At PayPal & any other data driven enterprise – data users & applications work with a variety of data sources (RDBMS, NoSQL, Messaging, Documents, Big Data, Time Series Databases), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive) to process petabytes of data. Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM).
To solve this problem and to make product development more effective, PayPal Data Platforms developed “Gimel”, an open source, unified analytics data platform which provides access to any storage through a single unified data API and SQL, which are powered by a centralized data catalog.
At PayPal & any other data driven enterprise – data users & applications work with a variety of data sources (RDBMS, NoSQL, Messaging, Documents, Big Data, Time Series Databases), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive) to process petabytes of data. Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM).
To solve this problem and to make product development more effective, PayPal Data Platforms developed “Gimel”, an open source, unified analytics data platform which provides access to any storage through a single unified data API and SQL, which are powered by a centralized data catalog.
Video:
Presentation Slides:
At PayPal & any other data driven enterprise – data users & applications work with a variety of data sources (RDBMS, NoSQL, Messaging, Documents, Big Data, Time Series Databases), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive) to process petabytes of data. Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM).
To solve this problem and to make product development more effective, PayPal Data Platforms developed “Gimel”, an open source, unified analytics data platform which provides access to any storage through a single unified data API and SQL, which are powered by a centralized data catalog.
Videos:
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Videos
Deepseek’s recent announcement of the Fire-flyer File System (3FS) has sparked excitement across the AI infra community, promising a breakthrough in how machine learning models access and process data.
In this webinar, an expert in distributed systems and AI infrastructure will take you inside Deepseek 3FS, the purpose-built file system for handling large files and high-bandwidth workloads. We’ll break down how 3FS optimizes data access and speeds up AI workloads as well as the design tradeoffs made to maximize throughput for AI workloads.
This webinar you’ll learn about how 3FS works under the hood, including:
✅ The system architecture
✅ Core software components
✅ Read/write flows
✅ Data distribution/placement algorithms
✅ Cluster/node management and disaster recovery
Whether you’re an AI researcher, ML engineer, or infrastructure architect, this deep dive will give you the technical insights you need to determine if 3FS is the right solution for you.