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VCs flash cash for Looker's SQL-on-steroids business intelligence tool

BI bods take valley lucre to fund sales bods

The smart choice: opportunity from uncertainty

Business intelligence is the site of a new goldrush as the twin marketing totems of "big data" and "cloud" collide, creating a supernova of splashy cash for canny firms.

So it is with little surprise that a BI startup headed by former Greenplum exec Frank Bien and Netscape technologist Lloyd Tabb has announced $16 million in funding from VCs, eager to slather the company in filthy valley lucre.

The money is going to be used to expand the young company's sales team, as it tries to expand beyond its 40 current customers and get its technology – the Looker BI Platform – humming inside more businesses.

Looker is a BI technology that uses a proprietary language named LookML to provide both SQL compatibility and custom language features to help analysts manipulate data models.

LookML lets people write queries in SQL that can then be reused on other batches of data – as long as they share the same schemas – without needing to be written to account for changes in field types, the company's CTO Tabb told El Reg.

"The problem with SQL is its re-usability model is broken," he said. "When someone has a SQL query, they query against a database and they get their results back. The way most people work is they have a library of queries they've written — that's very un-traditional in computer science. There's no building upon a framework, so LookML presents a framework. You program at the field level as opposed to the query level."

This lets admins spend less time writing basic SQL queries for business analysts, and more time crafting more complex questions to get the most out of their data, Tabb says.

Looker is deployed as a web server and can run either on-premise or in the cloud. It is designed to be applied to data kept in large data repositories such as Amazon's "RedShift" data warehouse, or Greenplum. To get going on the platform, Looker's internal engineering team will help interface the technology with the organization's data to help build the LookerML framework. Once the framework is about 75 percent accurate, Looker will hand it over to the buyer who can start to fiddle, tweak, and re-query until it does what they want.

The goal of the tech is to make it simpler for organizations to query their data, and take the strain off BI admins.

"The analysts we work with say they do more data science," Tabb says. "They get to do more sophisticated work because the business users get to self serve." ®

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