Alluxio is the data orchestration platform to unify data silos across heterogeneous environments. This is the last article in a series to give you the basics of Alluxio’s architecture and solution.
By bringing Alluxio together with Spark, you can modernize your data platform in a scalable, agile, and cost-effective way. In this post, we provide an overview of the Spark + Alluxio stack. We explain the architecture, discuss real-world examples, describe deployment models, and showcase performance and cost benchmarking.
This article highlights synergy between the two widely adopted open-source projects, Alluxio and Presto, and demonstrates how together they deliver a self-serve data architecture across clouds.
Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.
This whitepaper introduces how to speed up end-to-end distributed training in the cloud using Alluxio to accelerate data access. With the help of Alluxio, loading data from cloud storage, training and caching data can be done in a transparent and distributed way as a part of the training process. This whitepaper also demonstrates how to set up and benchmark the end-to-end performance of the training process, along with a comparison of other popular approaches.
Many companies have leveraged Alluxio to level up their current Presto platform, including Facebook, TikTok, Electronic Arts, Walmart, Tencent, Comcast, and more. They have gained significant benefits with Alluxio integrated into their Presto stack.
Alluxio is the data orchestration platform to unify data silos across heterogeneous environments. The following blog will discuss the architecture combining Spark with Alluxio.
Alluxio started as a virtual distributed file system, a research project out of the AMPLab at U.C. Berkeley. Alluxio foresaw the need for agility when accessing large data stores separated from compute engines like Hadoop or Spark.
Fast forward several years and over a thousand committers later, and Alluxio has blossomed into the industry’s leading data orchestration platform for analytics and AI/ML. But as with any new type of technology, figuring out the best ways to use it depends on your data environment, computational workloads, issues, and goals.
This blog is the first in a series introducing Alluxio as the data platform to unify data silos across heterogeneous environments. The next blog will include insights from PrestoDB committer Beinan Wang to uncover the value for analytics use cases, specifically with PrestoDB as the compute engine.