Looker flouts conventional wisdom in making data lakes accessible

Looker flouts conventional wisdom in making data lakes accessible

Looker flouts conventional wisdom in making data lakes accessible

Ever since business intelligence (BI) was invented, people have been trying to fix it.

It was supposed to put visual, easy-to-understand dashboards on the desk of everyman/woman, but that promise remained unfulfilled because of the limitations of those dashboards and the complexity of classic BI platforms. It wasn't until the emergence of Tableau that at least the promise of ubiquitous dashboards (we now call them visualizations) finally materialized.

But Looker has a different take.

It starts with data lakes, which are supposed to put all the data in one place, and self-service, which threatens to undermine that by proliferating islands of visualizations, each with their own caches or extracts of the data. The question is, has self-service come at the cost of providing a single source of the truth from the data lake? It's the latest chapter in the age-old saga of empowerment vs. control.

Looker claims it has an answer to that - it aims to offer the best of both worlds. How it accomplishes that is the interesting part: Looker's approach has constantly tilted at windmills.

Read Also:
Will We Soon No Longer Need Data Scientists?

First, it straddles the self-service vs. centralization debate. Looker is not against self-service, but it's against proliferating separate caches of the data. It offers a visual client side that allows business users to dynamically explore data on their own without being limited to what's cached or in memory. The modeling tier allows that to happen, keeping the views and the data populating them consistent, so everybody is literally on the same page.

Secondly, Looker isn't pigeonholed; it's both end user visualization client and developer tool.

Then there's Looker's middleware-style architecture that may seem a bit retro (especially if you survived the SOA era) as it abstracts the view of the data from the underlying physical representation. And finally, in an era of open source languages, Looker has the audacity to introduce its own proprietary LookML SQL-based modeling language.

By conventional wisdom, Looker's done everything wrong, but in the five years since its founding, the company has raised nearly $100 million in venture funding, and over the past year has doubled the client base to roughly 750.

Read Also:
Online epidemic tracking tool embraces open data and collective intelligence

Looker works by relying on the LookML modeling language to, in effect, wrap SQL with far richer metadata on what tables to use, how to join them, and how to calculate derived data. Much of this metadata is autogenerated by the tool itself from crawling target databases. By modeling, not only how the data is structured, but also how it is consumed, Looker can reduce or eliminate ETL jobs.

 



Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
Will We Soon No Longer Need Data Scientists?

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
Getting data privacy and security right is 'paramount' to success of open banking, says regulator

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
Big data analysis is the future of forex trading

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
Datameer and Cloudera enhance analytics for Aussie telcos
Read Also:
NoSQL vendors to lead the charge in fast-growing Big Data market, Forrester says

HR & Workforce Analytics Innovation Summit 2017 London

12
Jun
2017
HR & Workforce Analytics Innovation Summit 2017 London

$200 off with code DATA200

Read Also:
4 Business Risks Preventing Big Data ROI

Leave a Reply

Your email address will not be published. Required fields are marked *