Streaming Analytics - Comparison of Open Source Frameworks and Products

Fast Data and Streaming Analytics
in the Era of Hadoop, R and Apache Spark
Kai Wähner
kwaehner@tibco.com
@KaiWaehner
www.kai-waehner.de
LinkedIn / Xing  Please connect!

Key Messages
– Streaming Analytics processes Data while it is in Motion!
– Automation and Proactive Human Interaction are BOTH needed!
– Time to Market is the Key Requirement for most Use Cases!

Agenda
– Real World Use Cases
– Introduction to Stream Processing
– Market Overview
– Relation to other Big Data Components

© Copyright 2015 TIBCO Software Inc.
Find and Act on “Critical Business Moments”
“Business Moments” occur in Every Facet of Enterprise Operations, they drive
competitive differentiation, customer satisfaction and business success!
Optimize
Pricing Identify
fraud
Make cross-
sell offers
Restock
inventory
Reroute
trucks
Deliver proactive
customer service
Predict equipment
failure & fix
proactively Anticipate
and handle
disruptions

Operational Intelligence in Action
© Copyright 2000-2015 TIBCO Software Inc.
Actions by Operations
Human decisions in real time informed by
up to date information
The Challenge:
Empower operations staff to see and
seize key business moments6
Automated action based on models of history
combined with live context and business rules
The Challenge:
Create, understand, and deploy algorithms &
rules that automate key business reactions
Machine-to-Machine Automation

7
Success Story
Predictive Fault Management

“An outage on one well can cost $10M per
hour. We have 20-100 outages per year.“
- Drilling operations VP, major oil company

Data Monitoring
• Motor temperature
• Motor vibration
• Current
• Intake pressure
• Intake temperature
 Flow
Electrical power cable
Pump
Intake
Protector
ESP motor
Pump monitoring unit
Electric Submersible Pumps (ESP)
Predictive Analytics (Fault Management)

Voltage
Temperature
Vibration
Device
history
Temporal analytic: “If vibration spike is followed by temp spike then
voltage spike [within 12 minutes] then flag high severity alert.”
Predictive Analytics (Fault Management)

Live Surveillance of Equipment
Continuous, live geospatial display of pump
health and predictive signal breeches
Alerts based on
predictive signals
Compare live readings and signals
to historical average and means
Continuous, live visualization
of stats per 100’s of wells

12
Success Story
Smart Manufacturing

IoT for High Tech Manufacturing Yield Optimization
• Before: Solar Panel Manufacturer with No Unified View of
Manufacturing Process
– Multiple manufacturing facilities, multiple processes – no way to compare
production to yield expectations
• Negative Consequences: Sub-Optimal Production
– Operations are sub-optimal: high tolerance leads to better yield but less
output; tight tolerance means high throughput but lower yield
• Business Outcome: Higher Yield and More Runs
– Process Manufacturing can run tighter tolerances and adjust them mid-run,
predicting yield and adjusting to changing variables
– Systems proactively re-route high-value customers around affected network
areas in real-time
• How We Do It: The TIBCO Fast Data Platform
– IoT, Spotfire, StreamBase, and TERR for predictive modeling, high-speed
network by TIBCO “For every 1% increase in shipped
product, we make $11MM in profit.
The demand is there, we just need to
fulfill it.”
- Head of Quality, Solar Panel Manufacturer

High Tech Manufacturing Yield Optimization
Live streaming datamart analysis
Continuous update and exploration of
top yield metrics; take action

Continuously computed real-time analytics on streams by
StreamBase (thresholds, min / max, average)
Analysis, alerts and triggers are based on streaming analytics

Manufacturing operations staff
drill down on any machine, any
time, to inspect and fix problems
before they impact yield

