luciano resende's keynote at apache big data conference

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IBM Spark Technology Center

Apache Big Data NA 2016

Leveraging Open Source Analytics for making game changing decisions

Luciano Resende IBM | Spark Technology Center

IBM Spark Technology Center 2

Apache Big Data Evolution

http://pepperdata.com/2014/06/the-10-hottest-words-at-hadoop-summit-2014/

IBM Spark Technology Center 3

Apache Big Data Evolution

IBM Spark Technology Center 4

Apache Big Data Evolution

IBM Spark Technology Center 5

The Analytics

Operating System

IBM Spark Technology Center

Enhanceit! Offerit!

Leverageit!

SparkTechnologyCenter@SF

ShippingwithBigInsights/

SparkasaService

Insideourproducts

At IBM, We Love Spark!

OpensourcedApache

SystemML

OpensourcedApacheQuarks

IBM Spark Technology Center

IBM is Building on Apache Spark IBM Analytics

IBM Commerce

IBM Watson

IBM Research

IBM Cloud

Image source: http://zdnet2.cbsistatic.com/hub/i/r/2015/06/15/1a23c9cd-74bc-4c8b-9e83-da45e977d97d/thumbnail/770x578/4a70eb03e79c794393d1d7d26bb34687/ibm-apache-spark.gif

IBM Spark Technology Center 8

How our customers are leveraging

open source analytics

IBM Spark Technology Center

The Weather Company Data volumes from weather are growing ! –  ~30 billion API requests per day

–  ~120 million active mobile users

-  #3 most active mobile user base

–  ~360 PB of traffic daily

–  Billions of events per day (~1.3 M per sec)

–  Keep data forever

The use case – Efficient batch + streaming analysis

– Self-service data science

– BI / Visualization tool support

IBM Spark Technology Center

Healthcare Enterprise Health Care Data Lakes – Improve how health care is delivered

– Collect and combine data from dozens of sources

– Clinical, Operational, Financial

– Inside and outside your enterprise

Benefits – Better medical outcomes for patients

– Control cost and improve quality

SystemML on Spark – Predictive Risk Modeling

– Right patient intervention relating to adverse heath events

10

IBM Spark Technology Center

Spark maps Customer Experience “journey”

The Challenge – Improve Customer satisfaction rates

– Multiple channels for customer iteractions

– Very large volumes of data

The need – Create a 360 degree view of a customer

– Stich all interactions across channels –

“Customer Experience Journey”

– Classify interaction sentiment and take necessary actions

PUB / SUB MQTT / WebSockets / Flume / Kafka

` ` `

Journey

Dashboards

Interaction & Journey Data

Voice & Text Data

IBM Spark Technology Center

12 Image source: http://az616578.vo.msecnd.net/files/2016/03/21/6359412499310138501557867529_thank-you-1400x800-c-default.gif

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