Real Time Analytics Vertica A SQL analytic engine Built for Speed, - - PowerPoint PPT Presentation

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Sep 25, 2023 389 likes •621 views

Real Time Analytics Vertica A SQL analytic engine Built for Speed, Scale and Efficiency Supports standard SQL Provides rich Analytic functionality and is extensible Integrates well with Big Data ecosystem tools Runs on

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Real Time Analytics

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Vertica

– A SQL analytic engine – Built for Speed, Scale and Efficiency – Supports standard SQL – Provides rich Analytic functionality and is extensible – Integrates well with Big Data ecosystem tools – Runs on premises, in the Cloud, and on Hadoop

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What's wrong with this picture?

– SQL ?? – Real-time Analytics ???

– Real-time, continuous load ? – Real-time, very short response time ?

– Big Data ????

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Vertica – Does it scale ???

select GET_COMPLIANCE_STATUS();

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Vertica – Does it scale ???

(not a fake, believe me…)

select GET_COMPLIANCE_STATUS(); GET_COMPLIANCE_STATUS

Raw Data Size: 2.75PB +/- 0.30PB

License Size : 1.95PB Utilization : 141% Audit Time : 2016-09-27 23:59:29.367875+00 Compliance Status : ***** NOTICE OF LICENSE NON-COMPLIANCE ***** Continued use of this database is in violation of the current license agreement. Maximum licensed raw data size: 1.95PB Current raw data size: 2.75PB License utilization: 141% IMMEDIATE ACTION IS REQUIRED, PLEASE CONTACT VERTICA

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Vertica – Is it really fast ?

– Trillion Row Qlik-on-Vertica Dashboard – https://www.youtube.com/watch?v=ZnMDeg8V2sg

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Vertica – Is it so simple ?

– HPE Vertica and Qlik Direct Discovery: A Technical Exploration – https://community.dev.hpe.com/t5/Vertica-Knowledge-Base/HPE-Vertica-and-Qlik-Direct-Discovery-A- Technical-Exploration/ta-p/234332

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Vertica – Is it so simple ?

– No ! – HPE Vertica and Qlik Direct Discovery: A Technical Exploration – Implementation Methods

– Fact and dimension tables in-memory. Most applications are created using this approach. However, this paper does not cover the all-in-memory option because it is not suitable for big data (such as a few billion rows of fact data) and requires too much memory. – Fact and dimension tables in Direct Discovery (regular star schema). – BFFT (big flat fact table) in Direct Discovery. There are no dimension tables with BFFT. – Fact tables in Direct Discovery and dimensions in memory. – Multiple fact tables in Direct Discovery. This is not generally recommended because of complex design considerations.

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Vertica @ Nimble Storage

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Changing the game with the Internet of (Powerful) Things

InfoSight

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Nimble Storage – Some metrics

– >7,500 customers – millions of virtual objects under continuous monitoring – collected per day – Database Characteristics

– Raw Data : 550TB - Disk: 200 TB - On Nimble: 100 TB – 350K selects per day – 60K inserts/deletes per day

– Configuration

– 2 Vertica clusters – 2x8 servers – 2x8x54 cores – Nimble Storage instead of DAS >250 billion sensor values >2 billion log events >100 million configuration variables

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