How Uber Uses Cassandra

This article discusses the development and implementation of forecasting models aimed at improving driver availability at airports, which are critical to Uber's ridesharing ecosystem.

The article discusses Uber's implementation of encryption at rest and disk isolation at scale using their Stateful Platform, Odin.

The article discusses Uber's robust database backup recovery system, highlighting its importance for business continuity and disaster recovery.

The article discusses Uber's migration of large-scale interactive compute workloads from Peloton to Kubernetes, focusing on minimizing disruption while enhancing resource management and cloud readi...

The article discusses Uber's transition to a multi-architecture environment by adopting Arm-based hosts at scale.

The article discusses The Accounter, a global coordination system developed by Uber to enhance operational throughput and safety on its stateful platform, Odin.

The article discusses Uber's advanced settlement accounting system, which is crucial for managing financial transactions involving payment service providers (PSPs).

The article discusses the Sparkle framework developed by Uber to standardize modular ETL processes, enhancing developer productivity and data quality.

Odin is Uber's stateful platform designed to manage various technologies for data storage efficiently.

This article discusses how Uber has implemented single-zone failure tolerance (SZFT) for its Apache Cassandra® database, ensuring high availability even in the event of a zone failure.

The article discusses Uber's journey in enhancing its Palette Meta Store, focusing on the challenges faced, the solutions implemented, and the resulting improvements in machine learning feature man...

The article discusses the evolution of Data Lifecycle Management (DLM) at Uber, detailing the journey from initial implementations to the development of a unified system.

The article discusses how Uber optimized its operations of the open-source Apache Cassandra database at scale, addressing various challenges and improvements made over time.

Cadence 1. 0 is a powerful open-source workflow orchestration platform designed for building and managing stateful services at scale.

The article discusses Uber's transition from a Server-Sent Events (SSE) architecture to a gRPC-based push platform, detailing the motivations, implementation challenges, and outcomes of this migrat...

The article discusses Uber's implementation of an automated vertical CPU scaling system that optimizes resource allocation for storage workloads, leading to significant cost savings and improved re...

The article discusses strategies to avoid CPU throttling in a containerized environment, particularly at Uber, where stateful workloads run on a large fleet of hosts.

The article discusses the challenges and solutions in building scalable streaming pipelines for generating near real-time features at Uber.

The article discusses Uber's comprehensive re-architecture of its Fulfillment Platform, aimed at enhancing its Go/Get strategy.

The article discusses Uber's 'Orders Near You' feature, which utilizes real-time geospatial data analytics to enhance user experience in the Uber Eats app.

The article discusses the challenges posed by flaky unit tests in Java, particularly in the context of Continuous Integration (CI) systems.

The article discusses the evolution of Uber's Schemaless datastore into a distributed SQL database called Docstore, highlighting its features, architecture, and motivation behind the transition.

The article discusses Uber's journey towards metric standardization through the development of uMetric, a unified internal metric platform.

Uber's Real-Time Push Platform focuses on enhancing user experiences by transitioning from polling to a gRPC-based bi-directional streaming protocol.

The article discusses Uber's development of uWorc, a no-code workflow orchestrator designed to simplify the creation of batch and streaming data pipelines.

The article discusses Uber's Databook, an in-house platform designed to manage and surface metadata related to various data entities.

The article discusses Fiber, a distributed computing library developed by Uber that simplifies large-scale computation for AI and machine learning applications.

The article discusses Uber's implementation of multi-tenancy within its microservice architecture, highlighting its benefits for stability, modularity, and developer velocity.

The article highlights the contributions of women engineers at Uber in celebration of International Women’s Day.

The article discusses how Uber engineered SQL support on Apache Pinot, enhancing real-time analytics capabilities for their Big Data stack.

The article discusses Uber's advancements in data infrastructure during 2019, focusing on how data science was leveraged to optimize performance and manage vast amounts of data.

The article discusses Uber's open-source orchestration tool, Cadence, which facilitates seamless communication and coordination among microservices in a scalable and fault-tolerant manner.

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 Uber's approach to consistent data partitioning using a Global Index for managing large Apache Hadoop tables.

DBEvents is a change data capture system developed by Uber to efficiently ingest data into their Apache Hadoop data lake.

The article discusses how Uber employs machine learning to ensure the capacity safety of individual microservices, addressing challenges related to predicting service-level capacity requirements.

The article discusses Uber's approach to building a scalable observability and alerting ecosystem, highlighting the development of two key systems, uMonitor and Neris, which handle metrics-based an...

The article discusses the evolution and scaling of Uber's machine learning platform, Michelangelo, highlighting its development, deployment, and operational strategies.

The article discusses Peloton, Uber's unified resource scheduler designed to manage diverse cluster workloads efficiently.

The article highlights seven open source projects showcased at the Uber Open Summit, emphasizing Uber's contributions to the open source community.

The article highlights the Uber Amsterdam Tech Team's focus on transforming payment systems and empowering developers.

The article features an interview with Yuri Shkuro, the Jaeger Project Lead at Uber, discussing the development and open sourcing of Jaeger, a distributed tracing system.

The article discusses Uber's implementation of a one-click chat feature (OCC) designed to enhance communication between drivers and riders.

Marmaray is an open-source data ingestion and dispersal framework designed for Apache Hadoop, enabling Uber to manage large volumes of data efficiently.

The article discusses Uber's Databook platform, which transforms big data into actionable knowledge by managing metadata.

The article discusses how Uber enhances the quality of its maps through metrics computation, emphasizing the importance of accurate and up-to-date map data for improving user experience.

This article discusses how Uber leverages data science and machine learning to enhance financial forecasting and planning.

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...

The article discusses Uber Engineering's XP Background Push, a tool designed to enhance the reliability of mobile applications by enabling real-time bug fixes and feature management.

The article discusses the redesign of Uber Engineering's mobile content delivery ecosystem, focusing on the technical challenges and solutions developed for a new content feed and backend system.