Notes about Cutting-Edge Technologies and Everything

Millions of Uber trips take place each day across nearly 80 countries, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables us to facilitate better experiences for users. To make our data exploration and analysis more streamlined and efficient, we built Uber’s data science workbench (DSW), an all-in-one toolbox for interactive analytics and machine learning that leverages aggregate data. DSW centralizes everything a data scientist needs to perform data exploration, data preparation, ad-hoc analyses, model exploration, workflow scheduling, dashboarding, and collaboration in a single-pane, web-based graphical user interface (GUI). Leveraged by data science, engineering, and operations teams across the company, DSW has quickly scaled to become Uber’s go-to data analytics solution. Current DSW use cases include pricing, safety, fraud detection, and navigation, among other foundational elements of the trip experi...

Apache Spark is a foundational piece of Uber’s Big Data infrastructure that powers many critical aspects of our business. We currently run more than one hundred thousand Spark applications per day, across multiple different compute environments. Spark’s versatility, which allows us to build applications and run them everywhere that we need, makes this scale possible. However, our ever-growing infrastructure means that these environments are constantly changing, making it increasingly difficult for both new and existing users to give their applications reliable access to data sources, compute resources, and supporting tools. Also, as the number of users grow, it becomes more challenging for the data team to communicate these environmental changes to users, and for us to understand exactly how Spark is being used. We built the Uber Spark Compute Service (uSCS) to help manage the complexities of running Spark at this scale. This Spark-as-a-service solution leverages Apache Livy, cu...

Netflix Data Landscape Freedom & Responsibility (F&R) is the lynchpin of Netflix’s culture empowering teams to move fast to deliver on innovation and operate with freedom to satisfy their mission. Central engineering teams provide paved paths (secure, vetted and supported options) and guard rails to help reduce variance in choices available for tools and technologies to support the development of scalable technical architectures. Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management. Therefore, building a complete and accurate data lineage system to map out all the data-artifacts (including in-motion and at-rest data repositories, Kafka topics, apps, reports and dashboards, interactive and ad-hoc analysis queries, ML and experimentation models) is a monumental task and requires a scalable architecture, robust design, a strong engineering team and above all, amazing cross-f...