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 premises, in the Cloud, and on Hadoop
What's wrong with this picture? – SQL ?? – Real-time Analytics ??? – Real-time, continuous load ? – Real-time, very short response time ? – Big Data ????
Vertica – Does it scale ??? select GET_COMPLIANCE_STATUS();
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
Vertica – Is it really fast ? – Trillion Row Qlik-on-Vertica Dashboard – https://www.youtube.com/watch?v=ZnMDeg8V2sg
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
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.
Vertica @ Nimble Storage 10
Changing the game with the Internet of (Powerful) Things InfoSight
Nimble Storage – Some metrics – >7,500 customers – millions of virtual objects under continuous monitoring >250 billion sensor values – collected per day >2 billion log events >100 million configuration variables – 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
More on Vertica by Nimble Storage – https://my.vertica.com/wp-content/uploads/2016/09/B10823_10823_Presentation_2.pdf – From Vertica Big Data Conference 2016 : https://my.vertica.com/big-data-conference-2016/
Vertica @ Criteo 14
The analytics stack at Criteo Tableau and ROLAP Cube for Structured Data Access Vizatra for speed Hive and Vertica for Data Warehousing Cascading, Scalding and Hive for Data Transformation Hadoop for Primary Storage and MapReduce
More on Vizatra+Vertica by Criteo – SBTB FinagleCon 2015: Justin Coffey, Presenting Vizatra – YouTube – https://www.youtube.com/watch?v=uXmEhSFzNLs
More on Vertica
Vertica analytics platform Scalable Standard Costs Fast Boost performance by Handles huge workloads No need to learn new Significantly lower cost 500% or more at high speeds languages or add complexity over legacy platforms 18
About Vertica Massively Parallel Processing Client Network – Shared Nothing Private Data Network – Elastic scale-out architecture – Built-in high availability Node 1 Node 2 Node 3 2 x 12 Cores 2 x 12 Cores 2 x 12 Cores – Commodity Hardware 128+GB RAM 128+GB RAM 128+GB RAM – Easy setup and administration 20 TB 20 TB 20 TB – And more …
Core Vertica Technology Built for performance and scale 20
my.vertica.com – Download Vertica Community Edition on my.vertica.com – Up to 1 TB and 3 nodes 21
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