With bets on Looker and BigQuery, Google bolsters cloud data analytics push


Less than two years after taking the helm at Google Cloud, Chief Executive Thomas Kurian has made his playbook for competing with Amazon Web Services Inc. and Microsoft Azure clear: Build for a multicloud future, democratize analytics and press the benefit provided by BigQuery.

That final technique has proved to be a significant factor in Google Cloud’s current monetary resurgence. Designed from the beginning as a cloud-native database, BigQuery was singled out in guardian firm Alphabet Inc.’s October earnings report as a key contributor to a number of main buyer wins and a 45% bounce in income for Google Cloud over the last quarter.

BigQuery’s emergence as a sexy data warehouse and analytics choice has given Google Cloud a much-needed tailwind. However, it was Kurian’s first main acquisition final 12 months, the most important underneath his tenure to this point, that will transform essentially the most vital consider propelling the group’s cloud success.

In June 2019, Google paid $2.6 billion to purchase the data analytics software supplier Looker Inc., in a proposed deal that turned closing in February. Looker provided its clients, 350 of which have been additionally Google Cloud customers, a method for enterprises to derive insights rapidly from large datasets. That enterprise crucial has change into all of the extra obvious currently, most not too long ago with the blockbuster preliminary public providing of inventory by cloud data warehouse upstart Snowflake Inc., which goals to make the storage and evaluation of data a lot simpler.

But regardless that many observers considered the Looker acquisition as a method for Google to bolster its analytics platform, there was maybe a extra vital motivation behind its multibillion-dollar funding. The firm was constructing a crucial bridge into the red-hot world of builders, cloud-native software growth and APIs.

“A lot of people think about Looker as a business intelligence platform, but it’s actually much more than that,” mentioned Debanjan Saha (pictured), vp and normal supervisor of data analytics at Google, who spoke with Paul Gillin, SiliconANGLE senior editor and host of theCUBE video studio.

“What is unique about Looker is the semantic model that Looker can build on top of data assets,” Saha defined. “Once you have the data model, you can create a data API, an integrated development environment on top of which you can build your custom workflows, your custom dashboard, your custom data application. That is where we are moving.”

Giving builders choices

Following the acquisition of Looker, Google made two extra strikes previously 12 months to reinforce its data analytics portfolio. Both have been revealing when it comes to the positioning of Google Cloud.

In July, Google introduced the non-public launch of BigQuery Omni, an prolonged providing that may finally enable builders to make use of BigQuery throughout a number of clouds, together with AWS and Azure. The message right here is that Google desires to provide builders an choice to investigate data domestically and keep away from transferring it between platforms the place it most assuredly resides, recognizing that that is now a multicloud world. And it’s going to use its large guess on the Anthos platform to make that occur.

“More than 80% of the customers we talk to use multiple clouds and that trend is probably not going to change,” Saha mentioned. “Sometimes it’s because of compliance, sometimes it’s because they want different tools on different platforms. We are a big believer in a multicloud strategy and that’s what we are trying to do with BigQuery Omni.”

The second main transfer occurred in August, when Looker unveiled new data analytics options. In addition to enterprise intelligence and analytics platform upgrades, Looker launched a brand new growth framework into normal availability that allowed builders to customise data experiences and embed them into enterprise apps.

Google’s intent is to maneuver past dashboards and empower builders to customise the data they want for particular duties.

“I don’t think people want the old dashboards anymore,” Saha defined. “They want their data experience to be immersive within the workflow and within the context in which they are using the data. A lot of customers are now using the power of Looker and BigQuery and other platforms we have and building this custom data app.”

Data democratization

Google can also be leveraging the facility of its public cloud to advertise the democratization of data science. This is a transparent theme that has been fastidiously built-in into the corporate’s weblog posts and the writings of Saha himself over the previous two years.

From Google’s perspective, improved data entry by way of BigQuery permits data analysts to get insights extra simply by utilizing acquainted instruments akin to SQL. BigQuery’s serverless backend makes self-service enterprise intelligence a less complicated and extra scalable proposition for any quantity of data or variety of customers. That’s the facility provided by public cloud, Saha says.

“Large enterprises can afford to build a large data center and bring in tens of thousands of CPU cores, GPU cores,” Saha famous. “But it is difficult for smaller enterprises to have access to that amount of computing power, which is very important for data science. Cloud makes it easy. It has in many ways democratized the use of data science.”

Google’s strategy will get no argument from the enterprise world. In a examine carried out by the Harvard Business Review, sponsored by Google Cloud, 97% of firm leaders agreed that democratizing entry to data and analytics throughout the group was vital to enterprise success.

The identical HBR report additionally discovered that 72% of leaders considered the power to automate data-driven perception with machine learning constructed into workflows as “extremely important” to organizational efficiency. Google has taken that sentiment to coronary heart by releasing BigQuery ML in 2018 and Data QnA, an AI service for enterprise analytical queries, earlier this 12 months.

Whether it includes using SQL or pure language processing, Google desires individuals to really feel at house with no matter instruments they’re most snug utilizing for data analytics.

“With BigQuery ML, we have made it possible for our users who like SQL to use the power of machine learning without having to learn anything else or without having to move their data anywhere else,” Saha mentioned. “What QnA allows you to do is to use natural language on top of BigQuery data. If you can do that, it is the nirvana where someone working in a call center talking to a customer can use a simple query to figure out what’s happening with their bill.”

Pockets of power

Will Kurian’s recreation plan for Google Cloud repay? The firm has weathered the financial storms of 2020 by touchdown a variety of giant clients, with Nokia, Major League Baseball and the Indian know-how large Wipro Ltd. all signing up in current months. That lends credence to those that consider that Google is lastly beginning to determine the best way to promote to the enterprise.

In addition, buyer survey data from the summer time revealed pockets of power for Google Cloud, with improved market place for database and analytics. In a time when even a three-month forecast could seem formidable, Google is betting that enterprise clients might want to see what lies forward now greater than ever.

“Pretty much every business is leaning heavily on their data infrastructure to gain insight into what’s coming next,” Saha mentioned. “A lot of the models that people are used to are no longer valid, things are changing very rapidly. In order to survive and thrive, people have to lean on data, lean on analytics to figure out what’s coming around the corner.”

Here’s the whole video interview with Saha, certainly one of many Cube Conversations from SiliconANGLE and theCUBE:

Photo: SiliconANGLE

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