delivering real time analytics in 1 click

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©2015 Talend Inc. ©2015 Talend Inc.

Delivering Real Time Analytics in One Click Jean-Michel Franco - @jmichel_franco

Mark Balkenende - @MarkBalk

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See this presentation on line

An Online version of this presentation is accessible at the following URL.

• https://info.talend.com/en_bd_realtime_analytics_oneclick.html

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Your Speakers Today

Jean-Michel Franco Product Marketing Director – Data Governance Products

Mark Balkenende Manager, Technical Product Marketing

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Connecting the Data-Driven Enterprise

Data-Driven companies…

• 23 times greater customer acquisition

• 6 times greater customer retention

• 19 times more profitability

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Connecting the data driven enterprise with

Information

as an asset

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So you’re getting ready for rolling-out your data lake

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But will this finally meet the promises of analytics ?

In most companies, fewer than 10% employees have access to BI and analytic systems.

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Of course you can leverage data discovery, dataviz and predictive analysis

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Source: September 20, 2011, “Understanding The Business Intelligence Growth Opportunity” Forrester report

But the scope and reach of Analytics has expanded

NOW

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What you need to design is a data refinery

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BI as we believe it should go The three new dimensions of analytics

Build an agile and manageable

data integration layer

From dashboard to analytical application

Predictive analytics and machine learning

Embed analytics in your operational processes

Provisioning the data

Designing the System of

insights

Operationalize Your analytics

Big D

ata in

tegration

B

ig Data

An

alytics

Data In

te- gratio

n &

p

reparatio

n

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Build an agile and manageable integration layer

Data Inventory

Data Prepa-ration

Master Data

Mgmt.

Data Integra-

tion

Create your data

catalog.

Profile the Data.

Augment and connect.

Productize the

Data flows

Sanction the Data.

Share and monitor.

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Big data and Open source is opening new horizons for data scientists

Designing the system of insights

• Data scientist role is finally recognized as a must to success in analytics

• Democratization of Analytics/machine learning technologies

- Open source tools : Rapid Miner, Knime, R …

- Cloud based machine learning platforms : Google Prediction API, Azure ML, Amazon ML…

- Larger range of options of high end solutions: Blue Yonder, Watson, SAS, BigML…

• Better options to operationalizing analytics, rather than use it mostly on an ad-hoc basis

- Run the model in place and schema on-read, where the Big Data is with Hadoop

- Robust options for deploying models are now emerging (Mahout, Spark ML)

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Operationalize your analytics

Enterprise Apps

Market Data

Sensors

Logs

Digital applications

Data Integration

Real time Data & application

integration

Data warehouse

& marts

Ad hoc analysis

& mining

Repoting

Data

Lake

Data profiling

& preparation

Data

Discovery Data

modeling

Th

e D

ata

Lab

Th

e D

ata

F

acto

ry

Data

Hub

Data

flows

Predictions

& prescriptions

Embedded

analytics

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Easiest and Most Powerful Integration Solution for Big Data

Introducing Talend Big Data

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Future-Proof Architecture

ETL Day-to-day integration

ELT DW Appliance

CAMEL Message Transformation

HADOOP Highly

Scalable

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Simplify Real-Time Big Data

100x performance increase

< 1 sec response

Address new use cases

(last minute defense, dynamic pricing, real-time fraud detection, CEP, etc.)

New components for streaming data

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Spark integration in Talend Studio

Apache

• Technical Preview

• Machine learning components require a Talend Big Data Platform license

• Implementation of Spark, ML LIB and Spark Streaming API

• 17 Components for data integration - Data integration : Load, Connection,

Sample, FilterRow, FilterColumns, Normalize, Union, Replicate, Aggregate, Sort, Join, Uniq, Log, Store

- Machine learning and Data Quality: Sample, ALS Model, Recommend

"Don't assume you can easily port existing applications to Spark from another data-processing model, like MapReduce. Moving to Spark means a complete reimplementation, and the potential benefits must outweigh that cost. "

Nick Heudecker - Gartner

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Otto Optimizes Pricing & Stock

A company that’s doing everything right

Challenge:

• Ever increasing Big Data velocity

• Many last minute cart abandonments

• Hard to optimize pricing

Why Talend:

• Is the central integration tool within their Business

Intelligence (BI) organization.

• Integrates clickstreams from last 6 months

Value:

• Leftover merchandise reduced by 20%

• Can predict abandoned shopping cart in real-time with a 90%

accuracy

• Performs dynamic pricing

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Demonstration

Key capabilities • Drives the learning process by integrating data in

Hadoop and launch the MLlib learning process • Drives the recommendation process by ingesting

demographics data into the engine, and integrating the output into any application or data target.

Business Benefits • Hides the underlying complexity of Hadoop and

Spark • Easily embed machine learning into any

application or data target • Machine learning with precision and at scale • Predictive analysis for the rest of us

Demographics data Big Data

tSparkALSModel

tSparkRecommend

Test

Run

Training data

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Start now with the Talend Big Data Sandbox

Virtual Image installed with • Multiple scenarios for you to try:

- Clickstream data

- Twitter sentiment

- Apache weblogs

- ETL Offload

- Recommendations through Spark Machine Learning

Download your Free Talend Big Data Sandbox today! http://www.talend.com/talend-big-data-sandbox

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©2015 Talend Inc. ©2015 Talend Inc.

Delivering Real Time Analytics in one click Jean-Michel Franco - @jmichel_franco

Mark Balkenende - @MarkBalk