Requirements of Modern Analytic Databases

Presented by

Jim Harris, Vertica; David Menninger, Ventana Research

About this talk

Organizations have a wide variety of analytics needs ranging from dashboards and visualization to artificial intelligence and machine learning (AI/ML). The faster an organization can perform analytics, the more efficient and effective an organization’s business operations will be. One way to improve performance is with in-database analytics, the process of executing analytics directly within the database rather than extracting data to a separate system. This results in less data movement and less maintenance of data pipelines. In-database analytics accelerate time to insights and reduce maintenance and data governance issues, even as increasing volumes of data require compute and storage scalability without compromising efficiency. This webcast features research presented by David Menninger, SVP and Research Director of Data and Analytics Research at Ventana Research.

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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.