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Waterline Data Listed as “Representative Vendor” in Gartner Market Guide Report

Mountain View, CA – April 14, 2015 — Waterline Data, the automated data discovery for Hadoop company, today announced that it was mentioned as a representative vendor in Gartner’s Market Guide for Self-Service Data Preparation for Analytics(1).

The market guide “profiles products that can accelerate the shift toward business-user-oriented, visual, interactive data preparation. It helps business intelligence, analytics and information management leaders to understand the data preparation landscape and vendor-specific functionality.”

“We believe our inclusion as a representative vendor in the Gartner Market Guide validates the importance of automated metadata discovery as a foundational step in data preparation,” said Alex Gorelik, founder and CEO of Waterline Data. “As companies are moving beyond a few data science and analytics pilots in Hadoop, they are recognizing the need for multi-project foundational capabilities such as data profiling, data quality, metadata, and a business glossary. We’re focused on helping companies deploy these foundational capabilities as an integrated and automated data inventory solution that lets data engineers and data scientists find, understand, and govern data without having to explore each file one at a time in a large data lake.”

(1) Gartner, Inc. “Market Guide for Self-Service Data Preparation for Analytics” by Lakshmi Randall, Rita L. Sallam, Bill Hostmann, Ehtisham Zaidi, March 5, 2015.


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About Waterline Data

Waterline Data was founded in 2013, and is backed by Menlo Ventures and Sigma West.

Companies that want to open up Hadoop across business units for strategic value creation are creating a data lake to drive business transformation. It’s easy to get data into Hadoop, but it’s not easy to get the right data out of Hadoop to extract business value while staying in compliance. To do so, data architects must be able to quickly catalog data assets, business users must be able to quickly find and understand the data they need, and data governance policies must be adhered to throughout the process.

The inspiration for the name “Waterline” came from the metaphor of the Big Data lake. The idea behind “Waterline” is that data engineers, data scientists, and business analysts should be able to find the best suited and most trusted data without extensive coding and manual exploration – in other words they should be able to “Hadoop above the waterline.” Waterline Data automates the inventory of data assets in the lake, enables business users to find and understand the data through self-service, and enables data governance.

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