At Nutanix, our customers are increasingly looking to move to a hybrid cloud or multicloud world and are looking to leverage the latest technologies both on- and off-premises to gain the highest levels of efficiency and flexibility. With the Nutanix Objects™ release v.3.3, we’re extending our capabilities to include a number of new features that allow customers to fully leverage cloud-native technologies while providing the flexibility of a multicloud deployment.
Flexibility For Public Cloud Storage Endpoints
This starts with native tiering to the Microsoft Azure® cloud from Nutanix Objects, which builds on our tiering ability to Amazon® S3 storage. Objects Cloud Tiering allows customers to configure tiering with bucket-level granularity and set up flexible lifecycle policies to determine when data is tiered to the Azure endpoint. These can be based simply on data age, or can include other rules, including data type, name, specific user-associated tags and more. These rules allow our customers to create policies that, for example, might tier videos almost immediately while images and other data types are tiered later. Tiering to Azure provides customers with the flexibility to select their public cloud storage provider (S3 or Azure compatible) in order to realize the cost benefits associated with long-term archiving of data in the public cloud. As tiering is configured on a per-bucket basis, both S3 and Azure endpoints can be configured on the same objects.
Improved Time-To-Value For Analytics Workloads
When we look at data written by cloud-native and big data solutions, it’s common for the data stored in object stores to be semi-structured; for example data CSV files or JSON files. Objects 3.3 introduces S3 SELECT functionality that indexes the data stored inside objects. Nutanix Objects then provides a SQL-like interface that allows the applications - including many analytics and big data solutions - to query and read only the relevant data, rather than the entire underlying object, shifting compute resources closer to the data. The result is faster access to data, reduced network bandwidth and reduced index server workload. This is ideal for analytics workloads that store structured data inside objects and query that data regularly but can be leveraged by any application to gain all the same benefits.
Ingest Large Volumes Of Data Efficiently
Many big data solutions ingest large volumes of data on a daily basis. That data is either processed inline or as part of a batch job. Ingesting large amounts of data requires high write performance, which requires RF2 to achieve the write bandwidth, but RF2 is expensive in terms of capacity. Objects 3.3 improves capacity efficiency during ingestion by moving data from high-performance RF2 to high-efficiency EC-X 7x more quickly, reducing the capacity required to ingest large volumes of data.
Learn more about Nutanix Objects at https://www.nutanix.com/products/objects
© 2021 Nutanix, Inc. All rights reserved. Nutanix, the Nutanix logo and all Nutanix product, feature and service names mentioned herein are registered trademarks or trademarks of Nutanix, Inc. in the United States and other countries. Other brand names mentioned herein are for identification purposes only and may be the trademarks of their respective holder(s). This post may contain links to external websites that are not part of Nutanix.com. Nutanix does not control these sites and disclaims all responsibility for the content or accuracy of any external site. Our decision to link to an external site should not be considered an endorsement of any content on such a site. Certain information contained in this post may relate to or be based on studies, publications, surveys and other data obtained from third-party sources and our own internal estimates and research. While we believe these third-party studies, publications, surveys and other data are reliable as of the date of this post, they have not independently verified, and we make no representation as to the adequacy, fairness, accuracy, or completeness of any information obtained from third-party sources.
This post may contain express and implied forward-looking statements, which are not historical facts and are instead based on our current expectations, estimates and beliefs. The accuracy of such statements involves risks and uncertainties and depends upon future events, including those that may be beyond our control, and actual results may differ materially and adversely from those anticipated or implied by such statements. Any forward-looking statements included herein speak only as of the date hereof and, except as required by law, we assume no obligation to update or otherwise revise any of such forward-looking statements to reflect subsequent events or circumstances.