17
Success Story
Crowd Management

18
Crowd Management (Stadium, Airport, Conference, …)

Sacramento Kings  World’s Smartest Building

20

21

22

23

24

25

26

27
Success Story
Retailing in the 21st Century

Challenges of the 21st Century Retailer
• Retailing and Retail Challenges are changing
• Consumers expect better and integrated customer experience across all channels
– Rapid adoption of mobile is a major driver
– Customers want an integrated service across physical and digital channels… Simultaneously
– Customer experience is becoming one of the main differentiators
• Real-Time, one-on-one marketing can:
– Improve a retailer’s relevance with the customer
– Increase customer wallet-share
• Key to being able to achieve this is:
– Identifying and knowing your customer, in depth in real-time
– Understanding the opportunity their past behavior reveals
– Understanding your inventory (availability, velocity, pipeline)

29
All Customers are different… Treat them that way…
Capture – Engage – Expand - Monetize
Patterns – Real time
MOREPERSONAL
MORE CONTEXT
social
CRM
POS
mobileweb
e-mails

National Retailer Loyalty 2015
Top Benefits
• Smart cross-selling based in iBeacons
• Location-based services in real time
• Leveraging partner offerings

New Real-Time Fraud Detection
Based on Deep Historical Insight
Real-time fraud action can be taken based on
historical insight – system not “whiplashed”
by real-time events
Streaming Analytics for Gift Card Fraud Protection

32
Internet of Things
Hybrid Stores
Smart Tags
Smart Shelves
Smart Warehouse
Faster Delivery
Buy Online
Pickup at Store
Same Day
Delivery
Omni Channel 2.0
Store Fulfillment
Social Media
Predictive
Shopping
National Retailer Loyalty 2018

33
Great success stories, but …
… how to realize these use cases?

34
Real Time Close Loop
Model
Develop model
Deploy into Stream
Processing flow
Act
Automatically monitor
real-time transactions
Automatically trigger
action
Analyze
Analyze data via Data
Discovery
Uncover patterns,
trends, correlations

Traditional Data Processing: Challenges
• Introduces too much “decision
latency” into the business.
• Responses are delivered “after-the-
fact”.
• Maximum value of the identified
situation is lost.
– Cross-sell / up-sell opportunities are lost,
impending equipment failure is missed,
business processes are slow to respond
and lack timely context.
• Decisions are made on old and stale
data.
Store
Analyze
Act

The New Era: Fast Data Processing
• Events are analyzed and processed
in real-time as they arrive.
• Decisions are timely, contextual, and
based on fresh data.
• Decision latency is eliminated,
resulting in:
 Superior Customer Experience
 Operational Excellence
 Instant Awareness and Timely Decisions
Act
Analyze
Store

Streaming Analytics
time
1 2 3 4 5 6 7 8 9
Event Streams
• Continuous Queries
• Sliding Windows
• Filter
• Aggregation
• Correlation
• …

39
Act while data is in motion!
Time
Business
Value
Business Event
Data Ready for Analysis
Analysis Completed
Decision Made
$$$$
$$$
$$
$ Action Taken
Stream Processing
speeds action and
increases business
value by seizing
opportunities while
they matter

Operational Analytics
Operations
Live UI
SENSOR DATA
TRANSACTIONS
MESSAGE BUS
MACHINE DATA
SOCIAL DATA
Streaming AnalyticsAction
Aggregate
Rules
Stream Processing
Analytics
Correlate
Live Datamart
Continuous query
processing
Alerts
Manual action,
escalation
HISTORICAL ANALYSIS
MS Excel
SAS
Data
Scientists
Cleansed
Data
History
Data Discovery
R
Enterprise Service Bus
ERP MDM DB WMS
SOA
BIG DATA
Data Warehouse, Hadoop
InternalData
IntegrationBus
API
Event Server
Streaming Analytics Reference Architecture
Spark

44
Alternatives for Stream Processing
Time
to
Market
Streaming
Frameworks
Streaming
Products
Slow Fast
Streaming
Concepts
IncludesIncludes

Concepts (Continuous Queries, Sliding Windows)
Patterns (Counting, Sequencing, Tracking, Trends)
Build everything by yourself! 
45
What Streaming Alternative do you need?
Time
to
Market
Streaming
Frameworks
Streaming
Products
Slow Fast
Streaming
Concepts

