data engineering apis

Interzoid Launches AI-Powered Data Quality on the Snowflake Native Application Platform

by Interzoid Team

Normalizing data with AI models

Data Quality and Matching functions are now available via SQL statements on the Snowflake Data Cloud.

Snowflake's data warehousing platform has revolutionized the industry with its cloud-native architecture, scalability and performance, separation of compute and storage for cost optimization, rigorous security standards, and adherence to well-established industry protocols like SQL. Its support of structured data enables seamless data sharing, multi-faceted collaboration, and has fostered an emerging ecosystem of varied integrations that complement its Data Marketplace and extensive data offerings. Snowflake empowers organizations to modernize their data warehousing and analytics capabilities, driving efficiency and innovation across multiple industries.

Earlier this year, Snowflake's Native Application Framework went to general availability, enabling applications to be built directly within Snowflake's Data Cloud platform. This offers Snowflake users straightforward access to these native applications, allowing for seamless integration with data stored within Snowflake and with optimized performance. This approach simplifies deployment and utilization for Snowflake customers these extended capabilities while enabling secure data sharing and usage.

Interzoid has fully embraced the Snowflake platform and its Native Application Framework. For those who recognize the importance of high-quality, consistent, usable data in maximizing the ROI of Snowflake's platform and all of the data stored within it, we have launched our first two application deployments onto the platform. Our Company and Organization Name Matching and Individual Name Matching APIs have been fully integrated into the Snowflake Native Application Framework and entirely accessible using SQL.

You can now access all of our matching capabilities via Snowflake SQL statements directly within the Snowflake platform (such as a Snowflake worksheet, for example). This enables comprehensive matching data reports of any Snowflake table/view to be instantly generated, showing where data content is inconsistent, redundant, and likely problematic. The SQL invocation approach enables the utilization of additional available columns as part of custom match criteria with and beyond our AI-enriched, generated similarity keys. You can also perform "fuzzy" joins for higher match rates between tables for the enrichment of data, create observability-oriented stored procedures for ongoing data quality reporting, analyze external data tables and views for data quality using Snowflake, and more. The possibilities are essentially infinite, and all as easy to leverage as writing a SQL statement directly within the Snowflake platform.

To see how easy it is to make these capabilities available within your Snowflake account, visit here:

Snowflake Organization and Company Name Matching:
https://www.interzoid.com/snowflake-org-matching

Snowflake Individual Name Matching:
https://www.interzoid.com/snowflake-individual-matching

The corresponding links to the Snowflake Marketplace are in these pages.

And of course, we continue to enhance and innovate with our behind-the-scenes AI models, making the data quality and matching capabilities as powerful as ever.

Examples of Inconsistent Data


See our Snowflake Native Application. Achieve Data Quality built-in to SQL statements.
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Connect Directly to Cloud SQL Databases and Perform Data Quality Analysis
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Launch Our Entire Data Quality Matching System on an AWS EC2 Instance
Deploy to the instance type of your choice in any AWS data center globally. Start analyzing data and identifying matches across many databases and file types in minutes.
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