Mar 12th, 2018
Lenovo is an Alluxio customer with a common problem and use case in the world of data analytics. They have petabytes of data in multiple data centers in different geographic locations. Analyzing it requires an ETL process to get all of the data in the right place. This is both slow, because data has to be transferred across the network, and costly because multiple copies of the data need to be stored. Freshness and quality of the data can also suffer as the data is also potentially out of date and incomplete because regulatory issues prevent certain data from being transferred.
Feb 28th, 2018
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 further detail, you can access it here: Structured Big Data Federation Using Alluxio.
Feb 5th, 2018
The primary appeal of a coupled compute-storage architecture, an architecture where the computation is happening on the machines where the data resides, is the performance possible by bringing the compute engine to the data it requires; however, the costs of maintaining such tight-knit architectures are gradually overtaking the performance benefits. Especially with the popularity of cloud resources, being able to independently scale compute and storage results in large cost savings and cheaper maintenance. This post explores the benefits Alluxio brings in these environments...
Feb 4th, 2018
Processing and storing data in the cloud, such as Amazon S3, Microsoft Azure Blob Storage, or Google Cloud Storage is a growing trend. The global availability and cost effectiveness of these public cloud storage services make them the preferred storage for data. However, running data processing pipelines while sharing data via cloud storage can be expensive in terms of increased network traffic, and slower data sharing and job completion times. Recently, organizations have been deploying Alluxio to support various cloud-based pipelines, to improve performance and reduce costs.
Feb 2nd, 2018
We are excited to announce the release of Alluxio Enterprise Edition (AEE) and Community Edition (ACE) v1.7.0. This release brings enhanced caching policies, further ecosystem integrations, and significant usability improvements. One highlight is the Alluxio FUSE API which provides users with the ability to interact with Alluxio through a local filesystem mount. Alluxio FUSE is particularly useful for integrating with deep learning frameworks such as Tensorflow. Learn more about using Alluxio for deep learning here, and stay tuned for additional articles highlighting our latest capabilities.
Jan 30th, 2018
In the age of growing datasets and increased computing power, deep learning has become a popular technique for AI. Deep learning models continue to improve their performance across a variety of domains, with access to more and more data, and the processing power to train larger neural networks. This rise of deep learning advances the state-of-the-art for AI, but also exposes some challenges for the access to data and storage systems. In this article, we further describe the storage challenges for deep learning workloads and how Alluxio can help to solve them.
Dec 1st, 2017
Alluxio enables effective data management across different storage systems through its use of transparent naming and mounting API. With Alluxio, KAP can gain a good balance between performance, cost and management effort in the Cloud.
Oct 11th, 2017
We are excited to announce Alluxio Enterprise Edition (AEE) 1.6.0 and Alluxio Community Edition (ACE) 1.6.0 releases. The AEE release brings a new embedded journal as well as enhancements in the areas of security and Fast Durable Write. In addition, both the AEE and the ACE releases bring new clients support (Amazon S3 API and Python Client), major usability improvements as well as enhanced integrations with the ecosystem.
Jul 5th, 2017
Open source Alluxio 1.5.0 has been released with a large number of new features and improvements, particularly focused on ecosystem accessibility and compatibility.
Jun 26th, 2017
We are excited to announce Alluxio Enterprise Edition (AEE) 1.5.0 and Alluxio Community Edition (ACE) 1.5.0 releases. The AEE release brings enhancements in the areas of security, multi-tenancy as well as working with multiple under-stores. In addition, both the AEE and the ACE releases bring major usability and performance improvements as well as enhanced integrations with the ecosystem.
Mar 13th, 2017
Today, we’re excited to announce our partnership with Mesosphere to enable fast on-demand analytics with Alluxio via Mesosphere’s DC/OS in one-click. This partnership is a natural extension of the synergy between Alluxio and DC/OS. Alluxio, the world's first system that unifies data at memory speed, allows enterprises to manage and analyze data stored across disparate storage systems on premise and in the cloud at memory speed. Mesosphere brings enterprises the power of cloud native technologies, with the control to run on any infrastructure - datacenter or cloud...
