hadoop as an analytic platform: why not?

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The Briefing Room

Hadoop as an Analytic Platform: Why Not?

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: ANALYTIC PLATFORMS

November: DISCOVERY & VISUALIZATION

December: INNOVATORS


The Briefing Room

Executive Summary

Ø  Don’t build CARRIAGES for highways

Ø  Focus on NEW opportunities

Ø  SLOWLY ween off old systems

A NEW ERA of Architecture


The Briefing Room

Analyst: William McKnight

William is President of McKnight Consulting Group. His clients have included 17 of the Global 2000. Many clients have gone public with their success story. His team's implementations have won multiple Best Practices awards. William is an Entrepreneur of the Year Finalist, a frequent best practices judge and an expert witness. He has hundreds of articles and dozens of white papers in publication. William has also given numerous keynote presentations worldwide at major conferences and has given hundreds of public seminars and webinars. William’s experience includes taking his company to placement on the Inc. 500 and the Dallas 100 to seller of a multi-million dollar consulting firm. He is a passionate communicator and motivator, and a former IT VP of a Fortune 50 company.


The Briefing Room

Actian

! Actian is a database and software development company

!   The Actian Analytics Platform connects to data and Big Data sources to perform actionable and advanced analytics

! Actian recently released Hadoop SQL Edition, a component that enables SQL access on data stored in Hadoop


The Briefing Room

Guest: Jim Hare

Jim Hare is Senior Director of Product Marketing for the Actian Analytics Platform, helping organizations transform big data into business value. Prior to Actian, he was Director of Marketing at IBM responsible for go-to-market strategy and messaging for the big data platform. Prior to joining IBM in 2008, Jim was vice president of product marketing and business development at Celequest, a California-based operational business intelligence vendor, which was acquired by Cognos in 2007. He has over 16 years of deep experience in enterprise software, business

intelligence, business process management, business activity monitoring, big data, and automated software testing & monitoring. Jim holds a MS in Systems Management from the University of Southern California, and an undergraduate degree from the University of Colorado at Boulder.

Confiden'al © 2014 Ac'an Corpora'on 10

Hadoop as an Analy'c Pla8orm: Why Not? Jim Hare 14 October 2014


$140M Revenues + Profitable

10,000+ Customers

Global Presence: 8 world-wide offices, 7x 24 multinational support model

11 “Actian is now very powerfully positioned in the big data and analytics markets.” Robin Bloor

Ac'an is Delivering Transforma'onal Value

“Actian has assembled all of the next generation IPs into a single analytics platform, allowing users a level of flexibility in data interaction that competitors have not been able to match.” siliconANGLE


Emergence of Hadoop as the Data Reservoir

Low Cost Storage for New Data & Offload


Ø Scalable -‐ store large data sets across low cost servers

Ø Cost effec.ve – 1/10th the cost of tradi'onal data storage

Ø Flexible -‐ quickly and easily land any data in raw format

Ø Fast Access –'maps' data wherever it is located on a cluster

Ø Resilient -‐ data is replicated to other nodes in the cluster

Benefits of Hadoop


Hadoop Deployment Survey Results

Only 22% of Hadoop projects are in production today!

14

8%

14%

10%

41%

16%

11% Exploring and educa.ng

41%

16% 8%

14%

10%

11%

Conduc.ng POC

Developing First Solu.on

Pilot First Solu.on

First Solu.on

Deployed

Suppor.ng Mul.ple Analy.cs

Source: SandHill Group Research, “How do you Hadoop? A Survey of Big Data Practitioners”, May 2014


Because It isn’t Easy to Analyze Hadoop Data

Batch performance is

Slow Lengthy Time to

Discover Insights Expensive Skills

Lack of Data

Access & Security

Data preparation is time-consuming

Analytics Complexity


Organiza'ons are Replica'ng Hadoop Data to Overcome Analy'c Challenges

Rela'onal Data Store

OLTP, ERP, CRM

Unstructured docs, emails

Server logs

Social/Web data

Sensor, machine data

Geoloca'on

Clickstream

Discovery

Analy'cs

Predic've BI

Hadoop

•  Duplicate storage & infrastructure costs •  More IT resources to manage •  Network bandwidth usage •  Less accuracy from data Sampling •  Slower ?me to analysis results


Transforma.onal Value Data Explosion

? Customer Delight

Competitive Advantage

World-Class Risk Management

Disruptive New Business Models

Ac6an Transforms Hadoop from a Data Reservoir into a High Performance Analy6cs PlaCorm

