info vision sanjeev kumar _ why data is drowning it world

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Why Data is Drowning the (IT) World?

Sanjeev KumarVP & MD, Informatica India

Infovision 2012 SummitOctober 2012

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Agenda

• Why the Data Deluge?

• Trends Affecting Data Growth

• New Use-cases Enabled by Big Data

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Agenda




• Trends Underlying Big Data

• Building-blocks for Managing Big Data

• Q&A

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Data is the New Plastic

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Where Are We? Computing Circa 2012!

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• Six decades into the Computer Revolution

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• Four decades since the invention of Microprocessor

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• Two decades into the rise of modern Internet

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• Two billion people using the broadband Internet

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• Two billion people using the broadband Internet

Major businesses and industries running on software and delivered as online services*

*”Why software is eating the world” Marc Andreessen, WSJ Aug 2011

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Source: An IDC White Paper - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands. May 2009.

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Trends: Exploding Data Volumes, “Big Data”

Relational

Complex, Unstructured

• 2,500 Exabytes of new information in 2012 with Internet as primary driver• Digital universe grew by 62% last year to 800K petabytes and will grow to 1.2 “Zettabytes” this year

Kilo – Mega – Giga – Terra –Peta – Exa – Zetta - Yotta

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Big Data Buzz!

• 16 Big Data “V”s; Original 3: Volume, Variety & Velocity

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Big Data Buzz!

• 16 Big Data “V”s; Original 3: Volume, Variety & Velocity• 120+ Twitter accounts relating to Big Data

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Big Data Buzz!


• 9000 job search results for “data scientists”

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Big Data Buzz!


• 9000 job search results for “data scientists”• 70,000 Wikipedia “big data” hits per month

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Big Data Buzz!


• 9000 job search results for “data scientists”• 70,000 Wikipedia “big data” hits per month• 2,000,000 PDFs from search on “big data white paper”

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Big Data Buzz!


• 9000 job search results for “data scientists”• 70,000 Wikipedia “big data” hits per month• 2,000,000 PDFs from search on “big data white paper”• 112,000,000 Blog posts discussing big data

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Big Data Buzz!


• 9000 job search results for “data scientists”• 70,000 Wikipedia “big data” hits per month• 2,000,000 PDFs from search on “big data white paper”• 112,000,000 Blog posts discussing big data• 1,350,000,000 Google results for “What is big data?”

Source IBM 2012

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Why Now? Exploding Data Volumes

Internet of things

Increased consumption of digital content

Explosion in user generated content

Proliferation of web connected devices

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Trends: Changing Data Economics

Low ROB

Return on Byte = value to be extracted from that byte / cost of storing that byte.

High ROB

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Trends : Data Seen as a Strategic Asset

• Companies leveraging data assets to• Create new and differentiated products

• Product recommendation engines • Increase revenues

• Optimize ad placement to improve click-thru• Improve customer satisfaction / retention

• Analyze CDRs for dropped calls The sexy job in the next ten years will be statisticians. The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill. Hal Varian : Chief Economist, Google.

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Big Data in the Enterprise

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Why Now? Big Data Use-cases – User Behavior

• Location & Proximity Tracking• GPS in operational apps, security analysis, navigation & social media• New business opportunities for sales and services in proximity

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• Ad Tracking• Dynamic changes in ad placement, color, size and wording• Improved click-through behavior

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• Social CRM• Text analytics on huge array of unstructured social media• KPI’s: share of voice, audience engagement, conversation reach, …

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• Social CRM• Text analytics on huge array of unstructured social media• KPI’s: share of voice, audience engagement, conversation reach, …

• Causal Factor Discovery in Retail• Deviations based on competition, weather, promos, holidays, events

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Why Now? “Hadoop-able” Use-cases – Sensors

• Building Sensors• Temperature, humidity, vibration and noise• Energy usage, security violations, failures in a/c, heat, plumbing

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• In-flight Aircraft Sensors• Variables on engines, hydraulics, fuel & electrical systems• Real-time adaptive control, fuel usage, part failure prediction

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• Smart Utility Meters – Electric Grid• One read-out per second per meter across entire customer base• Dynamic load balancing on grid, failure response, adaptive pricing

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• Smart Utility Meters – Electric Grid• One read-out per second per meter across entire customer base• Dynamic load balancing on grid, failure response, adaptive pricing

• Mobile Cell Tower Networks• Analyze call-data-records(CDRs) to optimize cell tower placement• Improved user experience and network monetization

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“Hadoop-able” Use-cases – Computing Delta’s

• Commercial Seed Gene Sequencing• Analyzing the sequence, identifying genes and gene families• Baseline reference for the larger cotton crop genome

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• Satellite Image Comparison• Overlay of images to create “hot spot” maps to show differences• Construction, destruction, changes due to disasters, encroachment

