APAC Catch Media & Cloud Repatriation–Why Move Analytics Workloads OFF the Cloud

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Presented by

Boaz Ben-Yaacov, CTO, Catch Media, and Paige Roberts, Open Source Relations Manager, Vertica

About this talk

There’s a concept that all analytics workloads, or any other workload for that matter, are headed to the cloud. If your workload isn’t in the cloud yet, it will be soon. Contrary to this widely held belief, some analytic workloads are going in the opposite direction. A recent IDC survey showed that many CIOs are pulling their workloads back out of the cloud to on-premises, and dubbed it “Cloud Repatriation.” With Vertica, Catch Media has no problem working either on-prem or on the cloud, but there are some key reasons they’re pulling workloads back from the Cloud. In this webinar from our Data Disruptor series, come learn why Catch Media is swimming upstream: 1. How the easy templates in the cloud can be a Pandora’s [black] Box 2. Difference in cost and its predictability 3. DevOps teams need to maintain control for troubleshooting and optimization, cloud or not, but on-prem may be easier 4. Server utilization, and many more …

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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.