Discovery Analy.cs

Time-‐Sensi.ve Analy.cs

Crea'ng Transforma'onal Value from Hadoop Data

!   Highest performing, most industrialized Hadoop analy'cs pla8orm

! Only end-‐to-‐end analy.c processing na'vely in Hadoop

! Most consumable, accessible, manageable Hadoop analy'cs

Actian Analytics PlatformTM

Analyze

Act

Connect

Hadoop


Elastic and Secure

•  Schedule •  Transform •  Validate •  Aggregate •  Reformat •  Join •  Orchestrate

ENERPRISE Data

SOCIAL Data

MACHINE Data

CLOUD Data

LEGACY Data

DEVICE Data

Connect to Any Data Anywhere

200+ Connectors

Embeddable High Throughput

Engine

Drag and Drop Workflow Designs

Capture Data Feeds in Batch or Real-Time

Expandable Plugin

Framework

High Volume Parallel Data Processing

7 Ingredients Added to Hadoop to Unlock Value 1.  High speed integra'on to on-‐board data from any data

source and any type


1.  High speed integra'on to on-‐board data from any data source and any type

2.  Visual Framework for connec'ng, blending, & enriching data, data science discovery, building and tes'ng predic've models

7 Ingredients Add to Hadoop to Unlock Value

Connect Blend & Enrich Discover Build & Test Models

Coding



2.  Visual Framework: connec'ng, blending, & enriching data, data science discovery, building and tes'ng predic've models

3.  1500 KNIME Operators + R analy'cs running in parallel on HDFS + Hadoop = The Open Source Trifecta

7 Ingredients Added to Hadoop to Unlock Value

Gartner Magic Quadrant for Advanced Analytics Platforms Source: Gartner (February 2014)


Complete End-‐to-‐End Analy'cs on Hadoop

Source: 2013 Rexer Analy'cs Survey





4.  High-‐Performance, YARN-‐based data processing engine running on HDFS


Actian DataPrep

LEADER On-Node Processing

Read Write Prepare Analyze Read Write Analyze

Optimizer


High Performance, Parallelized Processing on HDFS Without Any Programming

Ac.an Analy.cs PlaPorm

Hadoop – Leader Node

Optimized, On-HDFS Processing

Query Pipelining CPU Pipelining

Reuse and share all components from

operators to workflows

Optimize

Choose from five sets of operators: Connections

Transformation Data Quality

Analytics Data Science

Automatically detect resources, plan

optimal utilization, and parallelize all

workloads on Hadoop

Use dual pipeline parallelism to

accelerate performance 30X

Run fully optimized processing directly on the Hadoop node via

YARN

Take processing to where the data lives,

runs natively on Hortonworks

Visual Framework

Manage the entire analytic process in a visual framework with no coding required.

≠ ☼ ≡ ∞ ∆ ∑ √ ≈ ∑ = ? # ~ ‰






5.  High-‐Performance, vector processing engine as the pajern for SQL on Hadoop

7 Ingredients Added to Hadoop to Create Value


Vector-‐based SQL Processing Na'vely on HDFS

HADOOP

YARN

HDFS

Datanode HDFS

Datanode HDFS

Datanode HDFS

Datanode HDFS

X100 X100 X100

Visual Data & Analytics Workbench

Read Load

Ac'an Vector Blend & Enrich

Data Science & Analy'cs

Datanode HDFS

X100

Namenode High Performance, Industrialized SQL

Database

High Performance, Parallelized Data

Flow Engine

SQL

Standards - ANSI SQL 92 plus advanced analytics Optimized - mature, proven planner and optimizer Secure – native DBMS security Reliable - full ACID-compliance Manageable – YARN certified Performance - 30X faster than Impala Scalable – unlimited expansion as Hadoop cluster grows Native – runs natively on top of HDFS via YARN

