Alluxio Announces Advanced Cloud Service Integrations on Amazon AWS and Google CloudNovember 7, 2019
Alluxio, the developer of open source cloud data orchestration software, today announced at the first Data Orchestration Summit at the Computer History Museum, the availability of a range of cloud offerings and integrations with the latest Alluxio version 2.1, as well as strengthening of partnerships with Amazon AWS and Google Cloud. Alluxio advanced to the Select Technology Partner status and joined the Google Cloud Partner Program as a technology partner giving AWS and Google Cloud customers the ability to accelerate analytical workloads and orchestrate data across clouds.
As users and enterprises demand more added capacity, particularly for their data-driven workloads, they are also in parallel moving towards all cloud or hybrid cloud environments. This requires cloud data orchestration to accelerate and synchronize data across different environments, and as a result users are migrating to cloud data analytic services like Amazon’s EMR and Google Cloud’s Dataproc that reduce hardware spend, eliminate the need to overbuy capacity, and provide business agility. Alluxio can now be seamlessly integrated with both of these leading cloud analytical services to speed up analytical jobs on a range of computational frameworks like Apache Spark, Presto, Hive, TensorFlow and others.
“The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. Analytical cloud services embrace this architecture and provide the needed agility to get insights from data faster,” said Steven Mih, CEO, Alluxio. “We are thrilled to deepen our partnership with Amazon Web Services and Google Cloud to further enable users on their data transformation journeys.”
New Offerings on Amazon Web Service
- Alluxio Enterprise Edition v2.1.0 is now available via the Amazon AWS Marketplace. This AMI is available via a pay as you go model as well as an annual subscription.
- Alluxio Cloud Formation Templates are available which greatly simplifies Alluxio cluster creation for users. After selecting a few configurations, a cluster of any size can be brought up in minutes.
- Tighter Alluxio integration with AWS EMR service includes options to backup state and enable stateful EMR clusters and brings up Alluxio on the EMR cluster as a part of provisioning step.
New Offering on Google Cloud
- Alluxio integration with Google Cloud Dataproc allows users to bring up Alluxio within the Google Dataproc cluster with a single command.
Google Cloud and Alluxio will hold an online tech talk walking users through the benefits of using Google Dataproc and Alluxio for analytical workloads for data in the cloud as well as for remote data. Details below.
Joint Starburst Data and Alluxio AWS Offering
Starburst Data and Alluxio jointly released a few Amazon Web Service product offerings to simplify Presto deployments in the cloud for performance sensitive workloads. With Alluxio, Presto queries running on data in AWS S3 can see a dramatic performance improvement.
- Presto with caching AWS Marketplace AMI – This AMI includes the Starburst Data Enterprise Edition and the Alluxio Enterprise Edition fully configured.
- Joint Cloud Formation Template – This template enables users to create a pre-configured Presto cluster with Alluxio co-located to provide greater data locality and concurrency to the workload.
“As more users move their SQL analytics workloads to the cloud, Alluxio adds significant benefits by not only providing dramatic performance increases but also data accessibility and data elasticity by abstracting many different data silos,” said Dipti Borkar, Vice President, Products at Alluxio. “In addition, with our latest integrations, users can leave their data on premises and burst analytical workloads to cloud services like AWS EMR and Google Dataproc or with Starburst Presto with our “zero-copy” bursting approach to solve their on-prem compute capacity challenges.”