The document discusses StreamCentral, a real-time business intelligence and data analytics platform. It allows users to build high-impact business solutions quickly using streaming and static data without extensive technical skills. StreamCentral automatically builds and manages an information warehouse with real-time updated data marts. It incorporates streaming data, events, entities, and environmental data to provide context. The platform also handles security, metadata management, and supports various data storage technologies.

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Business Powered By Data
Overview of the
StreamCentral
Information System
Raheel Retiwalla
raheel.retiwalla@virtus-it.com

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Goals of the StreamCentral Information
System
• Build high impact business solutions from 0 – finish in days or weeks
• Move traditional batch based Business Intelligence to real-time Business Intelligence
• Account for streaming data and the need to understand and analyze streaming data
in real-time
• Easily connect to the cloud
• Account of traditional static data
• Include events as a core part of real-time analytics
• Require little or no knowledge of Business Intelligence design patterns, practices or
terminology
• Introduce design automation that continually infers relationships in data to add
additional value to collected data
• Take advantage of Big Data data stores like NoSQL, MPP and Hadoop with little or no
knowledge of the underlying technology

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Collect Stream Data Collect static data
Stream event processing Data transformation
KPI & Conditions Correlation
Connect data to business entities Standardize time & location
Auto build data marts that are
updated in real-time
Auto build data marts that
are updated periodically
• One product that already comes built in
with integrated components that work
seamlessly together
• High Automation
• No coding required
• Focus on the solution and not on integration
• Can be used by data scientists and
developers
• Does not require extensive specialist skill
Building a Big Data Business Solution WITH StreamCentral
Time
Latest innovation
in Big Data technologies
Hadoop
MPP NoSQL Caching

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StreamCentral
BI Server – Run with Scale
Information Warehouse Manager – Auto Build
Workbench – Easy to Design
Rapidly industrialize
the use of data by
designing, building
and running real-time
business intelligence
and Big Data
solutions with
StreamCentral
Functional
Application
Event Driven
Predictive
Analytics
Industry
Application
BI / Reporting
Data Exploration /
Visualization
Analytic Applications
Association
Analysis
Business Event
Detection
Data Publishing:
SQL Server,
Vertica, MongoDB
Data ExportData Collection Data Processing Caching
Security Designer Systems Management
Solution Designer (Data
Consumption, Data
Transformations, Conditions,
Event, Correlation)
API Designer
De-normalized schema
generation for data marts
Security schema generationMeta Data Manager
Normalized schema
generation for Fact and
Dimensions
Auto generate database design, auto generate database and application code, infer relationships in data
StreamCentral - Big Data Plumbing
Focus of this
presentation

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The StreamCentral Information
Warehouse
A dynamic warehouse automatically built and continually managed by
StreamCentral

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The SC Information Warehouse
• Facts – Two types of fact tables
• Regular Fact Tables
• Fact tables integrate the dimension key internally automatically
• Has it’s own surrogate key
• Security Factless Fact tables
• Dimensions
• Custom Dimension
• Entities
• Systems Dimensions
• Environmental Facts
• Treated as facts and dimensions

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The StreamCentral Fact
Definition: A fact is a measurable value which represents an actual fact from a
specific business process or a system. Examples include sales data, video
performance, contract data, health record, reading from a sensor

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Facts Overview
• Facts are created as data sources in StreamCentral
• One time load
• Periodic load – assign frequency
• Pull - Supports Oracle, SQL Server, Flat Files, Excel, MySQL
• Push – API available to push data into a StreamCentral data source in
real-time (JSON and XML) – Useful to have real-time “facts” being
pushed directly to StreamCentral
• Dimension surrogate keys get appended to fact in real-time in parallel
to the dimension update process. Key Feature: Allows system to be
updated and available in real-time

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Security Fact Table
• User can specify security rule for data access at the attribute level for
a given security role using the Workbench
• StreamCentral automatically creates a Factless Fact Table – Key
Feature
• The Fact table records the role and the sequence of a record in a data
source
• As soon as a new record is entered into a source, the security
sequence along with roles that have access to the sequence is
maintained in this table

