Creating a Data-Driven Enterprise with DataOps

Part II. Case Studies

In Part II, we present four case studies contributed by data professionals at leading companies at different stages of the self-service journey.

In Chapter 9, we get some insight from LinkedIn. As with all consumer web companies, there has always been an enormous amount of data that’s flowed through LinkedIn. But the organization was relatively early to realize the importance of this data. This chapter shows how by centralizing a data group and creating a self-service infrastructure, employees throughout LinkedIn began making data-driven decisions.

In Chapter 10, we learn about Uber. The company realized that the only way to ensure that it could offer good user experience with operational efficiency was by using data effectively. Uber’s challenge was that it was too successful—growing too fast to have a coherent plan. This chapter explains how the company adapted and changed its data strategy to accommodate that growth.

Chapter 11 profiles Twitter, an organization that is all about real time and that needs infrastructure to handle data in real time to do analytics and provide insights. The company built Heron, a real-time, distributed, and fault-tolerant stream-processing engine that has powered all of Twitter’s real-time analytics across since 2014. Looking ahead, Twitter is hoping to build a system that can identify and fix problems as they are occurring.

Finally, in Chapter 12, we look at eBay. From its early days, eBay had a strong data-driven culture ...