Analyze ORC and Parquet data in the right place for the right price with Vertica

Presented by

Mark Lyons and Steve Sarsfield, Vertica

About this talk

In a modern enterprise architecture, gaining access to data that’s stored in multiple locations is crucial to delivering analytics. It shouldn’t really matter if the data is stored in your database or some other location in ORC or Parquet formats. Performing analysis on it without having to first copy it can be a very powerful capability. Join Vertica experts Mark Lyons and Steve Sarsfield as they talk about how the external tables feature of Vertica can save you time and effort in many day-to-day analytics tasks. Whether you’re joining internal databases to external data tables for additional insight, training machine learning models, or simply tiering less critical data analysis off to a less expensive storage location, external tables can speed you through this task. In this webinar, you’ll also learn how external tables are a fantastic way to keep costs low yet maintain easy access to data of almost any age and any importance.

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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.