real-time analytics using spark and objectivity's thingspan

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REAL-T IME ANALYTIC S USING SPARK AND

OBJECT IV ITY ’S THINGSPAN

BY O B J E C T I V I T Y I N C .

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OBJECTIV ITY ’S PEDIGREE• Headquartered in Silicon Valley since 1988

• Pioneer in high-performance distributed object data technology

• Decades of experience in “beyond petabyte” data volumes

• Deep domain expertise in massively scalable graph analytics

• Software validated and proven by Global 1000 customers and partners

WHY GRAPH?• Use actual relationships in

addition to statistical correlation

• Ultra-fast navigation and path finding without joins

• Combine conventional and graph analytics to support advanced pattern finding

• In-Memory graph limited by RAM and machine

• Billions of nodes and edges require parallelism and a distributed graph

SCALING GRAPHS

F INANCIAL USE CASES

• Smart Trading (alpha generation, portfolio optimization)

• Regulation and Compliance (Know Your Customer)• Cybercrime Prevention and Detection (security

breaches)

• Uses: Alpha generation; portfolio optimization

• Data sources: Financial accounts, markets, sectors, exchanges, reference data, social media

• Opportunities: • Compare streaming data to

historical trends • Determine relationships

between transactions to forecast stock value

SMART TRADING

• Uses: Know Your Customer; detect insider trading and securities fraud

• Data sources: Financial accounts, emails, SMS, social media

• Opportunities: • Accurately understand risk• Prevent loss of revenue due

to rogue activities and fines

R E G U L AT I O N & C O M P L I A N C E

• Uses: Identify unusual patterns indicative of security breaches

• Data sources: Network logs (firewall, proxy, VPN, DNS), emails, HR data

• Opportunities: • Correlate data from security

and network solutions with internal and semantic web apps

• Be proactive, not reactive

C Y B E R C R I M E P R E V E N T I O N

• ThingSpan is an massively scalable distributed platform purpose-built for real-time graph analytics and relationship discovery

• ThingSpan is architected to integrate and leverage major open source technologies – HDFS, YARN, Spark, Kafka

• ThingSpan supports a mixed workload environment with high-speed ingest and parallel querying

P O S I T I O N I N G

A N A LY T I CS

A FT E R-T H E - FACTS T R E A M I N G

P L AT F O R M

I N -T I M E

Time to Production

Time to Insight

T I M E -T O - I N S I G H T C O N T I N U U M

Real-time insight as events happen• ThingSpan + Spark Streaming

In-time context involving streams and state• ThingSpan + Graph Exploration

After the fact insight involving context and state• ThingSpan + Spark

Pattern Finding• Long-term insights

T H I N G S PA N A R C H I T E C T U R E

THINGSPAN STACK

D I S T R I B U T E D P R O C E S S I N G & D A T A B A S E

Hadoop Distributed File System

Distributed from top to bottom

T H A N K S F O R R E V I E W I N G !

Objectivity’s ThingSpan

• Real-time graph analytics

• Apache Spark-enabled

• Hadoop (HDFS)-ready

CONTACT US:

Headquartered in San Jose, CA

Contact Us: 408-992-7100

http://www.objectivity.com