Quantitative hedge funds process large data sets with sophisticated financial models to drive investment decisions. Machine Learning is used to continuously improve models and maximize financial return. One firm with billions ($US) of assets under management turned to Alluxio to address the performance and cost challenges of large scale data processing in a hybrid cloud environment. With Alluxio, the number of model runs per day increased by 4x and the cost of compute was reduced by 95%.
Tag: case study
From our friends at MOMO The hadoop ecosystem makes many distributed system/algorithms easier to use and generally lowers the cost of operations. However, enterprises and vendors are never satisfied with that, so higher performance becomes the next issue. We considered several options to address our performance needs and focused our efforts on Alluxio, which improves performance … Continued
Lenovo is the world’s largest personal computer vendor and one of the world’s largest smartphone vendors. The company has invested extensively in global information technology infrastructure, including ten data centers worldwide collecting petabytes of smartphone data. Analyzing data located in multiple data centers world-wide is critical for Lenovo to understand and improve the usability and reliability of their products.
Many organizations deploy Alluxio together with Spark for performance gains and data manageability benefits. Qunar recently deployed Alluxio in production, and their Spark streaming jobs sped up by 15x on average and up to 300x during peak times. They noticed that some Spark jobs would slow down or would not finish, but with Alluxio, those jobs could finish quickly. In this blog post, we investigate how Alluxio helps Spark be more effective. Alluxio increases performance of Spark jobs, helps Spark jobs perform more predictably, and enables multiple Spark jobs to share the same data from memory.
In a real development environment our customers leverage ArcGIS to read and write geospatial data to a plethora of distributed data stores, such as Amazon S3, HDFS, or OpenStack Swift, and some of these data stores are not natively supported by the ArcGIS platform…