46
Usually not an option ...
… as there are a lot of
Frameworks and
Products available!

47
Alternatives
OPEN SOURCE CLOSED SOURCE
PRODUCT
FRAMEWORK
(no complete list!)

Library (Java, .NET, Python)
Query Language (often similar to SQL)
Scalability (horizontal and vertical, fail over)
Connectivity (technologies, markets, products)
Operators (Filter, Sort, Aggregate)
48
Time
to
Market
Streaming
Frameworks
Streaming
Products
Slow Fast
Streaming
Concepts

49
Apache Storm
Spout Bolt

50
Apache Storm – Hello World
http://wpcertification.blogspot.ch/2014/02/helloworld-apache-storm-word-counter.html

51
Amazon Kinesis
https://aws.amazon.com/kinesis/
AWS S3 RedShift DynamoDB

52
Amazon Kinesis – Hello World

53
Amazon Kinesis – The Cloud ...
… is easy to setup and scale!
But you do not have full control 
• Any data that is older than 24 hours is automatically deleted
• Every Kinesis application consists of just one procedure, so you can’t use Kinesis to
perform complex stream processing unless you connect multiple applications
• Kinesis can only support a maximum size of 50KB for each data item
http://diamondstream.com/amazon-kinesis-big-real-time-data-processing-solution/
(blog post from 2014, might be outdated, but shows that you do not have full control over a cloud service)

54
Apache Spark
General Data-processing Framework
 However, focus is especially on Analytics (these days)
http://fortune.com/2015/09/09/cloudera-spark-mapreduce/

55
Apache Spark – Focus on Analytics
http://aptuz.com/blog/is-apache-spark-going-to-replace-hadoop/
http://fortune.com/2015/09/09/cloudera-spark-mapreduce/
http://www.ebaytechblog.com/2014/05/28/using-spark-to-ignite-data-analytics/
http://www.forbes.com/sites/paulmiller/2015/06/15/ibm-backs-apache-spark-for-big-data-analytics/
“[IBM’s initiatives] include:
• deepening the integration between Apache Spark and
existing IBM products like the Watson Health Cloud;
• open sourcing IBM’s existing SystemML machine
learning technology;

56
Spark Streaming
Spark Streaming
• is no real streaming solution
• uses micro-batches
• cannot process data in real-time (i.e. no ultra-low latency)
• allows easy combination with other Spark components (SQL, Machine Learning, etc.)

57
Apache Spark – Hello World
Spark Streaming API
Spark Core API

58
Alternatives
OPEN SOURCE CLOSED SOURCE
PRODUCT
FRAMEWORK
(no complete list!)

Visual IDE (Dev, Test, Debug)
Simulation (Feed Testing, Test Generation)
Live UI (monitoring, proactive interaction)
Maturity (24h support, consulting)
Integration (ootb integration: ESB, MDM, etc.)
Library (Java, .NET, Python)
Query Language (often similar to SQL)
Scalability (horizontal and vertical, fail over)
Connectivity (technologies, markets, products)
Operators (Filter, Sort, Aggregate)
Time
to
Market
Streaming
Frameworks
Streaming
Products
Slow Fast
Streaming
Concepts

60
IBM InfoSphere Streams

61
IBM InfoSphere Streams
https://developer.ibm.com/streamsdev/wp-content/uploads/sites/15/2014/04/Streams-and-Storm-April-2014-Final.pdf

TIBCO StreamBase
• Performance: Latency, Throughput, Scalability
– Multi-threaded and clustered server from version 1
– High throughput: Millions of messages, 100,000s of quotes, 10,000s of orders
– Low-latency: microsecond latency for algo trading, pre-trade risk, market data
• Take Advantage of High Performance Hardware
– Multicore (12, 24, 32 core) large memory (10s of gigabytes)
– 64-bit Linux, Windows, Solaris deployment
– Hardware acceleration (GPU, Solace, Tervela)
• Enterprise Deployment
– High availability and fault tolerance
– Distributed state management for large data sets
– Management and monitoring tools
– Security and entitlements Integration
– Continuous deployment and QA Process Support
StreamSQL compiler
and static optimizer
In process, in thread
adapter architecture
Visual parallelism and
scaling
ActiveSpaces
integration for
distributed shared state
Data parallelism and
dispatch
StreamBase Server
Innovations
“The StreamBase engine is for real. We couldn’t break it, and believe
me, I tried” SVP Development, Top 5 Broker Dealer

