools. This is where Alation’s DQP steps in.

The DQP is not a standalone data quality tool. Instead, it works seamlessly with existing data quality solutions, integrating results from multiple platforms into Alation’s data catalog. It simplifies the process for users who want to ensure the quality of their data without learning the ins and outs of each tool. 

“The business users are really interested in just the quality of their data, not the tool that brings it in,” Arnold elaborates. “And what's great about Alation’s data health tab is that it's going to present the results of whatever tool you're using.”

The DQP delivers an integration that, in essence, brings together all the data quality information from different sources into one convenient place, empowering analysts and business users to quickly assess the health of their data at a glance.

How does the DQP help customers?

The beauty of the DQP lies in its simplicity. Business users need trustworthy data to make informed decisions, but they shouldn’t have to worry about which data quality tool was used or how the results were generated. Alation’s DQP offers a “no-code” solution that allows data administrators and SMEs to apply custom rules to the data quality metrics collected from other tools, transforming raw results into meaningful insights.

For example, if an organization is working with multiple back-end systems like Snowflake, Databricks, or Oracle, users can apply specific thresholds—like identifying the number of incorrect email formats or tracking unusual trends in data quality—directly within Alation. 

These results are then displayed in the unified data health tab, giving users an easy-to-understand snapshot of their data quality. This level of transparency allows businesses to act on their data with confidence, or steer clear of data that has been flagged as erroneous. 

How does the Alation DQP work with Snowflake, Databricks, and AWS?

Alation’s DQP is particularly beneficial for organizations using Snowflake. Snowflake’s built-in data profiling tools provide key insights, but the challenge lies in making these insights actionable within a broader data governance framework. The DQP extracts Snowflake’s data quality metrics and allows users to set rules and thresholds that are meaningful for their specific use cases. (Alation also offers a DQP for Databricks, and integrates with AWS Glue).

“We've got tools like Snowflake, Amazon Glue, Databricks,” Arnold shares. “Now, right in the middle here is DQP, the data quality processor. And what DQP does is it takes the profiling information… and gives you the ability to apply rules to those results and pushes the result into Alation in the data health tab... You're simply just creating rules and you're using the data profiling from some of the best out there in the market.”

“We have a number of organizations that are maybe not quite ready to dive into the expense of a full-blown data quality product,” Arnold continues. “There's a number of great ones out there, and obviously Alation integrates with them and they do come at a cost and they're worth that cost. But often organizations just aren't quite ready yet. [Reps often ask], ‘You know, we're doing [data quality profiling] now, we just have to turn it on… But how do we get that into Alation? The DQP data quality processor.”

By integrating these insights into Alation’s catalog, the DQP enables customers who use Snowflake, Databricks, and AWS Glue to evaluate the quality of their data without needing to switch between tools. This seamless integration ensures that users see a consistent, holistic view of their data quality, allowing them to make informed decisions and maintain high data standards across their organization.

Conclusion

Alation’s DQP is a game-changer for organizations looking to unify and simplify their data quality efforts. By integrating with existing data quality tools and providing a centralized hub for viewing data quality metrics, the DQP ensures that organizations can easily assess and act on the quality of their data. For Snowflake, Databricks, and AWS Glue users, this tool offers a powerful way to ensure trustworthy data, bringing together the best of Snowflake’s profiling capabilities and Alation’s governance framework.

Curious to learn how you can leverage the DQP with Alation? Book a demo with us today.