Tag: Data Analytics
In 2019, Uber's Data Platform team leveraged data science to improve the efficiency of our infrastructure, enabling us to compute optimum datastore and hardware usage.
Uber is honored to join the Presto Foundation, a new initiative hosted by the Linux Foundation, to advance the open source data processing community.
Noticing increased latency in our metrics platform, Uber engineers track down a bug related to stack growth in a goroutine, resulting in a fix elevated to the Go open source GitHub repository.
Uber's Maps Collection and Reporting (MapCARs) team shares best practices when choosing which HDFS file formats are optimal for use with Apache Spark.
DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data Lake
Uber engineers discuss the development of DBEvents, a change data capture system designed for high data quality and freshness that is capable of operating on a global scale.
AresDB, Uber's open source real-time analytics engine, leverages GPUs to enable real-time computation and data processing in parallel.
Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.
In an interview for the Uber Eng blog, Data Scientist Sunny Jeon talks about how his team develops solutions in order to advance Uber's core value of safety.
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
Uber Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.
The UberEATS Restaurant Manager gives restaurant partners insight into their business by measuring customer satisfaction, sales, and service quality.
Snap your fingers and presto! How Uber Engineering built a fast, efficient data analytics system with Presto and Parquet.