Rick Boone, Strategic Advisor for Uber's Core Infrastructure group, talks about his journey from his work in site reliability to his current role in long-term planning for infrastructure health and scalability.
In 2019, Uber's Infrastructure team built new services and systems to enable resource savings, efficiency gains, and greater resilience across our technology stack.
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.
On May 3, 2019, Uber’s Programming Systems Team hosted the Programming Systems and Tools Track of the company’s Second Uber Science Symposium, featuring a full day of talks by leading researchers and practitioners in the the field.
Implementing QUIC protocol against TCP over cellular networks on our apps led to a reduction of 10-30 percent in tail-end latencies for HTTP traffic.
Aarhus Engineering Internship: Building Aggregation Support for YQL, Uber’s Graph Query Language for Grail
Uber intern Lau Skorstengaard shares his experience working on YQL, the graph query language for our in-house infrastructure state aggregation platform.
M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.
Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.
On April 19, 2018, Uber's LadyEng group hosted Going Global: Uber Tech Day, our second annual event focused on showcasing the technical work of engineers, data scientists, and product managers from across the company.
Uber's Data Infrastructure team overhauled our approach to scaling our storage infrastructure by incorporating several new features and functionalities, including ViewFs, NameNode garbage collection tuning, and an HDFS load management service.
Uber Engineering built QALM, a smart load management tool allowing for graceful degradation by preserving critical system requests and shedding non-critical requests.
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.
Get to know Uber Aarhus Engineering and the work they do behind the scenes to build and maintain our storage and compute platforms.
Snap your fingers and presto! How Uber Engineering built a fast, efficient data analytics system with Presto and Parquet.
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.
The making of Schemaless, Uber Engineering’s custom designed datastore using MySQL, which has allowed us to scale from 2014 to beyond. This is part one of a three-part series on Schemaless.
Here’s a look inside the world of Supply Engineering at Uber, obsessed with creating the best, most scalable earnings platform for our partners in over 330 cities.