Data Quality


Brokering Data: Accelerating Data Evaluation with Databricks White Label

As the data-as-a-service ecosystem evolves, data brokers are faced with an unprecedented challenge – demonstrating the value of their data. Successfully crafting and selling a compelling data product relies on a broker’s ability to differentiate their product from the rest of the market. In smaller or static datasets, measures like row count and cardinality can […]

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Big Data Data

Democratizing Data Quality Through a Centralized Platform

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how does one ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Here’s an […]

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Improving IoT sensors data quality with Verified Telemetry

Customers are increasingly using sensors to collect data and use that to make critical business decisions. The quality of data drives the quality of decision and outcome. Verified Telemetry is a new capability offered as part of the Azure Edge offerings enabling customers to retro-fit the ability of measuring sensors condition, continuously, alongside the data, […]

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Superconductive Helps You Always Know What to Expect from Your Data

Join Adi Polak for an interesting conversation with James Campbell and Abe Gong of Superconductive  .  James and Abe share the story of how their startups started as an open source solution for Data Quality challenges. Why Data Quality issues are on the raise now. And how they still work closely with the community to develop […]

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Big Data Databricks Spark

Data Quality Testing in the Medallion Architecture with PyTest and PySpark

Here’s a great Lightning talk from Data + AI Summit 2020 by Carter Kilgour on ”Why data quality is especially important in the medallion architecture, and how to ensure it with scheduled testing and reporting.”

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