StreamCentral for the IT Professional

http://www.virtus-it.com
A trusted partner
Software to model &
build Business
Intelligence &
Big Data Solutions

Table of Contents
• Big Data Overview………..………………………………………………………… 3 - 6
• Step by Step approach to building Big Data Solutions……………….. 7 - 10
• StreamCentral Introduction……………………………………..………….... 11 - 21
• Keeping it structured? –Extending current DW/BI investments………. 22 - 25
• An approach to building Operational Intelligence solutions………….... 26 - 28
• StreamCentral additional details……………………………………..………….... 29 - 37
• StreamCentral physical architecture……………………………..………….... 38 - 40
• How StreamCentral fits in an enterprise technology architecture…… 41 - 44
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BIGData
Today With Big Data
Custom Application
ERP
• Analysis of structured data from
internal applications
• Data sets updated using batch
processes
• Traditional BI & Data Warehousing
• Traditional BI and data
warehousing extended to
include structured and
unstructured data from internal
and external applications
processed in real-time or in
batch.
• Ability to predict events as well
as analyze historical
associations in wide sets of data
for patterns and trends.
Data Management Discovery & Analysis Event Detection
Big Data Solution
Modeling & Model
Deployment
Stream Processing &
Batch Data
Acquisition
Blocks for building Big Data Solutions
ERP
Internal & External Applications Data Stores
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0
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0
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0
Real-Time + Batch Big Data Processing Layer
Real-time event data in
operational applications
Pattern, trend and
association analysis
Understand the connections in a wide variety of data that impact
business performance and use that knowledge to deliver exceptional
business results

Understanding business performance with Big
Data includes two distinct capabilities:
Managing performance by analyzing
internal and external, structured and
unstructured data for patterns and
associations collected over time
• Customer segmentation based on buying
history patterns and finding associations
with population, census and twitter data
to develop marketing strategy
• Web analytics to improve marketing
campaigns and relevant content
• Sales pipeline analysis compared to
industry data to understand the right
goals to set
• Cash flow analysis to make capital
investment decisions considering
external variables
Managing performance by analyzing real-
time data for day to day events –
Operational Intelligence
• What is the current workload?
• Is staff available and working on
high priority work?
• What factors are impacting
customer experience right now?
• What processes are taking longer
than expected?
4
A few example scenarios: A few example scenarios:

Telco’s Core IMS
Network Data
Data, Voice & Video
Performance Data
Data, Voice & Video
Performance Data
Data from
Telco Towers
Weather Data
Traffic
IncidentsPopulation
Data
Data Stream
weatherundergrou
nd
MapquestUSA Today Census
data
Sources of real
time streaming
data from
networks,
devices, services
and other
internal
applications
External sources
of data that add
understanding of
what’s happening
when events are
detected
Example Big Data Solutions: Telco
Network
Test
New
Service –
Investment
Planning
Adaptive
Bit Rate –
Video
Streaming
QoE
360o
Customer
QoE for
1st Level
customer
service
Video QoE
for IPTV
Business
Solutions
5

Telco’s Core IMS
Network Data
Data, Voice & Video
Performance Data
Data, Voice & Video
Performance Data
Data from
Telco Towers
Weather Data Traffic IncidentsPopulation Data
Data Stream
weatherunderground INRIXUSA Today Census data
Example Big Data Scenario : Utilities - Water
Sources of real
time data
relating to
your business
Sources of
BIG DATA
relevant to
your business
VIBRATION
SENSOR
ENERGY
HARVESTING
WATER MAIN
PRESSURE
SCADA
NETWORK
6

http://www.virtus-it.com 7
Steps it takes to build
powerful Big Data
solutions!
Solution
Modeling
Model
Deployment
Data
Acquisition
(streaming or batch, internal or
external, structured or unstructured)
Data
Management
Event
Detection
Discovery &
Analysis
Big Data Solution Lifecycle
Start here

