How Uber Uses Scala
19 engineering articles about Scala from Uber's engineering team
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Uber's migration from Spark 2. 4 to Spark 3. 3 involved upgrading over 2 million Spark applications, utilizing innovative automation tools like Iron Dome.
Amruth Sampath, Arnav Balyan, Nimesh Khandelwal, Sumit Singh, Parth Halani, Suprit Acharya
8 min read
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The article discusses Uber's upgrade of its search platform from Lucene version 7. 5. 0 to 9. 4.
Anand Kotriwal, Aparajita Pandey, Charu Jain, Yupeng Fu
12 min read
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The article discusses how Uber utilizes Apache Pinot for low-latency offline table analytics, highlighting its capabilities in handling various use cases, including real-time and offline data inges...
Ankit Sultana, Caner Balci
15 min read
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The article discusses the Sparkle framework developed by Uber to standardize modular ETL processes, enhancing developer productivity and data quality.
Dinesh Jagannathan, Sharath Bhat, Suman Voleti, Praveen Raj
8 min read
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This article details Uber's migration of over a trillion entries of ledger data from DynamoDB to LedgerStore, focusing on the challenges, strategies, and outcomes of the process.
The article discusses how Uber implemented an incremental ETL process using Apache Hudi to manage its transactional data lake.
The article discusses Uber's journey in rebuilding its A/B testing platform, Morpheus, to address scalability and reliability challenges.
The article discusses Project RADAR, an intelligent fraud detection system developed by Uber that integrates machine learning and human expertise to identify and mitigate fraudulent activities in r...
Sergey Zelvenskiy, Garvit Harisinghani, Tiffany Yu, Edwin Ng, Robin Wei
14 min read
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This article discusses Uber's journey in containerizing their Apache Hadoop infrastructure, detailing the challenges faced and the solutions implemented over two years.
The article introduces the 2020 Safety Engineering interns at Uber, highlighting their experiences during a unique summer internship affected by COVID-19.
Safety Engineering Interns
10 min read
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This article discusses the optimization of JVM memory and garbage collection (GC) for large-scale services at Uber, focusing on the challenges and solutions implemented to enhance performance and r...
Xinli Shang, Yi Zhang, Fengnan Li, Amruth Sampath, Girish Baliga
29 min read
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The article discusses how Uber has implemented the Uber Spark Compute Service (uSCS) to simplify the use of Apache Spark across its extensive infrastructure.
The article discusses the evolution and scaling of Uber's machine learning platform, Michelangelo, highlighting its development, deployment, and operational strategies.
Jeremy Hermann, Mike Del Balso
29 min read
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The article discusses Uber's implementation of an automated feature rollout system that utilizes robust regression analysis to ensure safe deployments across its mobile applications.
The article discusses building reliable reprocessing and dead letter queues using Apache Kafka in distributed systems.
Ning Xia
10 min read
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The article introduces Michelangelo, Uber's internal machine learning platform designed to democratize machine learning and streamline the process of building, deploying, and operating ML solutions...
Jeremy Hermann, Mike Del Balso
24 min read
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This article details how Uber Engineering developed an intelligent experimentation platform (XP) to facilitate the stable and rapid rollout of new features across its applications.
The article discusses the evolution of distributed tracing at Uber Engineering, detailing the transition from monolithic applications to microservices and the challenges faced in maintaining system...
The article discusses Streamific, Uber's in-house ingestion service designed for efficiently streaming data into Hadoop's ecosystem.
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