Enterprises are adopting big data technologies to analyze and derive insight from their growing volumes of structured and unstructured data. A familiar problem is the requirement to analyze data from multiple independent storage silos concurrently. In order to consolidate the data, large enterprises typically use custom solutions or build a data lake. These approaches present additional challenges and can be costly and time consuming. Alluxio helps organizations handle their big data by providing a unified view of all of the data in your enterprise – on premise, in the cloud, or hybrid. Applications access data using a standard interface to a global virtual namespace. Alluxio also employs a memory-centric architecture to enable data access at memory speed. With the combined unification and performance benefits, Alluxio can effectively provide big data federation for organizations by acting as a virtual data lake. We just published a whitepaper that goes into more detail on this common use case, you can access it here:Structured Big Data Federation Using Alluxio.
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Coupang, a Fortune 200 technology company, manages a multi-cluster GPU architecture for their AI/ML model training. This architecture introduced significant challenges, including:
- Time-consuming data preparation and data copy/movement
- Difficulty utilizing GPU resources efficiently
- High and growing storage costs
- Excessive operational overhead maintaining storage for localized data silos
To resolve these challenges, Coupang’s AI platform team implemented a distributed caching system that automatically retrieves training data from their central data lake, improves data loading performance, unifies access paths for model developers, automates data lifecycle management, and extends easily across Kubernetes environments. The new distributed caching architecture has improved model training speed, reduced storage costs, increased GPU utilization across clusters, lowered operational overhead, enabled training workload portability, and delivered 40% better I/O performance compared to parallel file systems.
Suresh Kumar Veerapathiran and Anudeep Kumar, engineering leaders at Uptycs, recently shared their experience of evolving their data platform and analytics architecture to power analytics through a generative AI interface. In their post on Medium titled Cache Me If You Can: Building a Lightning-Fast Analytics Cache at Terabyte Scale, Veerapathiran and Kumar provide detailed insights into the challenges they faced (and how they solved them) scaling their analytics solution that collects and reports on terabytes of telemetry data per day as part of Uptycs Cloud-Native Application Protection Platform (CNAPP) solutions.
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