Notes about Cutting-Edge Technologies and Everything

  In this blog post, we shared efforts and ideas in improving the platform efficiency of Uber’s Big Data Platform, including file format improvements, HDFS erasure coding, YARN scheduling policy improvements, load balancing, query engines, and Apache Hudi.  These improvements have resulted in significant savings.  In addition, we explored some open challenges like analytics and online colocation, and pricing mechanisms.  However, as the framework outlined in our previous post established, platform efficiency improvements alone do not guarantee efficient operation.  Controlling the supply and the demand of data is equally important, which we will address in an upcoming post. As Uber’s business has expanded, the underlying pool of data that powers it has grown exponentially, and thus ever more expensive to process. When Big Data rose to become one of our largest operational expenses, we began an initiative to reduce costs on our data platform, which divides challe...