Looker Joins Google Cloud BigQuery for Self-Serve Analytics

Combination removes machine learning bottlenecks and enables line-of-business users to deploy self-serve predictive metrics.

LookerGoogle

The third annual staging of the Google Next conference this week at the rebuilding Moscone Center in San Francisco was an event loaded with integration news more than anything else.

Data platform provider Looker was one of those newsmakers, announcing an integration July 25 with Google Cloud BigQuery machine learning that automates data science workflows and allows business users—not only data scientists—to find important insights with interactive predictive metrics.

Looker’s full-service data platform offers data analytics and business insights to any enterprise department and easily integrates into applications to deliver data directly into the decision-making process.

Using both Looker and BQML, data teams can now save time and eliminate unnecessary processes by creating machine-learning models directly in Google BigQuery via Looker–without the need to transfer data into additional ML tools. BQML’s predictive functionality also will be integrated into new or existing Looker Blocks,  allowing users to surface predictive measures in dashboards and other applications.

Much of the work in machine learning centers around data preparation and ML model evaluation and tuning.  Looker and BQML work together well in that Looker handles the data preparation and BQML does the learning, Lloyd Tabb, Looker Co-founder, Chairman and CTO said in a media advisory.

Looker also helps users evaluate and tune ML models to integrate predictions into dashboards and data workflows, Tabb said.

Machine learning is not only difficult when using large data sets, but it has generally been the domain of data scientists only. BQML puts ML modeling in the hands of line-of-business users and data analysts, all on top of massive data sets in Google BigQuery.

“Looker and BigQuery have allowed us to arm our content creators, producers and every department at BuzzFeed with the data and insights they need to make decisions and iterate rapidly,” said Nick Hardy, Data Scientist at Buzzfeed. “With the introduction of BQML, we can further expand the ways these products are impacting our Data Science workflow — we’re excited to see what new opportunities it unlocks.”

For more information, go here.

Chris Preimesberger

Chris J. Preimesberger

Chris J. Preimesberger is Editor-in-Chief of eWEEK and responsible for all the publication's coverage. In his 13 years and more than 4,000 articles at eWEEK, he has distinguished himself in reporting...