Looker, the company on a mission to power data-driven companies, today announced a series of Looker Blocks for Google BigQuery that make it easy to build an organization-wide data analytics platform. As companies continue to invest in data, they are demanding an intuitive and fast way for everyone to access and explore terabytes or even petabytes of data. Looker, with Looker Blocks for BigQuery and Looker’s unique in-database architecture, is the only analytics tool that makes every petabyte of data in BigQuery available to everyone in a company, not just the data scientist. With Looker and BigQuery, you can scale query processing power and storage independently and elastically, allowing for fast exploration over massive data sets.
While the importance of data is now undeniable, companies have struggled to get value from their investments in massive storage infrastructures. Analytical tools that address these super large data sets have been too complicated, unwieldy, and slow, forcing business users to analyze subsets of the data creating information silos across an organization. Today, as both the amount of data and its importance to businesses have grown, companies are hoping to abandon tools that create data silos because extracting subsets or cubing data is counter to the core value of big data itself – big data isn’t big if you are only looking at a fraction of it. Looker and BigQuery are at the heart of this transition. BigQuery, unlike all other databases, never runs out of storage and never gets slow. Looker is the only tool that lets users harness the power of the underlying database with its unique in-database architecture. Looker offers fast time to value by operating on the data directly in BigQuery, never extracting subsets of data, making incredibly fast queries against all of the data available to everyone.
We have over 3.5 petabytes of data and our teams need access to all of it to run our business. While Google BigQuery is cost efficient and incredibly fast, only a few people know how to use it. Looker’s data platform lets us connect our campaign managers to all the data in BigQuery in a structured way so we are able to continually optimize ad performance and sales,” said Daniel De Sybel, Chief Technology Officer at Infectious Media.
BigQuery is compatible with many of the existing Looker Blocks that launched in September 2015 to rapid adoption and success. Today, Looker is announcing a few specific Looker Blocks and additional features to connect with the Google Cloud:
The BigQuery Table Date Range Analytics Looker Block capitalizes on BigQuery’s unique approach to data partitioning, allowing Looker to optimize query performance and making it easy for end users to understand event data over time.
Looker is also announcing a new Query Size Estimator. This feature allows data analysts to determine the size of a query before it is run from directly within Looker’s data platform, giving data analysts greater insight for database management.
Looker and BigQuery are compatible because both technologies are, at the core, architected to scale. Whether you have 250 gigabytes or petabytes of data, Looker makes data exploration easy and accessible to everyone within a company,” said Frank Bien, CEO of Looker. “We aren’t just a visualization tool that only analyzes subsets of the data, we are a reliable data platform for organization-wide analytics and business metrics.”
The Google Analytics Premium Looker Block sessionizes events at the individual user level and comes pre-built with a full suite of web analytics metrics such as funnels, bounce rates, and attribution analysis. Once up and running with the GA Premium Block, you can then layer on additional custom analysis to understand the metrics that drive your business.
Looker Blocks make it quick and easy to transform massive amounts of data, helping you build a governed platform where all your data can be accessed in meaningful, efficient ways by everyone in your company. Several additional BigQuery-specific Looker Blocks are planned for release over the next few months.