HP Vertica basics

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The cluster-based, column-oriented, Vertica Analytics Platform is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses and other query-intensive applications.Support for standard programming interfaces ODBC, JDBC, and ADO.NET.The Vertica Analytic Database runs on cluster of Linux-based commodity servers. It is also available as a hosted DBMS provisioned by and running on the Amazon Elastic Compute Cloud. The product integrates with Hadoop

LivePersonDev is happy to host this meetup with Zvika Gutkin, an Oracle and Vertica expert DBA in LivePerson, and specialist in BI and Big Data. At LivePerson, we handle enormous amounts of data. We use Vertica to analyse this data in real time. In this lecture Zvika will cover the following: 1. Present the architecture of Vertica 2. Compare row store to column store 3. Explain how Vertica achieve Fast query time 4. Show few use cases . 5. Explains what does Liveperson do with Vertica? Why we chose Vertica? 6. Talk about why we Love Vertica and Why we hate it . 7. Is Vertica SQL DB or NoSQL? Is vertica Consistent or Eventually consistent? 8. How Vertica differ from other SQL and noSQL technologies?

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In this lecture I will cover the following: 1. Present the architecture of Vertica 2. Compare row store to column store 3. Explain how Vertica achieve Fast query time 4. Show few use cases . 5. Explains what does Liveperson do with Vertica? Why we chose Vertica? 6. Talk about why we Love Vertica and Why we hate it . 7. Is Vertica SQL DB or NoSQL? Is vertica Consistent or Eventually consistent? 8. How Vertica differ from other SQL and noSQL technologies?

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"R is the most popular language in the data-science community with 2+ million users and 6000+ R packages. R’s adoption evolved along with its easy-to-use statistical language, graphics, packages, tools and active community. In this session we will introduce Distributed R, a new open-source technology that solves the scalability and performance limitations of vanilla R. Since R is single-threaded and does not scale to accommodate large datasets, Distributed R addresses many of R’s limitations. Distributed R efficiently shares sparse structured data, leverages multi-cores, and dynamically partitions data to mitigate load imbalance. In this talk, we will show the promise of this approach by demonstrating how important machine learning and graph algorithms can be expressed in a single framework and are substantially faster under Distributed R. Additionally, we will show how Distributed R complements Vertica, a state-of-the-art columnar analytics database, to deliver a full-cycle, fully integrated, data “prep-analyze-deploy” solution."

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