level up – how to achieve hadoop acceleration

Grab some coffee and

enjoy the

pre-show

banter

before the top of the

hour!

The Briefing Room

Level Up – How to Achieve Hadoop Acceleration

Twitter Tag: #briefr

The Briefing Room

Welcome

Host: Eric Kavanagh

[email protected] @eric_kavanagh


The Briefing Room

!   Reveal the essential characteristics of enterprise software, good and bad

!   Provide a forum for detailed analysis of today’s innovative technologies

!  Give vendors a chance to explain their product to savvy analysts

!   Allow audience members to pose serious questions... and get answers!

Mission


The Briefing Room

Topics

2014 Editorial Calendar at www.insideanalysis.com/webcasts/the-briefing-room

This Month: BIG DATA ECOSYSTEM

September: INTEGRATION & DATA FLOW

October: ANALYTIC PLATFORMS


The Briefing Room

Executive Summary

u  Yes, you still need to PLAN

u  File formats and partitioning MATTER

u  Pay attention to TCO

u  Be willing to fail fast, OFTEN

LOOK BEFORE YOU LEAP INTO HADOOP!


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Analyst: Robin Bloor

Robin Bloor is Chief Analyst at The Bloor Group

[email protected] @robinbloor


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HP Vertica

!  HP Vertica offers a range of enterprise software and database management solutions

!  The column-oriented Vertica Analytics Platform leverages a standard SQL interface that now integrates with Hadoop

!  HP’s big data initiative includes HAVEn (Hadoop, Autonomy IDOL, Vertica, Enterprise Security and n Apps), a platform designed to analyze and manage petabytes of data


The Briefing Room

Guest: Walter Maguire

Walter Maguire has twenty-seven years of experience in analytics and data technologies. He practiced data science before it had a name, worked with big data when "big" meant a megabyte, and supported the movement which brought data management and analytic technologies from back-office to the front. In October of 2010, Walt became the first hire west of Denver for Vertica, makers of the Vertica Analytics software platform for real-time analytics of structured and unstructured data. Since then, he has helped build the HP Vertica customer base and team in the western USA. Now as Chief Field Technologist with HP Vertica, Walt addresses customer needs with the continuing evolution of Vertica and HAVEn, the HP Big Data strategy that links hardware, software, services, and business transformation consulting for successful execution.

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

With the HP Vertica Analytics Platform Walt Maguire, Chief Field Technologist, HP Vertica Chris Selland, VP Business Development, HP Vertica

Big Data & SQL: Hadoop Convergence

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 11

Essential Requirements of an Analytics Platform


Faster answers from Big Data at a fraction of the cost of traditional data warehouses

Introducing HP Vertica Dragline

Store all your data in any format cost-effectively across Vertica + Hadoop

Explore all your data directly in Hadoop without moving or changing it

Serve all of your data consumers without compromise from individualized queries to large complex reports

HP Vertica


Cost-Optimized Storage - ILM

Tier-off older data

Value Discovery

Interactive Data Frequently queried Vertica data cache

Batch Data

Archive Data

Serve Convert data to Vertica storage format

Explore Any format

Store Any format Location Format

Cold

Cool

Hot

Dark Data


The Richest, Most Open SQL on Hadoop

Challenge: Extracting data from Hadoop requires complex and brittle ETL processes

Solution: Hadoop Navigation and Analytics Benefits: •  Navigate Hadoop data using its native catalog •  Quickly and easily load native data types from Hadoop to

Vertica •  Avoid creating and maintaining time-consuming schemas •  Use the full power of HP Vertica SQL and analytics •  Choose your own Hadoop distribution


The Richest, Most Open SQL on Hadoop

Challenge: Extracting Data from Hadoop requires complex and brittle ETL processes

Solution: Hadoop Navigation and Analytics Benefits: •  Navigate Hadoop data using its native catalog •  Quickly and easily load native data types from Hadoop to

Vertica •  Avoid creating and maintaining time-consuming schemas •  Use the full power of HP Vertica SQL and Analytics •  Choose your own Hadoop distribution


16

HP Vertica Flex Zone Avoid creating and maintaining time-consuming schemas

on semi-structured data Faster SQL querying

semi-structured data loading Auto-schematization

for JSON and delimited data Flexible parsers

for blazing-fast performance One-step schema

Load, manage, and explore semi-structured data


Exploring Data with FlexZone

Create Flex Table (with or without any columns)

Load Flex Tables (using parsers

data format independent)

Explore Data Using map functions

Materialize Flex Table (Compute keys / Build Views Materialize flextable columns)

Manage Flex Table (Alter, Config, etc)


Monitor Customer Experience by Joining Machine Logs to Tweets

Create Flex Table with Machine Log

Data

Explore Data with Map Functions

Create Flex Table with Twitter Data

Sentiment Score Tweets with Pulse

Join Machine Log Data to Tweets

with Time Series Event Join

Associate customer

sentiment with application

response times


Ecosystem

HP Vertica


•  Integrated SQL-on-Hadoop solution with MapR live as of May 2014

•  Reseller relationship with Hortonworks announced July 2014

•  Significant joint customer base with Cloudera

Hadoop Partnership Momentum and Milestones


HP Vertica Marketplace


Thank you!

Walt Maguire Chief Field Technologist, HP Vertica [email protected] Chris Selland VP Business Development, HP Vertica [email protected]


The Briefing Room

Perceptions & Questions

Analyst: Robin Bloor

Robin Bloor, PhD

THE NEXT PHASE OF HADOOP’S EVOLUTION?

Hadoop Evolution FR

OM

Serial batch workloads MapReduce Versatile data storage Key-value access only Island of processing

TO Multiple concurrent

workloads Multiple algorithms “Optimized” data storage SQL, JSON & even SPARQL access Integrated processing

The Data Warehouse: From/To

Data & Data Lifecycle Management

The Major Workload Will Be Analytics

The consequences are that: 1.   DATA ACCESS will need to be more

versatile

2.   WORKLOAD MANAGEMENT will need to be more versatile

3.   Hadoop will need to “shake hands” with ONE OR MORE database engines

u  In general what is the DBA overhead for the combination of Hadoop, Flex Zone and Vertica?

u  How does data lifecycle management work in practice?

u  Analytics can be done on Hadoop, in Flex Zone and in Vertica. How are these choices normally handled in a Flex Zone/Vertica environment?

u  What analytics components can be used with Flex Zone and Vertica?

u  What do you see as the sweet spot for this architecture (by sector & company size)? Where might it be overkill?

u  In respect to scale, what is your largest implementation of Hadoop/Flex Zone/Vertica by data volume?

u  Who do you see as closest in direct competition?


The Briefing Room


The Briefing Room

Upcoming Topics

www.insideanalysis.com

2014 Editorial Calendar at www.insideanalysis.com/webcasts/the-briefing-room

This Month: BIG DATA ECOSYSTEM

September: INTEGRATION & DATA FLOW

October: ANALYTIC PLATFORMS


The Briefing Room

THANK YOU for your

ATTENTION!

Opening slide image courtesy of Wikimedia Commons

level up – how to achieve hadoop acceleration

Technology

hp vertica copyright

continuingevolution

big data sql

data technologies

big data ecosystemseptember

data consumers

managepetabytes of data

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