Solution Modeling
•Logical Data Model
design
•Data standardization
& transformation
modeling
•Key Performance
Indicator modeling
via business rules
•Dimensional
modeling
•Historical Data Mart
Modeling
•Event detection
modeling via
business rules
•Real-time analytics
data mart modeling
Model Deployment
•Physical Design
Implementation
•Physical deployment
of dimensional
model
•Database
deployment
•Physical deployment
of data marts
•Rules deployment
Data Acquisition
•Data from internal
data sources
•Data from external
sources
•Streaming data
•Batch data
•Structured Data
•Unstructured Data
•Data transformation
•Data standardization
Data Management
•Structured Data
Storage
•Unstructured Data
Storage
•Scalability
•Performance
Event Detection
•Detecting events on
streaming data
•Alerting
•Integration with
operational
applications
Discovery & Analysis
•Information
Discovery
•Data Classification
•Analytics
•Querying
•Visualization
Solution
Modeling
Model
Deployment
Data
Acquisition
Data
Management
Event
Detection
Discovery&
Analysis
Big Data Solution Lifecycle – Tasks Detailed

9
Solution
Modeling
Model
Deployment
Data
Acquisition
Data
Management
Event
Detection
Discovery &
Analysis
1. Hadoop - MapReduce
2. MPP Columnar Databases like Neteeza, Vertica, ParStream
3. NoSQL – MongoDB, Cassandra
4. Evolution of traditional RDBMS to support column indexes
SQL Server
Big Data Innovations in Data Management
Big Data Innovations in
Discovery & Analysis
Where has the
innovation been
in Big Data?
The last few years have seen
lots of innovation in Data
Management as well as
Discovery and Analysis

Solution
Modeling
Model
Deployment
Data
Acquisition
Data
Management
Event
Detection
Discovery &
Analysis
Big Data Lifecycle
But, where is the innovation in
these areas?
• Fragmented, point use or lack of
industry strength technology to aid in
Design, Model Deployment, Data
Acquisition and Event Detection
makes it difficult, time consuming
and specialist resource intensive to
build Big Data Solutions
• What is the use of having scalable
platforms that can store and manage
this data and tools that can deliver
incredible visualizations when the
effort to get the data right is still a
problem as it has always been?

Introducing
StreamCentral

Solution
Modeling
Model
Deployment
Data
Acquisition
Data
Management
Event
Detection
Discovery &
Analysis
Big Data Solution Life cycle
1. StreamCentral Solutions Designer makes it easy to
model traditional BI/DW and Big Data solutions
2. Builds and deploys model on HP Vertica or Microsoft
SQL Server
3. Adds context by connecting all streaming and static
data to time, location and entities
4. StreamCentral Big Data Server, horizontally scalable,
executes the model definition in real-time
5. StreamCentral drastically reduces time to market, risk
and cost in building Big Data solutions!
Software to design &
build BI & Big Data
SolutionsStreamCentral enables you to quickly move from a blank
sheet of paper to a production system, comprehensive and
powerful that can be delivered without a large investment
in specialist skills.

1010101010101010
ABCABCABCABCABC
StreamCentral Workbench:
Solution Designer
StreamCentral
Workbench:
Model DeploymentData
Collection
Data
Processing
Correlation
Data
Publishing
Data
Security
StreamCentral Big Data Server
StreamCentral has three main components:
1. Use the Workbench Designer to define source data, entities,
rules for monitoring conditions, events and data correlation,
analytical models and knowledgebase
2. Workbench Model Deployment configures, builds and deploys
the model on top of HP Vertica or Microsoft SQL Server
3. Big Data Server executes the defined model in real-time
1
2
3

Database
REST/SOAP
API
LDAP
PUSH
API
Data Processing
Engine
Vertica SQL Server
Correlation EngineCollector
Data Publishing, Access
and Security
• Capture data
• Validate data
• Prepare data
• Apply transformations
• Perform calculations
• Determine conditions & KPIs
• Identify & build dimensions
• Identify alerts
• Correlate incoming data based
on defined rules
• Detect events based on
correlated data
• Update fact data
• Update entity & dimension data
• Update analysis collections
• Update event collections
• Manage data level security
Data Acquisition –
Push / Pull data from
variety of sources
Design data
transformations
Conditions & KPI
modeler via rules
builder
Real-time data
correlation
Event detection
via rules builder
Real-time data
mart designer
360o data mart
designer
Define entities and
Import Entity Data
Dimension
modeler
Data Security
designer
StreamCentral Workbench: Big Data Solution Designer
Meta Data
Create Database
Structure
Add Context
StreamCentral Workbench: Big Data Solution Deployment

