How Vertica & Scality deliver cloud analytics on-premises at massive scale

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Presented by

Maziar Tamadon, Scality, and Amit Saha, Vertica

About this talk

Under the Hood: Companies are moving analytic workloads to the cloud for operational flexibility and efficiency. Yet the risk, cost, and complexity of moving very large data volumes to the cloud is high, and analytics leaders are often forced to weigh their need for cloud innovation with the myriad challenges associated with cloud migration. Join Maziar Tamadon, Director of Product and Solution Marketing at Scality, and Amit Saha, Principal Product Manager for Big Data Analytics & Cloud for Vertica, to share how Vertica in Eon Mode for Scality RING S3 object storage on HPE Apollo 4000 platform gives customers a cost-optimized on-premises architecture for modern analytic workloads at massive scale and speed. The joint solution combines the power of Vertica’s analytic capabilities with the elasticity of Scality S3 object storage so customers can deliver advanced and predictive analytic outcomes based on the entirety of their data.

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The Vertica Unified Analytics Platform is built to handle the most demanding analytic use cases and is trusted by thousands of leading data-driven enterprises around the world, including Etsy, Bank of America, Uber, and more. Based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and built-in machine learning capability, Vertica enables data analytics teams to easily apply these powerful functions to large and demanding analytical workloads. Vertica unites the major public clouds and on-premises data centers, as needed, and integrates data in cloud object storage and HDFS without forcing any data movement. Available as a SaaS option, or as a customer-managed system, Vertica helps teams combine growing data siloes for a more complete view of available data. Vertica features separation of compute and storage, so teams can spin up storage and compute resources as needed, then spin down afterwards to reduce costs.