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Looker Accelerates and Simplifies Data Science Workflows

By   /  May 16, 2018  /  No Comments

A new press release reports, “Looker, a leading data platform company, today announced tools and integrations that optimize data science workflows. Looker accelerates the data science stack by removing the struggle to prepare data and freeing up time for data scientists to use their unique skill set to perform higher-value tasks. Looker already delivers reliable, governed data, at scale, for data scientists to input into their models and then present the insights in understandable and actionable dashboards and reports. Now, Looker has further improved this workflow with an SDK for R and connections for Python, as well as streamed and merged results, Google TensorFlow integrations, and clean, visual recommendations for users. Looker also continues to partner with best-of-breed data science vendors to provide more seamless workflow integration.”

The release adds, “In addition to Looker’s ability to efficiently prepare data for data science modeling and operationalize insights across an organization, new capabilities include: Merge results – Combine data from multiple sources into a single analytic view; Stream results – Query and stream even massive data sets for use in data science modeling; Statistical functions – Perform advanced statistics directly in Looker; Suggested analytics – Looker provides suggested analytics and dashboards right from the user’s home page; R SDK – Easily leverage data from Looker while working with R and RStudio; Python connections – Easily leverage data from Looker while working with Python and Jupyter Notebooks; Machine Learning/Artificial Intelligence Partners – Integrate with best-of-breed technology partners to make the Data Science workflow more efficient, including Big Squid and TensorFlow from Google.”

Read more at Business Wire.

Photo credit: Looker

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