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Uber

Fast Copy-On-Write within Apache Parquet for Data Lakehouse ACID Upserts

Experience the power of row-level secondary indexing in Apache Parquet, enabling 3-20X faster upserts and unlocking new possibilities for ef...

Uber

Spark Analysers: Catching Anti-Patterns In Spark Apps

Uber runs more than 100K big data workloads per day using Apache Spark–at that scale it’s crucial to write optimized apps.

Uber

Optimizing HDFS with DataNode Local Cache for High-Density HDD Adoption

This blog post unveils the seamless, exabyte-scale integration of local SSD disks into the Hadoop Distributed File System (HDFS), enabling t...

Uber

Setting Uber’s Transactional Data Lake in Motion with Incremental ETL Using Apache Hudi

Uber’s Global Data Warehouse team leveraged Apache Hudi to drastically improve performance of traditional batch ETL pipelines by going incre...

Uber

Containerizing the Beast – Hadoop NameNodes in Uber’s Infrastructure

We recently containerized Hadoop NameNodes and upgraded hardware, improving NameNode RPC queue time from ~200 to ~20ms – A 10x improvement! ...

Uber

Speed Up Presto at Uber with Alluxio Local Cache

Uber’s interactive analytics team shares how they integrated Alluxio’s data caching into Presto, the SQL query engine powering thousands of ...

Uber

Introducing WorkflowGuard: The Workflow Governance and Observability System That Oversees over 120,000 Data Workflows

Our Data Workflow Platform team introduces WorkflowGuard: a new service to govern executions, prioritize resources, and manage life cycle fo...

Uber

Reducing Logging Cost by Two Orders of Magnitude using CLP

Uber’s Data team discusses how they used CLP to scale log ingestion, retention, and analytics for Petabytes of Spark logs, reducing log stor...

Uber

Uber Freight Carrier Metrics with Near-Real-Time Analytics

We’re simplifying the lives of trucking companies by providing a platform for carriers to browse through all available shipment opportunitie...