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The StreamCentral Dimension

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Definitions
• Dimension – A dimension is a category of information that can be used to further
understand a fact. Dimensions are usually connected to fact tables to give them
additional context and meaning. For example, the time dimension
• Dimension Attribute: A unique level within a dimension. For example, Month is an attribute
in the Time Dimension.
• Hierarchy: The specification of levels that represents relationship between different
attributes within a dimension. For example, one possible hierarchy in the Time dimension is
Year → Quarter → Month → Day.
• Conformed Dimension – A dimension that has the same meaning and content
when connected to different fact tables. Examples of conformed dimensions
include customer, product, employee, partner
• Role Playing Dimension - Dimensions are often recycled for multiple applications
within the same database. For instance, a "Date" dimension can be used for
"Date of Sale", as well as "Date of Delivery", or "Date of Hire". This is often
referred to as a "role-playing dimension".

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StreamCentral Dimension Overview
• Dimension are created as data sources
• One time load
• Periodic load – assign frequency
• Dimensions can be built on multiple data sources
• Dimension gets updated as soon as data source is updated with new data
• Pull - Supports Oracle, SQL Server, Flat Files, Excel, MySQL
• Push – API available to push data into data source in real-time
• Supports conformed dimensions, degenerate dimensions and role-playing
dimensions
• Dimension data is available in a distributed cache to allow adding of context to
real-time data during ingestion – Key feature
• Dimension generates surrogate key based on the selected business key from the
fact table

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Custom Dimensions
• In conformed dimensions it is the same business key across multiple fact tables
• Build dimension from multiple sources
• Build a dimensions with attributes from multiple sources
• Benefit: No need to standardize data before it gets to SC. SC transforms and standardizes data
automatically for a dimension
• Handling changing attributes – Key Feature
• Fixed Attribute – Attribute always has a fixed value. Does not change. This is the default
characteristic of an attribute
• Historical Attribute – Attribute can change. Once attribute changes, new record is created to
show the latest and the historical representations. Start Date, End Date and Surrogate Key.
Benefit: Historical record of every change automatically created and maintained by SC
• Changing Attribute – Attribute value can change. The change will not create a new record. If
historical records exist, then option is provided to apply the change to historical records as
well
Benefit: Changing values of existing attributes without having to create historical records

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System Dimensions
• Time
• Built as a role-playing dimension
• Any granularity of the timestamp in the fact will be linked to the time dimension –
Key Feature
• Year, Quarter (default Jan), Month Number, Month Name, Day Name, Date
• Hours, Minutes, Seconds
• Location
• Built as a role-playing dimension
• Any granularity of the timestamp in the fact will be linked to the location
dimension
• Support three different representations of Latitude/Longitude
• Support reverse geo-coding with Microsoft Bing or Google Maps API
• Country, State, Region, City, Area, Street Name, Zip Code/Postal Code, Lat/Long –
Key Feature

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• StreamCentral auto creates time and location dimensions without the need to explicitly define
them. As data sources have fact tables that contain time or location data, StreamCentral starts
conforming time and location dimensions
• Extended data types allow very specific association of a variety of time and location based
attributes in fact tables
• Time and Location data types can be assigned to attributes in entities, regular data sources and
environmental data sources
• For every incoming attribute that is associated with one of the special time or location data types,
StreamCentral looks to see if a specific record for that data already exists in the dimension. If not,
it creates a new record for that value. If it exists already, then the key value of that data is
substituted in the data source. This happens in real-time
• Time and location data is stored in the database and in a distributed cache. Real-time lookups are
done against the data stored in the cache
• StreamCentral can dynamically feed time or location data to REST or SOAP based web services
from these dimensions
• StreamCentral supports standardizing location data for any geographic level and supports ability
to standardize for specific radius
A note on time and location data