Model Pull
Data
Sources with
strong REST,
SOAP & DB
Support Push Data
API
Data
Transformat
-ion
Model
Entities &
import static
data
Dimension
modeler
Time &
Location
Standard-
ization
Conditions &
KPI modeler
Correlation
Modeler
Event
Detection
rules on
real-time
data
Real-time &
Historical
analytics
Data mart
modeler
• Software targeted to be used by IT and non IT people to
design and build Big Data solutions
• Can work with batch data (as in traditional Business
Intelligence) or real-time streams (as in Operational
Intelligence)
• Workbench lets analysts model all necessary steps in
building a Big Data Solution
• Data Pull/Push
• Model Transformations
• Model Entities (like customers, patients, products),
import static entity data and define entity relationships to
source data
• Shared dimensions across data
• Condition modeler via business rules to monitor specific
sets of conditions in batch or streaming data
• Evaluate different entities with different sets of
conditions as data flows in
• Specify rules to model how to correlate data streams in
real-time
• Event detection
• Model data marts that aggregate the right data for
association and pattern analysis
StreamCentral Workbench : Software to design
traditional BI/DW & Big Data Solutions
Workbench

Generating insights from data requires context to be
added to the data. This context is a continuous
thread that connects all types of data throughout the
Big Data Solution lifecycle. Four typical examples of
context..
Insight
Who (entities
like customer,
patient)
When (time)
Where
(location)
What
(streaming &
static data
correlation)
• StreamCentral automatically builds
and maintains time and location
dimensions
• Entities can be created and defined in
StreamCentral
• All data in StreamCentral is
continuously and automatically
connected to time, location and
defined entities
• Resultant real-time events and
analytical data marts automatically
inherit this context without need for
any programming or development
work
• This increases the impact and value
of collected data
Converting data to insights by continuously adding context

Auto build
and deploy
DB structure
based on
Workbench
Model
Continuous
Pull with
strong REST,
SOAP & DB
Support
Push Data
API for
streaming
sources
Time &
Location
Standard-
ization
Monitor
conditions
Event
detection
Build data
marts &
continuously
update new
data
In-Memory
Operations
Distributed
Architecture
MPP Support
StreamCentral Big Data Server: Software
that runs Big Data Solutions
• Extends your Business Intelligence strategy by
easily incorporating external data sets
• Introduces integration of real-time data for
event insight to your organization
• Auto-builds database schema (facts,
dimensions, entities, flat tables and more)
• By default, standardizes all incoming data by
connecting it to auto created time and location
dimensions
• Builds event data marts and continuously loads
data
• Builds real-time data marts to help in
understanding associations in data
Continuously loads these analysis data marts
• Deliver real-time event insights to new or
existing operational applications
• Significantly reduces IT overhead in building Big
Data solutions
Big Data Server

18
Solution
Modeling
Model
Deployment
Data Acquisition
Data
Management
Event Detection
Discovery &
Analysis
Bringing it together:
Building Big Data Solutions
with StreamCentral and
partner solutions
1. MPP Columnar Databases : Vertica,
ParStream
2. Microsoft SQL Server
StreamCentral Big Data
Server
StreamCentral
Workbench: Model
Deployment
StreamCentral Workbench:
Big Data Solutions Modeler
Tableau Software,
Microsoft PowerView
StreamCentral Big Data
Server

• Industrial strength, enterprise ready with web scale
characteristics - handles extremely large amounts of
data
• Uses in-memory processing for high speed
• Next generation distributed architecture – allows you to
run on any number of commodity hardware
• Built in redundancy at every layer for high availability
• Easy to use tools to monitor and manage StreamCentral
• Built on Microsoft technology that most enterprises
already have invested in
• Runs on best of breed and latest database technology
from Microsoft SQL Server and HP Vertica
Choose
database from:
19

