Looker Debuts New Tools for Optimizing Data Science Workflows


The tools and integrations are designed to provide significant upgrades in the speed of data science projects.

Data platform company Looker has announced the rollout of new tools and integrations designed to improve data science workflows.

Looker’s offerings help data scientists spend more time on high-value tasks by removing the hassles of data prep and freeing up time that otherwise would have been spent on it.

The company’s platform delivers governed data at scale and easy to understand insights in a user-friendly dashboard. The improvements include integration with Google TensorFlow, Python connections, an SDK for T and streamed results. They also offer partnerships with highly regarded data science vendors.

See also: Cloud + streaming analytics + data science = 5 new big data trends now

“Cleaning and preparing data is not the most valuable use of time for data scientists,” said Frank Bien, CEO of Looker. “Looker eliminates that time-consuming work, making data science workflows vastly more efficient. With Looker, data scientists can spend more time on high-value model building, creating greater business impact, and moving on to the next problem faster.”

“We use Looker as a single source of truth for clean data about our clients, and rely on it when building predictive models or collaborating on metrics with other teams internally,” added Julia Silge, data scientist at Stack Overflow. “It’s a massive time-saver for us, reducing steps that used to take hours to only a few minutes.”

According to the company’s announcement, key features 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 homepage
  • 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

For more info, join Looker’s webinar hosted by Data Science Central to learn how innovative companies are modernizing their tech stack and workflows for data scientists.

Sue Walsh

About Sue Walsh

Sue Walsh is News Writer for RTInsights, and a freelance writer and social media manager living in New York City. Her specialties include tech, security and e-commerce. You can follow her on Twitter at @girlfridaygeek.

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