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Entities
• Definition: An entity represents a group of people or groups of things, that
incoming data is directly connected to. Examples include departments,
customers, site, products etc. By defining entities you tell StreamCentral how
distributed data is connected to things core to your business. This is an important
and unique differentiator within StreamCentral. With this capability raw data is
immediately enriched with your business context at the time of ingestion
• Treated same like custom dimensions with additional capabilities
• Entity data can be editable in StreamCentral
• Multiple attributes in an entity can map to identifier attributes from different
data sources. Benefit: A data source carrying an identifier that maps to an entity
attribute will instantly have access to all the entity data during source data
ingestion
• Ability to specify multiple locations for an entity

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Environmental Fact

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• This source of data is used to add context (a Dimension) and measure performance (Fact Table) –
These are also called environmental data sources:
 Example typically include external data that adds context about external factors in play
 Does not have to be connected to the entities directly. StreamCentral will use implicit
relations with time and location dimension to tie environmental data to other enterprise
data. For example, consider an environmental data source called weather. Weather has
location information associated with it. There are two entities namely “Customer” and
“Tower”. Both also have location information associated with them. StreamCentral
standardizes all three to the location dimension but StreamCentral also implicitly connects
Customer to weather and Tower to weather because weather was created as an
environmental data source. Now when analyzing data, StreamCentral will be able to provide
real-time or historical context as to what the weather is where the customer is and what the
weather is where the tower is
 Great to use in data marts for analyzing associations with other data
 Can be used in event detection as part of conditions set and to evaluate events
Types of data sources: Environmental

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Environmental Facts
• Same as regular StreamCentral fact tables with additional
functionality
• As a Fact
• Attributes can be selected as measures to be included in data marts
• As a dimension
• It connects to all fact tables on time and location attributes automatically

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StreamCentral 360o
Data Marts

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StreamCentral 360o
Data Mart Overview
• Fully automated system from building data mart, modifying data mart
and continually keeping data mart refreshed with new and updated
data
• The StreamCentral intelligence brings together all related and
relevant pieces of information to the user during creation of the data
mart using the StreamCentral Workbench
• Data Mart creation and modification involves no coding.
StreamCentral intelligence takes care of alerting user of possible
relationships that can be included in the data mart for wider
association analysis

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360o
Data Mart Overview
• StreamCentral Real-time 360o
Data Mart
• Data Marts updated in real-time – Data is updated in the data mart in parallel with data being
updated in the information warehouse (fact table)
• Data Mart connected to an event – Auto builds a 360o
view of the event
• Data Mart connected to the entire data warehouse
• Storage Types:
• Custom Start date with no end date
• Rolling Window (fixed amount of time that keeps moving)
• StreamCentral Historical 360o
Data Mart
• Data Marts updated on demand and updated on defined frequency
• Two types:
• Built using LiveJoin (inherits the relationship definition defined to detect events)
• Built using OnDemand Join (define custom relationships in data)
• Storage Types:
• Custom Start date with no end date
• Rolling Window (fixed amount of time that keeps moving)
• Snapshot – Custom Start Date with Custom End Date

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Types of Data Mart structures
• Pivot
• Keeps KPI’s and alerts as columns
• Header Detail
• Fact data is maintained as a single record in header table
• Any KPI’s and alerts that make the data duplicate will be maintained in a
detail table
• Flattened
• Fact data is duplicated in a flattened structure – all facts, KPIs and alerts in
one flattened structure

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Logical and Physical Model
• StreamCentral builds both the logical and physical models for the
information system
• The physical model is supported for:
• Microsoft SQL Server
• HP Vertica
• MongoDB (Q3 2014)
• Hadoop (Q3 2014)

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Thank you
For more information please contact:
USA:
Raheel Retiwalla
CTO - Virtus IT Ltd
e: raheel.retiwalla@virtus-it.com
m: +1 617 901 8370
UK:
Stephen Wells
CEO - Virtus IT Ltd
e: stephen.wells@virtus-it.com
m: +44 771 113 0879
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