Why StreamCentral?
• Roadmap to Big Data: StreamCentral is the only solution that enables the evolution
of current practices in Business Intelligence and Data warehousing to now include
external data, event monitoring and real-time insights
• No programming solution modeler: StreamCentral takes a solution approach –
designing and modeling shifts to analysts versus everything being done by
developers or programmed from scratch
• Continuously adds context to data: Any kind of data that is streamed to
StreamCentral, pulled in near real-time or imported via batch is continuously and
automatically connected to time, location and defined entities. This significantly
reduces risk, time and cost associated with building BI/DW and Big Data solutions
• Reduced dependency on specialist skills: No in-depth knowledge needed on HP
Vertica or SQL Server development as StreamCentral builds, deploys and maintains
all internal structures in those environments automatically
• Plays well: Is standards based and agnostic to existing enterprise technologies
• Adaptable: Everything created in StreamCentral can be modified. Makes it easy to
adapt the Big Data solution to changing needs of the business
20

Making a business case for leveraging Big Data
just got a whole lot easier with StreamCentral
70%
Time taken to build Big Data
solutions is drastically reduced
by using StreamCentral
60%
Cost of building Big Data
solutions is drastically reduced
by using StreamCentral
In addition, StreamCentral reduces risk, data
quality issues, specialist skillsets requirements and
complexity in building traditional Business
Intelligence/Data Warehousing or Big Data
solutions

No immediate plans to go Big on Data?
Planning to work primarily with
structured data?
But would like to deliver additional
insights by enhancing your existing
investments in Business Intelligence and
Data Warehousing?

Traditional Data Warehousing
Interrogation of historical data for trend
analysis. Business Intelligence applications
deliver analytics or reports to
management for performance analysis
On-Demand Business Intelligence
Update Data Warehouse continuously with
real-time data. Provides the ability to
analyze data updated in real-time
Allows organizations to monitor fast
moving data for key indicators and events
and immediately act on these insights,
through manual or automated actions
Reporting:-
What did happen ?
Analysis:-
Why did it happen ?
Happens on previously
stored data
(data at rest)
Happens on real-time
streaming data
(data in-flight)
Solution value to businessLower Higher
PerceivedComplexityHigherLower
Event Monitoring:-
What is happening ?
Predictive Analytics:-
What will happen ?
Solutions
On-Demand
BI
Operational
Intelligence
23
Keeping it structured – A roadmap to
extend current investments in BI/DW

Reporting:-
What did happen ?
Analysis:-
Why did it happen ?
stored data
(data at rest)
streaming data
(data in-flight)
Event Monitoring:-
What is happening ?
What will happen ?
Solutions
On-Demand
BI
Operational
Intelligence
Most organizations have
traditionally invested in
this area
In most companies, the scope of
understanding business performance is
limited to historical analysis and rarely
includes real-time understanding of key
events that impact day to day
operational processes

Reporting:-
What did happen ?
Analysis:-
Why did it happen ?
stored data
(data at rest)
streaming data
(data in-flight)
Event Monitoring:-
What is happening ?
What will happen ?
Solutions
On-Demand
BI
Operational
Intelligence
Most organizations have
traditionally invested in
this area
StreamCentral’s
area of focus
25

An approach to working
with real-time data -

Data Layer
Interfaces
Data Processing
Real-Time Insights
Business Solutions
Operational (User)
Internal
Applications and
Data Sets
External Data
Connections to existing
architecture for tapping
data & data streams
APIs
Databases
Enterprise Service Bus
Messages
Push Streaming Data |Pull Data |Format | Standardize | Transform |
Measure | Correlate | Event Detection | Rules Engine | In-Memory
Processing Real-Time Streaming Analytics
Real-Time Event Notification
Historical data that
supports pattern &trend
analytics. New insights are
added in real time
Customer
Experience
Continuous
Improvement
Day to day insights and actions
delivered in multiple mediums to many
users
KPIs
Complaints
Brand –
Protection
1
2
3
4
5
6
!
Access to right information at the right
time along with knowledge base of
actions to perform
Operational Intelligence practices are similar to traditional Data Warehousing practices
27

Data Layer
Interfaces
Data Processing
Real-Time Insights
Business Solutions
Operational (User)
Internal Data Sets External Data
Connections to existing
architecture for tapping
data & data streams
APIs
Databases
Enterprise Service Bus
Messages
Push Streaming Data |Pull Data |Format | Standardize | Transform |
Measure | Correlate | Event Detection | Rules Engine | In-Memory
Processing Real-Time Streaming Analytics
Real-Time Event Notification
Historical data supporting
pattern &trend analytics.
New insights added in real
time
Customer
Experience
Continuous
Improvement
Day to day insights and actions
delivered in multiple mediums to many
users
KPIs
Complaints
Brand –
Protection
1
2
3
4
5
6
!
Access to right information at the right
time along with knowledge base of
actions to perform
Focus of StreamCentral
28

