Looker Announces Tools and Integrations to Optimize Data Science Workflows
SANTA CRUZ, Calif., May 15, 2018 — 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.
“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,” said 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.”
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
“We are excited to enhance Looker’s data science capabilities for their users through Kraken, our automated machine learning platform. Working with Looker’s diverse partner community and skilled user base has allowed us to create our own Looker Blocks called MLKits; faster, more automated solutions for machine learning insights,” said Mark Dillon, SVP Strategy and Alliances at Big Squid. “Using LookML, Big Squid is leveraging the work that Looker users have already done to extract and prepare their data, and then automating the analysis and model selection needed to learn from that data. Model results and data can then be pulled back into Looker directly for visualization, analysis, and action.”
“DataRobot and Looker have complementary approaches to data: we simplify, model, and automate user workflows for big data analytics and machine learning to improve efficiencies,” said Seann Gardiner, EVP, Business Development at DataRobot. “Our joint customers see dramatic increases in the efficiency of their machine learning workflows, more than 500% in some cases. With tools like DataRobot for deploying AI and Looker for curating data, users can build powerful, enterprise-grade, automated machine learning more quickly than ever.”
Learn more about how Looker can simplify and accelerate the data science workflow.
- See Looker in the data science workflow in action
- Watch how Looker improves Stack Overflow’s data science process
- Join Looker’s webinar hosted by Data Science Central to learn how innovative companies are modernizing their tech stack and workflows for data scientists.
- Read how to create more impact with efficient data science workflows.
Looker is a complete data platform that offers data analytics and business insights to every department, and easily integrates into applications to deliver data directly into the decision-making process. The company is powering data-driven cultures at more than 1300 industry-leading and innovative companies such as Sony, Amazon, The Economist, IBM, Spotify, Etsy, Lyft and Kickstarter. The company is headquartered in Santa Cruz, California, with offices in San Francisco, New York, Chicago, Boulder, London and Dublin, Ireland. Investors include CapitalG, Kleiner Perkins Caufield & Byers, Meritech Capital Partners, Redpoint Ventures, First Round Capital, Sapphire Ventures and Goldman Sachs.