StreamBase: The Power of Visual Programming
1) Get ideas into
market in days or
weeks, not months or
years
2) Unlock the power of
IT and data scientists
working together

64
Code Anyone Can Read
Limit Gift Card Activation Amounts at One Location
Aggregate
Capture card activations per
location
Sales too high!
Log to any
database
No Fraud
Sales too high?

Visual Debugger
Feed Simulation
Unit Testing
“StreamBase’s modeling tools are easy to
use and will enable the exchange to
quickly react to the ever changing needs of
our customers.” Steve Goldman,
Director of Enterprise Architecture
StreamBase Development Studio

Live Datamart
Continuous Query Processor Alerts
BusinessEvents
FTL
EMS
ActiveSpaces
Live Datamart
BusinessWorks
Social Media Data
Market Data
Sensor Data
Historical
Data
ActiveSpaces Datagrid
Enterprise
dataMarket Data
IoT
Mobile
Social
LiveView Desktop
Command & Control
ACTION
Continuous Query

67
Dynamic aggregation
Live visualization
Ad-hoc continuous query
Alerts
Action
LiveView Desktop

Live Datamart Clients and APIs
• Rich Desktop Client
– Drag&Drop, no coding
• Rich Web Client
– Drag&Drop, no coding
• HTML5 and Javascript API
– D3, jQuery, ExtJS, Google Charts,
Bing, AngularJS
• .NET API
– For custom .NET development
• Java API
– For custom Java GUI development
• Combination
– Rich Client + HTML5 Extensions

Predictive Sensor Analytics
Live Demo (Stream Processing)

70
Spoilt for Choice – Which one to choose?
What are the
key aspects?

71
What do you need (out-of-the-box)?
• A stream processing programming language for streaming analytics
• Visual development and debugging instead of coding
• Out-of-the-box connectivity to streaming and historical data sources
• Performance (real-time vs. micro-batches)
• Automated monitoring and alerts
• Live UI for proactive human interaction
• Maturity and proven deployments
• Fault tolerance
• Commercial support
• Professional services and training

72
Spoilt for Choice – Framework or Product?
Does it make sense
to combine both?

Example: Apache Storm + TIBCO Live Datamart
External
Data
Snapshot Results
Continuous Query Processor
Query
TIBCO Live Datamart
Continuous
Alerting
Active Tables Active Tables
Continuous
Updates
Clients
Message Bus
Public Data
Customer Data
StreamBase Bolt StreamBase Spout
Operational
Data
StreamBase Bolt and Spout connect
Apache Storm to StreamBase to provide
real-time analytics on operational data

76
Real Time Close Loop
Model
Develop model
Deploy into Stream
Processing flow
Act
Automatically monitor
real-time transactions
Automatically trigger
action
Analyze
Analyze data via Data
Discovery
Uncover patterns,
trends, correlations

Real Time Close Loop: Understand – Anticipate – Act
Big Data
 store everything
in Hadoop, DWH, NoSQL, etc.
 even without structure
 even if you do not need it today
http://blogs.teradata.com/international/tag/hadoop/

Data Discovery + Statistics + Machine Learning
to find insights and patterns in historical data

Streaming Analytics
to operationalize insights
and patterns in real time
Stream
Processing
Hadoop
Open
Source
R
TERR
SAS
MATLAB
In-
database
analytics
Spark

R with Revolution Analytics (now Microsoft)
Open Source GPL License
http://www.revolutionanalytics.com/webinars/introducing-revolution-r-open-enhanced-open-source-r-distribution-revolution-analytics