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• CAT Scan Comparison• Images taken as “slices” of human body• Automatic diagnosis of medical issues and their prevalence

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• CAT Scan Comparison• Images taken as “slices” of human body• Automatic diagnosis of medical issues and their prevalence

• Document Similarity Testing• Latent semantic analysis: “documents that agree with my doc”• Threat discovery, sentiment analysis and opinion polls

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Agenda




• Trends Underlying Big Data

• Building-blocks for Managing Big Data

• Q&A

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Big DataConfluence of Big Transaction, Big Interaction and Big Data Processing

OnlineTransactionProcessing(OLTP)

Online AnalyticalProcessing(OLAP) &DW Appliances

SocialMedia Data

DeviceSensor Data

Scientific, genomic

Machine/Device

BIG TRANSACTION DATA BIG INTERACTION DATA

BIG DATA PROCESSING

Call detail records, image, click stream data

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OnlineTransactionProcessing

(OLTP)

Online AnalyticalProcessing(OLAP) &

DW Appliances

OracleDB2Britton-LeeIngresInformixSybaseSQLServer

TeradataRedbrickEssBaseSybase IQNetezzaGreenplumDataAllegroAsterdataVerticaParaccelHana

BIG TRANSACTION DATA

Big Transaction DataOLTP and Analytic Databases

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HRApplication

CRMApplication

MainframeCustomApplication

CustomApplication

CustomApplication

CustomApplication

CustomApplication

Big Transaction DataChanging Economics of Computing From Buy To Rent

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Big Interaction DataChanging Role Of Computing From Transactions to Interactions

SocialMedia Data

DeviceSensor Data

Clickstream

Image/Text

Scientific• Genomic/Pharma• Medical

Machine/Device• Sensors/Meters/

RFID Tags• CDR/Mobile

BIG INTERACTION DATA

Social Media

Device Sensor Data

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Big Interaction DataFrom Operational Efficiency To Organizational Effectiveness

Business Management• Business Analysis • Operational Automation

Brand Management• Sentiment Analysis• Proactive Customer

Engagement

RelationalTransactions

1970 - Current

SocialInteractions

2008 - Current

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Big Interaction DataHow Do You Leverage Device Sensor Data?

• Geo Encoding

• Cell-phone Towers

• Medical Sensors

• RFID Tags

• Edge Networks

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Big Data ProcessingHighly Scalable Processing Of All Data

OnlineTransactionProcessing(OLTP)

Online AnalyticalProcessing(OLAP) &DW Appliances

SocialMedia Data

DeviceSensor Data

Scientific, genomic

Machine/Device

BIG TRANSACTION DATA BIG INTERACTION DATA

BIG DATA PROCESSING

Call detail records, image, click stream data

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Big Data Processing What is Hadoop?

PARALLEL

PERSISTENCE

SCRIPTING SQL QUERY

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Big Data ProcessingWhat does Hadoop do?

• Cost effective scalability• Scale out on commodity hardware

• Support for processing all data types• Structured, Semi-structured and Unstructured data

• Extensibility• Open APIs to implement custom data processing logic

• Hadoop Challenges• Data movement into/out of Hadoop / HDFS• Requires specialized development skills

• Java, Hive, PIG etc.

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Ingest Data Into HDFSSupport over 100different data sources

Native HDFSSource and

Target Support

Integrated development environment with metadata and preview support

Perform any preprocessing

needed before ingestion

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Design and Execute Data Integration Logic on Hadoop

Design integration logic for Hadoop in a graphical and metadata driven environment

Configure where the integration logic should run – Hadoop or Native

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Address Validation

Standardize

Parsing

Matching

Design and Execute Data Quality on HadoopBig Data Cleansing, Dedup, Unstructured Parsing

Address Validation and Geocoding enrichment across

260 countries

Probabilistic or Deterministic Matching

Standardization and Reference Data Management

Parsing of Unstructured Data/Text Fields of all data types of data (customer/

product/ social/ logs)

DQ logic pushed down/run natively ON Hadoop

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Extract data from HDFS and Hive

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Persist and write hadoop data into DW, HDFS or any target systems

Extract from HDFS as a native source

Extract from Hive as a native source

Perform any post processing

needed after extraction

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Processing Big Data : What is missing?• Support for graph/networked data

• How does one visualize complex relationships?

• Data with dynamic schemas• Do the current patterns scale for very large number of columns?

• Are mappings the right paradigm?

• Ability to extract entities from unstructured data

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References

• Why Software is Eating the World• Marc Andreessen, WSJ Aug 2011

• Evolving Role of EDW in Era of Big Data Analytics• Ralph Kimball, Kimball Group 2011

• Data Scientist: Sexiest Job of the 21st Century• Thomas H. Davenport & D.J.Patil, HBR Sept 2012

• Newly Emerging Best Practices for Big Data• Ralph Kimball, Kimball Group Oct 2012

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Questions

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