More details on how
StreamCentral works

StreamCentral Workbench Big Data
Solutions Modeler - Inputs
• Data Sources
• Push/Pull
• Data transformations
• Define and import entity data
• Modeling
• Rules for monitoring conditions in data
• Correlation rules to identify related records across data sources in real-time
• Rules for detecting events
• Common dimension modeling
• Data Mart modelers
• Support for Real-time
• Correlation rules to identify related records across data sources in real-time
• Rules for detecting events
• Configure real-time data marts
• 360o data aggregation**
• Define data relationships across data sources
• Configure 360o data marts
• Data level security**
30** Coming Q3 2013

StreamCentral Big Data Server - Output
• Database structure automatically created, updated and managed in Big Data databases like HP
Vertica or SQL Server by StreamCentral.
• The StreamCentral database automatically builds time and location dimensions, fact tables, other
dimension tables, standardizes facts across data sources to the one time and location dimension
as well as connects facts to KPIs. StreamCentral also auto-loads this database from various data
sources into Big Data databases like HP Vertica or SQL Server
• Real-time event notification that can be consumed by operational applications via an API**
• Real-time event alerts
• Data marts that are automatically created, updated and managed by StreamCentral. The data
marts denormalize data into a single table facilitating faster querying and analysis of data
• Real-time analytical data marts built that aggregates events and data across data sources to better understand
conditions that influence events
• Real-time event data marts that bring together all relevant information for a single event
• 360o data marts for association and pattern analysis**
31** Coming Q3 2013

Sensors
Weather
Enterprise
Applications
Data Visualization
(Reporting, Analytics,
Dashboards)
Correlates
Data
Generates Key
Performance
Indicators
Uncovers
Events
Consumes real-
time or static data
OR Pulls data from
various data
sources and
applies
transformation and
standardization
rules
Model Deployment
Auto-builds database schema
Auto-loads database
Builds and continuous loads data to
event data marts
Builds and continuous loads
Analysis Collections
Publishes event data that can be
subscribed by Operational
Applications
Devices
Auto-build Database
Schema
360o Data marts and real-time data
marts
Event Data Marts for every event along
with its context as denormalized flat
tables
StreamCentral
Push
Push
Massively Parallel Processing Systems - Vertica
RDBMS – MS SQL Server
Publish event data
to operational
applications – Web,
mobile or desktop
StreamCentral Workbench – Big Data
Solutions Modeler
Collate Raw Data (Push/Pull) – Real-Time or Static
Model data standardization and transformation
rules
Define business entities and connect raw data to
business entities
Model Dimensions
Model conditions to monitor across data sources
Assign different conditions to different entities
Model Correlation Rules
Model events and specify context to add to events
Model analytical data marts auto built by
StreamCentral
Enterprise
Applications
API
Traffic
API
API
API

builds two distinct types of analytical data marts
360o Data Marts** Real-Time Data Marts
• Defined: Easily bring together and aggregate data
across data sources to get 360o insight. Analyze
associations in data to determine patterns that
impact business performance
• Define data mart structure by choosing the right
set of attributes from data sources, KPIs, attributes
from entities, and dimensions in the Workbench
• . StreamCentral auto-builds the data mart
• Standardize data across time and location
• Update data mart at pre-defined intervals
StreamCentral Data Marts are denormalized flat tables – Why?
• Defined: Aggregate real-time events and bring
together data across data sources to analyze
conditions that existed when events are detected
• Standardize data across time and location
• Define data mart structure by choose the right set
of attributes from data sources, KPIs, events,
attributes from entities, and dimensions .
StreamCentral auto-builds the data mart
• Once data gets correlated in real-time data mart
gets updated with appropriate insights
• Technology advancements in columnar data stores, bit map indexes, column indexes make it
possible to scan and query large amounts of data in a single table
• Takes advantage of distributed architectures to scale out using commodity software
• Supports :
• SQL Server columnar indexes
• Vertica MPP
** Coming Q3 2013

