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Datameer Brings Native Data Preparation to Four Biggest Cloud Warehouses

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Datameer, the world’s leading data preparation and analytics solution, announced it is the first platform to natively support the four biggest SQL cloud data warehouses: Amazon Redshift, Azure SQL Data Warehouse, Google BigQuery, and Snowflake. Companies can now use Datameer to explore, analyze, and operationalize all of their data that lives in the cloud, in addition to on-premises big data infrastructure.

Organizations in all industries have a data problem. Generating and collecting data is easy. But making sense of it all is hard. In fact, as much as 73% of a company’s data is never analyzed. In large part, this is because much of that data is unstructured, which presents a problem for more than 95% of businesses.

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Datameer has emerged as the data engineer’s preferred solution to this challenge. With Datameer, customers are able to manipulate all kinds of unstructured data including streaming data, digital media, and JSON files and transform it into secure data sets allowing for further analysis at any scale. Data that lives on-premises or in popular cloud data warehouses is managed just as easy as spreadsheets without any coding required. This enables customers to quickly merge and clean disparate data sets before feeding them into popular business intelligence tools like Tableau and Looker or leveraging them to train AI models.

“We’re thrilled to stay on the cutting edge of data prep, bringing our powerful and intuitive platform to the cloud data warehouses our customers use every day,” says Christian Rodatus, Datameer CEO. “With Datameer, preparation and analysis is all performed on our platform you don’t need two different tools.”

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Customers in all industries, including fintech, use Datameer to increase agility, accelerate innovation, and streamline collaboration.

Pairity, for example, is an AI-driven fintech platform designed to optimize accounts receivables. The company uses AI insights to help debt collectors optimize the collections process and improve collection rates. To fulfill this mission, Pairity needs to prepare and integrate diverse datasets before plugging them into machine learning models. The company needed a way to do this quickly and efficiently, and they found Datameer.

“Thanks to Datameer, we were able to wrap up a project we thought would take two-and-a-half months in two short weeks,” explains Cam Byrd, Pairity CIO. “We’ve been able to create an agile modeling cycle that lets us rapidly prepare and integrate datasets at scale without any coding.”

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