GoDaddy’s big data analytics
GoDaddy, with its web hosting and Internet domain name registration businesses, ingests on a daily basis more than 13 terabytes of new, uncompressed data, everything from Web site traffic and usage metrics to server management and customers’ e-commerce statistics.
GoDaddy’s staff uses all that data to configure products and provide client services to its 14.7 million customers, which range from major corporations to mom-and-pop shops. The company’s in-house staff also taps the data to conduct analyses of its operations in order to better meet customers’ needs.
However, until a few years ago, when GoDaddy’s staff wanted to dig a little deeper into its operations, they often dumped data into Microsoft Excel spreadsheets. GoDaddy, however, wanted to give them something better, according to Sharon Graves, a systems administrator at GoDaddy.
Today, the company is delivering online self-service analytics with Tableau as the primary visualization tool.
“By creating a self-service environment,” Graves says, “GoDaddy product managers and business users can leverage data to create a better customer experience, and find and design the product that will meet their needs by identifying trends and anticipating issues.”
GoDaddy was founded in 1997 as Jomax Technologies, and launched its first web site a year later. By 2005 the company, which was renamed GoDaddy in 1999, had 2 million customers, 10 million domain registrations and $100 million in annual revenue. The company went public in 2015, when it had reached 4 million international customers. By 2016 GoDaddy had achieved $1 billion in domains bookings and $1.8 billion in annual revenue.
GoDaddy, according to Graves, has a big data platform that includes Hadoop Hive, a data warehouse system for query and analysis; Microsoft SQL, MySQL and Teradata relational database management systems; Cassandra, an open-source distributed database management system; Apache Pig, a data analytics processing engine; Apache Spark, a processing engine for cluster-computing environments; and third-party online analytics tools such as Google Analytics.
To arm its staff with something better than spreadsheets with which to look at its data, GoDaddy, in 2013, implemented Tableau Server, an enterprise-wide visual analytics platform, first for its business intelligence team and then for other users within the organization.
These power users, most with some query knowledge and at least some exposure to analytic measures, were the first to serve up their own, somewhat limited, analytics.
But with so much data coming into the organization every day, GoDaddy had partialdocumentation on the origins of the data, its usage and how some of the calculated fields might have been derived.
“Our power users who may not be intimately familiar with the data didn’t know where to find the appropriate data for their analytics, or, if they did know where it was, they were not sure how to use it to meet their needs,” Graves says.
Analysts were spending much of their time trying to figure out which fields in which data sources they should use, and not enough time conducting actual, meaningful analysis.
To help its product managers, business users, data scientists and other data consumers gain better insights, GoDaddy ramped up its effort. It gave 1,400 users access to Tableau and, at the same time, deployed Alation’s Data Catalog, which reinforce data governance for self-service analytics at scale and allows users to find better insights without the need for intervention from the technical staff. Alation uses machine-learning algorithms to automatically inventory data and enrich it with the context necessary to find, understand and trust the data—creating a single source of reference for an organization‘s data. Alation runs on a Linux server and through machine learning “profiles” connected data platforms to profile all of GoDaddy’s data.
