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.
Facing the need for a resilient data structure over thousands of storage nodes to serve the 15 million rides per day that occur on our platform, Uber engineers developed Herb, our data replication solution. Herb ensures data availability and integrity across our data centers.
Migrating our Schemaless sharding layer from Python to Go while in production demonstrated that it was possible for us to rewrite the frontend of a massive datastore with zero downtime.
In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.
Get to know Uber Aarhus Engineering and the work they do behind the scenes to build and maintain our storage and compute platforms.
Chameleon, a global CMS for Uber.com, enables regional operations and marketing teams at Uber to build and ship customized, on-brand webpages.
Snap your fingers and presto! How Uber Engineering built a fast, efficient data analytics system with Presto and Parquet.
Here we look at Hadoop data ingestion, and how Uber Engineering streams diverse data into a cohesive layer for querying in near real-time using our in-house developed Streamific.
The details and examples of Schemaless triggers, a key feature of the datastore that’s kept Uber Engineering scaling since October 2014. This is the third installment of a three-part series on Schemaless; the first part is a design overview and the second part is a discussion of architecture.