StreamCentral Real-Time Operational Intelligence
• Data Sources
• Import initial data load
• Push data to StreamCentral API
• Pull data from data sources at defined intervals
• Apply transformations on the data in flight
• SQL Server, Oracle, My SQL, REST API, SOAP Web Service, LDAP
• Auto connects data to time and location dimension
• Model entities. Connect data sources to entities
• Model measures and KPIs
• Model standard dimensions
• Model real-time correlation rule (to identify related records across data sources in real time)
• Model Events
• Events based on real-time correlation rule
• Event Data Mart (automatically gets created when event is detected)
• Requires real-time correlation
• Brings together all data across data sources that were captured at the time the event was detected
• Model Real-Time Data Marts
• Requires a real-time correlation rule
• Update real-time data mart with streaming correlated data
• Define attributes that make up the real-time data mart definition. Select subsets of information from : specific attributes from data sources, KPIs, events, entity
attributes, dimensions, time and location
• Edit real-time data mart definition

StreamCentral 360o Data Aggregation**
• Data Sources
• Import initial data load
• Pull data from data sources at defined intervals
• Apply transformations on the data
• SQL Server, Oracle, My SQL, REST API, SOAP Web Service, LDAP
• Auto connects data to time and dimension location
• Model entities. Connect data sources to entities
• Model measures and KPIs
• Model standard dimensions
• Model 360o Data Marts
• Model 360 view query (define relationships across data sources to aggregate data)
• Schedule batch update interval (typically hours)
• Define attributes that make up the analysis collection. Select subsets of information from : specific
attributes from data sources, KPIs, entity attributes, dimensions, time and location
• Edit and update data mart definition
• Define data level security
** Coming Q3 2013

Data formats supported :
• XML
• JSON
• String
Data Sources supported :
• Database
• Microsoft SQL Server
• Oracle
• My SQL
• REST API
• SOAP API
• LDAP
• Specify transformation rules to data
that is applied to data in flight
• Specify parameters when calling APIs
• Auto fills location parameters based
on location data stored in the
database about entities
• Auto creates tables in the backend
database for data source data
Pull Data from Applications Push data to StreamCentral
• StreamCentral REST API available to
stream data to StreamCentral –
stream data from agents, sensors,
probes, devices
• Specify transformation rules to data
that are applied to the data in flight
• Auto creates the tables in the
backend database for source data
StreamCentral Databases
• Supports Microsoft SQL Server and
HP Vertica
• Auto creates data structures in the
database for source data
• Auto creates fact tables, dimensions,
flat tables for event analysis and flat
tables for pattern and association
analysis
• Data level security
StreamCentral Analytics
• Device friendly visualization
• Powerful portfolio of
visualization tools
• Ability to embed in custom
applications
• In-memory operations for fast
querying
StreamCentral Reports
• Role based security
• Subscribe to reports
• Ability to embed in custom
applications
• Export reports to various
formats

Transformation Description
LTRIM Removes all white spaces from the left
RTRIM Removes all white spaces from the right
Ignore Space Removes all white spaces from left, middle or right
Ignore Special Characters Returns string after ignoring all special characters
Contains Search for specific characters
Substring Extract a substring from a string
Left Removes the left part of a character string
Right Removes the right part of a character string
Replace Replaces specified string with another string
Startswith Search for a starting character
Endswith Search for an ending character
DoesNotContain Search for specific characters
Remove Remove specified characters or words from string
Range Search for a range
RoundOff Rounds off decimal value to a specific length
StreamCentral
Transformations
• Easy to use transformations
• Multiple transformations can
be executed on one attribute

StreamCentral
Physical Architecture

StreamCentral Collector
Windows Server 2012, .Net Framework 4.5, MSMQ
StreamCentral Stream Processing Engine
Windows Server 2012, .Net Framework 4.5,, MSMQ
StreamCentral Stream Correlation Engine
StreamCentral Data Engine
All components can run on
one machine
Every component can run
on more than one machine
StreamCentral Cache Cluster
Windows Server 2012, .Net Framework 4.5, AppFabric
StreamCentral Metadata database
Windows Server 2012, Microsoft SQL Server2008 R2
or Microsoft SQL Server 2012
StreamCentral Database and data marts
Option 1:
Windows Server 2012, Microsoft SQL Server2008 R2
or Microsoft SQL Server 2012 or SQL Server Parallel
Data Warehouse
Option 2
Linux, HP Vertica
StreamCentral Analytics
Windows Server 2012, Tableau Software
StreamCentral Physical Architecture and
Software Requirements