Feb 8th, 2017
Alluxio 1.4.0 has been released with a large number of new features and improvements. This blog highlights some stand out aspects of the release.
Nov 25th, 2016
Deep learning algorithms have traditionally been used in specific applications, most notably, computer vision, machine translation, text mining, and fraud detection. Deep learning truly shines when the model is big and trained on large-scale datasets. Meanwhile, distributed computing platforms like Spark are designed to handle big data and have been used extensively. Therefore, by having deep learning available on Spark, the application of deep learning is much broader, and now businesses can fully take advantage of deep learning capabilities using their existing Spark infrastructure.
Oct 24th, 2016
Today we’re excited to unveil our first products which enable organizations to turn data into value with unprecedented ease, flexibility, and speeds. We believe our new products will substantially advance Alluxio for both the community and our enterprise customers.
In this blog, I will share with you the challenges that we see application developers and business line owners face today when working with big data, and show how Alluxio addresses these challenges.
Oct 16th, 2016
This is an excerpt from the Accelerating Data Analytics on Ceph Object Storage with Alluxio whitepaper. In addition to the reference architecture in this blog, the whitepaper provides a detailed implementation guide to reproduce the environment
Sep 1st, 2016
Alluxio is the world's first memory-speed virtual distributed storage system that bridges applications and underlying storage systems, providing unified data access orders of magnitudes faster than existing solutions. The Hadoop Distributed File System (HDFS) is a distributed file system for storing large volumes of data. HDFS popularized the paradigm of bringing computation to data and the co-located compute and storage architecture.
Aug 27th, 2016
We are excited to announce a big data storage acceleration solution with Huawei. This solution combines Huawei’s FusionStorage with Alluxio’s memory-speed virtual distributed storage system to dramatically enhance the speed and efficiency of big data analytics for the enterprise.
Aug 25th, 2016
Organizations like Baidu and Barclays have deployed Alluxio with Spark in their architecture, and have achieved impressive benefits and gains. Recently, Qunar deployed Alluxio with Spark in production and found that Alluxio enables Spark streaming jobs to run 15x to 300x faster. In this blog, we investigate how Alluxio can make Spark more effective, and discuss various ways to use Alluxio with Spark. Alluxio helps Spark perform faster, and enables multiple Spark jobs to share the same, memory-speed data.
Aug 19th, 2016
This is an excerpt from the Accelerating On-Demand Data Analytics with Alluxio whitepaper, which includes a detailed implementation guide in addition to this high level overview.
Jun 21st, 2016
Alluxio 1.1 release includes many great features and improvements from the community. Alluxio would not be what it is today without the growing open source community, and we would like to thank everyone involved in this project. With the Alluxio 1.1 release, the community has continued to grow at a rapid pace, to reach over 250 contributors to Alluxio – nearly 3x growth over the last year!
May 30th, 2016
Alluxio, formerly Tachyon, began as a research project at UC Berkeley’s AMPLab in 2012. This year we announced the 1.0 release of Alluxio, the world’s first memory speed virtual distributed storage system, which unifies data access and bridges computation frameworks and underlying storage systems. We have been working closely with the Alluxio community on realizing the vision of Alluxio to become the de facto storage unification layer for big data and other scale out application environments.
Apr 5th, 2016
Alluxio, formerly Tachyon, provides Spark with a reliable data sharing layer, enabling Spark to excel at performing application logic while Alluxio handles storage. For example, global financial powerhouse Barclays made the impossible possible by using Alluxio with Spark in their architecture. Technology giant Baidu analyzes petabytes of data and realized 30x performance improvements with a new architecture centered around Alluxio and Spark.
Feb 14th, 2016
Alluxio, formerly Tachyon, began as a research project when I was a Ph.D. student at UC Berkeley’s AMPLab in 2012. At the time, Spark and Mesos were taking off. We saw what Spark and Mesos could do for compute and resource management respectively, while the storage piece of this story was missing.