Looker Introduces Enhanced Datafold Engine

Print Friendly, PDF & Email

Looker-logoLooker, a business analytics software company, today announced its enhanced Looker Datafold Engine, with support for persistent derived tables to deliver faster, more meaningful business insights. Persistent derived tables rely on Looker’s in-database architecture to empower data analysts and reduce their workloads. Analysts can now model complex raw data—quickly and in multiple ways—without the expensive and time-consuming overhead traditionally required to structure large data sets in advance of analysis. Instead, business users can explore raw data, reset, and dive in again with different parameters, discovering profound insights that are often obscured when using other BI tools.

Existing BI and data discovery products leave businesses blind to events on a granular level because they can’t make sense of massive amounts of data, forcing organizations to aggregate and extract data in advance of exploration. This extraction often obscures the data or limits the ability to understand root cause by restricting detailed drilling. As a result, legacy approaches make it difficult to engage in real-time data discovery—something core to Looker’s value proposition, as speed-to-insight becomes more critical to today’s data-driven businesses.

The Looker Datafold Engine enables the unlocking of massive sets of data and delivers powerful value to today’s businesses,” said Frank Bien, CEO of Looker. “The result is a new kind of business—one that shares and collaborates around data and drives curiosity and intelligence throughout the organization.”

The Datafold Engine uses the underlying analytics database to transform raw data at query time, enabling deep exploration of ever-growing and increasingly complex data sets. And while Looker already supports derived tables, the addition of persistence greatly expands the ways derived tables can be used to extract meaningful results. By automatically refreshing tables in specified conditions, persistence conserves valuable computing resources that would otherwise be needed to query the data store. Persistent derived tables also free up precious technical talent for other business-critical projects.

Use Case: Mindjet Zooms in on Customer Data to Improve Sales Process

The enhanced Looker Datafold Engine allows analysts to deepen their understanding of data, streamline the costs typically associated with modeling, create powerful actionable information, and share that information with people who can take advantage—such as business unit managers, sales staff, and the C-Suite.

Mindjet, a collaborative work management software company in San Francisco, uses Looker to closely track its subscription model and identify where additional products and services would benefit customers. Due to the large amount of event information generated, the company’s legacy BI tools didn’t enable quick analysis into the details of its subscription sales processes. Mindjet leverages the power of the Datafold Engine to drill into its large data sets on demand, giving its analysts and business users multiple views of the data. Persistent derived tables enable them to quickly ask questions with varying parameters, without having to manually create new tables each time they need a different view of the data.

Looker allows us to drill down into our subscription information in many different ways,” said Jascha Kaykas-Wolff CMO at Mindjet. “This saves us untold amounts of time and provides powerful analytics to help us improve the sales cycle and enhance our marketing efforts.”

Unlocking Massive Datasets for Detailed Analysis

The Datafold Engine works in concert with LookML, Looker’s modeling environment, to enable analysts to slice and dice large datasets by any combination of dimensions and measures. With a LookML model, anyone can build off of existing queries and define new parameters of the entire data set—on the fly—eliminating the burden of architecting data for cubes and other BI-specific requirements.

The combination of Looker’s modern approach to data discovery and its in-database architecture allows data-rich organizations to:

  • Quickly and easily define specific dimensions and metrics
  • Drill into detailed data, zoom out for a larger view, then drill back down in a different way
  • Use a dashboard as a starting point for more involved analysis
  • Access data from any application, using Looker as a general-purpose data server


Sign up for the free insideBIGDATA newsletter.



Speak Your Mind