Viant: Democratizing access to big data with BigQuery

Viant helps marketers plan, execute and measure their digital media investments using a cloud-based platform, providing access to over 1.2 billion registered users, one of the largest registered user databases in the world. The company needed a system that could scale affordably on demand to keep up with its increasing data needs, both in the volume of data and number of customers it serves. To help its customers make better choices for their ad campaigns, it wanted to give them the most sophisticated analytics tools possible.

“The success of Viant is built on data, and on getting information into our customer’s hands as quickly as possible. We needed to give data access and querying capabilities to multiple staff members and our widely dispersed customers. To do it, we chose Google Cloud Platform because of the ease with which it lets data and analytics tools be shared, its ability to scale quickly and inexpensively, and the speed of its queries.”

— Mickey Hsieh, Vice President of Engineering, Data and Applications, Viant

Handling fast-growing online transactions

When Viant launched, it tracked data using MySQL, then switched to a combination of an expensive hardware appliance and Hadoop. But the data appliance was too costly, MySQL and Hadoop queries ran slowly, and the system wasn’t easily scalable. In addition, Viant wanted to get data and analytics tools into the hands of many people in the company and its clients. That wasn’t possible with Hadoop.

When someone wanted to get information, they had to send a request to Viant engineers, who created a report and sent it to the person who requested it. This process took days or even weeks, and wasn’t interactive, so people couldn’t explore the data for new insights. Viant wanted a solution that would democratize data access.

In his search for a solution, Hsieh tried BigQuery. “Once I tried BigQuery and saw how perfect it was for what we needed, I said, ‘Forget it, we don’t need to look at anything else,’” he says.

BigQuery brings data analytics to everyone

Viant uses BigQuery to provide data and information to its advertising customers, which include Coca-Cola and other multinational corporations. On a typical day, the company tracks hundreds of millions of transactions and helps its customers turn that raw data into actionable intelligence. A common query is to understand a set of actions from a unique user that led to a certain event, such as seeing what page he or she visited before completing a purchase. Using MySQL, that query would take 24 hours to complete. With BigQuery it takes 10 seconds. Customers need real-time information to make fast decisions and won’t settle for lags in getting data.

“Advertising is fast moving, and companies need to quickly build, track and change their ad campaigns. With BigQuery, our customers get the information they need within seconds or minutes. And they can iterate their requests and discover things they never would have if they didn’t have direct access to the data themselves,” Hsieh says.

Data scientists, product managers and others who need data have been given querying capabilities. Previously, only two or three users had access to querying capabilities. Now 30 people have it, and that number is steadily growing. They get the information they need instantly, rather than having to wait weeks for it.

Cutting costs, increasing productivity

In addition to speeding up querying and democratizing data access, Viant has improved productivity and cut costs with GCP. Engineers no longer need to spend time writing programs for data requests and running reports. They also don’t have to manage a data hardware appliance or other infrastructure, because GCP handles it all.

“Our engineers are 10 times more productive than they were before, and our costs 10 times less than they would have been because GCP handles our infrastructure so we don’t have to. That frees us to provide better services for our marketing customers, which is ultimately what will guarantee our success,” says Hsieh.