R with TIBCO Runtime for R (TERR)
TIBCO TERR delivers production-grade R analytics to
enterprises
 Flexibility & analytic power of R language
 Time-to-market agility
 Enterprise-grade platform
• A TIBCO licensed & supported product
• Not GPL, not a repackaging of the Open source R engine
• Deployment in TIBCO products and 3rd party applications
(e.g. Hadoop)
http://spotfire.tibco.com/discover-spotfire/what-does-spotfire-do/predictive-analytics/tibco-enterprise-runtime-for-r-terr

Use Open Source R or Not?
http://www.forbes.com/sites/danwoods/2015/01/27/microsofts-revolution-analytics-acquisition-is-the-wrong-way-to-embrace-r/

Spark MLlib
MLlib is Spark’s machine learning
(ML) library. Its goal is to make
practical machine learning scalable
and easy.
It consists of common learning
algorithms and utilities, including
classification, regression,
clustering, collaborative filtering,
dimensionality reduction, as well as
lower-level optimization primitives
and higher-level pipeline APIs.
You can even combine Mllib module with R language

Predictive Sensor Analytics
Live Demo (Data Discovery, Statistics)

80% of betting happens
AFTER the game begins
TODAY

Case Study: Streaming Analytics for Betting
• Situation: Today, 80% of Betting is Done After the
Game Starts
• It’s not your father’s bookie anymore!
• Problem: How to Analyze Big Betting Data?
• Thousands of concurrent games, constantly adjusting odds, dozens of
betting networks – firms must correlate millions of events a day to find
the best betting opportunities in real-time
• Solution: TIBCO for Fast Data Architecture
• TXOdds uses TIBCO to correlate, aggregate, and analyze large
volumes of streaming betting data in real-time and publish innovative
predictive betting analytics to their customers
• Result: TXOdds First to Market with Innovative Zero
Latency Betting Analytics
• Innovative real-time analytics help players who can process electronic
data in real-time the edge
“With StreamBase, in two
months we had our first betting
analytics feed live, and we
continually deploy new ideas
and evolve our old ones.”
- Alex Kozlenkov, VP of technology, TXOdds

87
“WHEN 5 KEY BOOKIES RAISE
THE SAME ODDS IN A 5-SECOND
WINDOW, BET LESS”
? ? ? ? ?? ???

88
“WHEN THE REAL-TIME ODDS ARE
5% GREATER THAN THE HISTORICAL
SPREAD, INCREASE MY BET”
? ? ? ? ? ? ? ? ? ? ? ? ? ?
? ? ? ? ? ? ? ? ? ? ? ? ? ?

Reference Architecture: Streaming Betting Analytics
Event Processing
MONITOR
REAL-TIME ANALYTICS
AGGREGATE
HISTORICAL COMPARISON
Predictive odds
analytics
Zero Latency
Betting Analytics
GLOBAL, DISTRIBUTED INFRASTRUCTURE
Historical odds
deviations
B
U
S
BETTING LINES
SCORES
NEWS
HADOOP
Context:
Historical
Betting Data,
Odds, Outcomes
B
U
S
CACHE CACHE CACHE
Real-Time Analytics
CORRELATE
StreamBase LiveView
SOCIAL

Twitter
(#TomBradyBrokenLeg)
Twitter (#Boston)
Brady’s Stats
Actionable
Insights
Real-Time Social Media Analytics
Twitter (#NFL)
Something relevant happening?
Every minute counts!
Change Odds (automated or manually triggered):
• Stop live-betting for the currently running game?
• How many interceptions will the Quarterback throw?
• Will the Patriots win the Super Bowl?
• …

91
Real-Time Social Sentiment Analysis

– Streaming Analytics processes Data while it is in Motion!
– Automation and Proactive Human Interaction are BOTH needed!
– Time to Market is the Key Requirement for most Use Cases!
Key Messages

Questions?
Kai Wähner
kwaehner@tibco.com
@KaiWaehner
www.kai-waehner.de
LinkedIn / Xing  Please connect!

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