1 server for StreamCentral Components:
Collector, Stream Processing Engine, Correlation Engine, Data Engine
Characteristics of this server : Processor dependent therefore the higher
the number of cores the better, medium cache and low disk storage
Software: Windows Server 2012, .Net Framework 4.5, MSMQ
1 server for cache
Hardware characteristics: : Cache
dependent therefore more memory the
better. Medium CPU and low disk storage
Software : Windows Server 2012, .Net
Framework 4.5, AppFabric
1 server for StreamCentral Meta Data
Database, data mart storage and reporting
Hardware Characteristics:: High CPU, High
Memory and High Storage
Software : Windows Server 2012, SQL Server
1 server for StreamCentral Meta Data
Database and reporting
Hardware Characteristics:: Medium CPU,
Medium Memory and High Storage
Software : Windows Server 2012, SQL Server
OR
1 server for StreamCentral data marts
Hardware Characteristics:: High CPU, High
Memory and High Storage
Software : Linux, HP Vertica
+
StreamCentral suggested minimum system configuration

How does StreamCentral
fit within your enterprise
technology architecture?
41

Data Sources Method of Access
StreamCentral -
Read data from
Application
Application - Read data
by subscribing to
StreamCentral Real-Time
Event API
Application - Read data
by querying
StreamCentral database
Enterprise Applications X real-time X
Using Web Service or REST API X real-time
Using database query X
Enterprise Service Bus X real-time X
via Web Service or REST API
X real-time
via subscribing to messages
X real-time
Enterprise Data
Warehouse
via database query
X X
Point databases
via database query
X X
LDAP via database query X
External Data Sources via Web Service or REST API X real-time 42

Sensors
Weather
Devices
Traffic
Custom
ApplicationsMainframe
Business Services
Enterprise Service Bus - Messaging / Mediation / Orchestration / Security
Business
Process
Business
Process
Business
Process
Composite
Application
Composite
Application
Composite
Application
Auto-build Database
Schema
Analysis Collections – Data marts as
denormalized flat tables
Event Collections – Data Marts for
every event along with its context as
StreamCentral Engine
StreamCentral Workbench
Collate Raw Data (Push/Pull)
Standardize Data
Define Business Rules
Define Correlation
Define events
Define analytical data marts
auto built by StreamCentral
Historical
Analysis
Real-time event data published
to operational applications
and dashboards
Columnar databases with Bit Map indexes – ParStream
StreamCentral as part of an Enterprise Service Bus architecture
API
ERP
Push / Pull
43

Sensors
Weather
Devices
Traffic
ERP
Custom
ApplicationsMainframe
Business Services
Enterprise Service Bus - Messaging / Mediation / Orchestration / Security
Business
Process
Business
Process
Business
Process
Composite
Application
Composite
Application
Composite
Application
Auto-build Database
Schema
Analysis Collections – Data marts as
Event Collections – Data Marts for
every event along with its context as
StreamCentral Engine
StreamCentral Workbench
Collate Raw Data (Push/Pull)
Standardize Data
Define Business Rules
Define Correlation
Define events
Define analytical data marts
auto built by StreamCentral
Historical
AnalysisEnterprise Business Intelligence
System
Columnar databases with Bit Map indexes – ParStream
StreamCentral and Enterprise BI as part of an Enterprise
Service Bus architecture
Real-time event data published
to operational applications
and dashboardsAPI
Push
Pull
Push / Pull
44

Thank you
for your time
Contact us for a demonstration
Stephen Wells
CEO - Virtus IT Ltd
E: stephen.wells@virtus-it.com
M: +44 77 111 30879
Raheel Retiwalla
CTO - Virtus IT Ltd
E: raheel.retiwalla@virtus-it.com
M: +1 617 901 8370
A trusted partner
45

StreamCentral for the IT Professional

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