Thought Leadership Webcast: Probing the Concept of Cloud-Native Databases

Presented by

Steve Sarsfield and Jeff Healey, Vertica

About this talk

Today, there’s a lot of buzz about cloud-native databases and how essential their capabilities are when moving data analytics workloads to the cloud. On-premises-only databases are rare, while cloud-only databases are common. What does “cloud-native” actually mean? Does it simply mean “cloud-only?” What’s the real difference, and is it important? In your planning to move analytical workloads to the cloud, are there specific reasons to prefer a database that can exist both on clouds and on-premises? Or, are there specific reasons to prefer a database built only for the cloud? In this webinar from our Thought Leadership Series, we’ll deconstruct the propaganda around cloud-native databases and discuss their place in your design of analytical systems. We’ll discuss: • Deployment – when you need simplified deployment or choosing optimal clusters for your specific workloads • Compute and Storage Separation – the practical advantages, when it makes sense, and where it can be deployed • Autoscaling – Advantages, disadvantages and alternatives • Ease of Admin – what the trade-off is for easy administration, how that can be balanced • Analytics – the depth of analytics offered on cloud platforms, and exploring options to get the analytic depth you need including machine learning • Data Governance – circumstances where business users control costs, handle compliance and data replication versus IT management of those costs Attend this webinar to learn how to deal with concerns like GDPR and other privacy regulations, vendor lock-in, pricing predictability, and more in your analytical database design.

Related topics:

More from this channel

Upcoming talks (1)
On-demand talks (164)
Subscribers (37347)
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.