Industrialized







6.  Extreme-‐Performance, super-‐low latency, massively parallel analy'cs engine



Libraries of Analytics

Mas

sive

ly P

aral

lel

Inte

grat

ion

Hadoop

Sophisticated, Low Latency Analytics in

Database

Connections for Any Data

Actian Analytics PlatformTM

Enterprise Data

Machine Data

Social Data

Business Processes

Users

Machines

Applications

Data Warehouse

Real-Tim

e A

nalytic Services

Visual Framework for Data and Analytic Workflows

SaaS Data

Ac'an Analy'cs Pla8orm: Next Genera'on Big Data Analy'cs

Amazon Redshift

High Performance Data Science Natively on

Hadoop







6.  Extreme-‐Performance, super-‐low latency, massively parallel analy'cs engine

7.  Blueprints to accelerate analy'cs applica'on development and value crea'on



Big Data 2.0 Media Mix Modeling Blueprint

IMPACT FORECAST ANALYSIS

MARKETING IMPACT

ANALYSIS

CRMdb

All Relevant Account Info and Demographics

CONNECT

BUILD CUSTOMER

PROFILE

EDWdb

All Relevant Sales Histories

ANALYZE ACT

MAXIMIZE REVENUE

FROM MARKETING

SENTIMENT AND

CONTENT ANALYSIS

AGGREGATE SALES DATA

Hadoop Logs

Detailed ePOS Receipts

SKU LEVEL SALES DATA

BY GEO

JOIN DERIVE

AGGREGATE PREPARE

EDWdb

Marketing Vehicle Details

CAPTURE MARKETING VEHICLES

MARKETING MIX SALES

CONTRIBUTION YEARLY CHANGE

REPORT

SALES VOLUME,

EFFECTIVENESS, EFFICIENCY

AND ROI REPORT

NEW MEDIA MIX

OPTIMIZATION

MINIMIZE MARKETING SPEND TO REVENUE

RATIO

Hadoop Text Files

Campaign Response Notes

PREPARE FOR TEXT

ANALYTICS

CUSTOMER MATCH WITH

CAMPAIGNS

VEHICLE RESULTS AT GEO,

STORE, SKU AND

CUSTOMER LEVEL


1)  Stay tuned for several exci'ng announcements on 16 October 2014 at the Strata Conference in NYC!

2)  Visit Ac'an at Booth 225 for a demo of Ac'an Analy'cs Pla8orm -‐ the Highest Performing Analy'cs & SQL in Hadoop

3)  Download and try it out yourself: bigdata.ac'an.com/sql-‐in-‐Hadoop

Learn more about the Ac'an Analy'cs Pla8orm – Hadoop SQL Edi'on


www.ac'an.com facebook.com/ac'ancorp @ac'ancorp

Thank You


The Briefing Room

Perceptions & Questions

Analyst: William McKnight

ANALYTICS: A BUSINESS IMPERATIVE

Formed from SUMMARIES of data

Tied to Business Actions

Continual Re-evaluation

i.e., Customer Segmentation and Profit

Adding Big Data!

ANALYTICS EXAMPLES

Number of customers in each customer state (optionally by product or multiple products)

Average balance of customers by geo Average start date in each customer lifetime value decile by geo and device

New number of customers in each state Propensity to churn by age band and device

Cost of acquisition by age and gender Average session duration by cost of acquisition

Session duration differences between first and tenth session Network with highest up time last month

Number of calls per session Best performing ad network by day part in a geo, age band and device

And on and on and on and on….

ANALYTICS ACTION

35

SMARTER MARKETING

Spend + Media Arbitrage

Opportunities + Incremental Direct Marketing Spend

Improvement:

Map Media Buys to the Best Customer Demographic

Do sponsorships align with customer base?

Monitored transactions, renewals, customer care calls

Leveraged data to pitch right product, right time

Decrease in marketing cost

Increase in revenue, profit, customer satisfaction

VEHICLES FOR BIG DATA

Data Warehouse

Regional and Departmental

Views

ADS

Applications & Engines

Operational Analytics & Hot Views

Data Marts Independent

Dependent

Relational Data

Conformed Dimensions

Last Year

This Year

Next Year

THE EVER-EXPANDING DATA WAREHOUSE

•  Enterprise Data Warehouse users face huge annual upgrade expenses

•  To avoid this spend, organizations are looking for lower cost alternatives.

•  Movement of data to tape not desired, because data is offline and not available for analytics

•  Moving infrequently used data to Hadoop is a cost-effective, online option that preserves ability to query

Cost

DATA WAREHOUSE EXPANSION

Offload data to less expensive Hadoop cluster to save on data management costs

2

As data volume

increases exponentially,

cost of warehousing

rises also

Add operational data for greater insight and

agility in analytics and BI

4

1

Combine Hadoop data with DW data for a more

comprehensive view of history 3

HDFS

HDFS

HDFS

Where should analytics be created – in a relational environment or in Hadoop?

Where should they be analyzed? Do we have enough tools in a Hadoop environment to do analysis there?

How do businesses analyze a combination of structured and unstructured data?

Is it as simple as ‘structured data to the data warehouse or analytic one-offs and unstructured data to Hadoop’?

Is using Hadoop as a data refinery the best use of Hadoop?

Does any data go to both environments? Or do just summaries get shared?

Can price/performance of a database vendor’s product be superior to an open source product?

QUESTIONS FOR ACTIAN


The Briefing Room


The Briefing Room

Upcoming Topics

www.insideanalysis.com

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

2015 Editorial Calendar coming soon!

This Month: ANALYTIC PLATFORMS

November: DISCOVERY & VISUALIZATION

December: INNOVATORS


The Briefing Room

THANK YOU for your

ATTENTION!

Opening slide image courtesy of Wikimedia Commons

hadoop as an analytic platform: why not?

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