streamcentral for the it professional

http://www.virtus-it.comA trusted partner

Software to model & build Business Intelligence & Big Data Solutions

http://www.virtus-it.com 2

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

http://www.virtus-it.com

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101010

101010

3

BIGDataToday 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 DetectionBig Data Solution

Modeling & Model Deployment

Stream Processing &Batch Data Acquisition

Blocks for building Big Data Solutions

ERP

Internal & External Applications Data Stores

101010

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?

A few example scenarios: A few example scenarios:


Telco’s Core IMS Network Data Data, Voice & Video

Performance DataData, Voice & Video Performance Data

Data from Telco Towers

Weather DataTraffic

IncidentsPopulation Data

Data Stream

weatherunderground

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


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


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


• Information Discovery

• Data Classification• Analytics• Querying• Visualization

Solution Modeling

Model Deployment

Data Acquisition

Data Management

Event Detection


Big Data Solution Lifecycle – Tasks Detailed


9

Solution Modeling

Model Deployment

Data Acquisition

Data Management

Event Detection


1. Hadoop - MapReduce2. MPP Columnar Databases like Neteeza, Vertica, ParStream3. NoSQL – MongoDB, Cassandra4. 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


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


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.


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StreamCentral Workbench: Solution Designer

StreamCentral Workbench:

Model DeploymentData Collection

Data Processing Correlation Data

PublishingData

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


DatabaseREST/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 DataCreate 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


Bringing it together: Building Big Data Solutions with StreamCentral and partner solutions

1. MPP Columnar Databases : Vertica, ParStream

2. Microsoft SQL Server


StreamCentral Workbench: Model Deployment

StreamCentral Workbench: Big Data Solutions Modeler

Tableau Software, Microsoft PowerView



• 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


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 WarehousingInterrogation of historical data for trend analysis. Business Intelligence applications deliver analytics or reports to management for performance analysis

On-Demand Business IntelligenceUpdate Data Warehouse continuously with real-time data. Provides the ability to analyze data updated in real-time

Operational Intelligence

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

Perc

eive

d Co

mpl

exity

Hig

her

Low

er

Event Monitoring:- What is happening ?

Predictive Analytics:-What will happen ?

Traditional Data Warehousing Solutions

On-Demand BI

OperationalIntelligence

Keeping it structured – A roadmap to extend current investments in BI/DW




Happens on previously stored data (data at rest)

Happens on real-time streaming data (data in-flight)


Perc

eive

d Co

mpl

exity

Hig

her

Low

er




On-Demand BI


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





Happens on previously stored data (data at rest)

Happens on real-time streaming data (data in-flight)


Perc

eive

d Co

mpl

exity

Hig

her

Low

er




On-Demand BI


Most organizations have traditionally invested in this area

StreamCentral’s area of focus



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

APIsDatabases

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 NotificationHistorical data that supports pattern &trend analytics. New insights are added in real time

CustomerExperience

ContinuousImprovement

Day to day insights and actions delivered in multiple mediums to many users

KPIsComplaintsBrand –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


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

APIsDatabases

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 NotificationHistorical data supporting pattern &trend analytics. New insights added in real time

CustomerExperience

ContinuousImprovement

Day to day insights and actions delivered in multiple mediums to many users

KPIsComplaintsBrand –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


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**** 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**

** 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

StreamCentralPush

Push

Massively Parallel Processing Systems - VerticaRDBMS – 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

AP

I

TrafficA

PI

AP

I

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 2Linux, 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 EngineCharacteristics of this server : Processor dependent therefore the higher the number of cores the better, medium cache and low disk storageSoftware: Windows Server 2012, .Net Framework 4.5, MSMQ

1 server for cacheHardware characteristics: : Cache dependent therefore more memory the better. Medium CPU and low disk storageSoftware : Windows Server 2012, .Net Framework 4.5, AppFabric

1 server for StreamCentral Meta Data Database, data mart storage and reportingHardware Characteristics:: High CPU, High Memory and High StorageSoftware : Windows Server 2012, SQL Server

1 server for StreamCentral Meta Data Database and reportingHardware Characteristics:: Medium CPU, Medium Memory and High StorageSoftware : Windows Server 2012, SQL Server

OR1 server for StreamCentral data martsHardware Characteristics:: High CPU, High Memory and High StorageSoftware : Linux, HP Vertica

+

StreamCentral suggested minimum system configuration


How does StreamCentral fit within your enterprise technology architecture?


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


Sensors

Weather

Devices

Traffic

Custom Appl ica ti on sMai n fra me

Busi ness Servi ces

Enterpr i se Servi ce Bus - Messa gi n g / Med ia ti on / Orchestra ti on / Secu ri ty

Busi ness Process

Busi ness Process

Busi ness Process

Composi te Appl ica ti on




Analysis Collections – Data marts as denormalized flat tables

Event Collections – Data Marts for every event along with its context as

denormalized flat tables

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

Massively Parallel Processing Systems - VerticaColumnar databases with Bit Map indexes – ParStreamRDBMS – MS SQL Server

StreamCentral as part of an Enterprise Service Bus architecture

AP

I

ERP

Push

Pull

Push / Pull


Sensors

Weather

Devices

Traffic

ERP

Custom Appl ica ti on sMai n fra me

Busi ness Servi ces

Enterpr i se Servi ce Bus - Messa gi n g / Med ia ti on / Orchestra ti on / Secu ri ty

Busi ness Process

Busi ness Process

Busi ness Process





Analysis Collections – Data marts as denormalized flat tables

Event Collections – Data Marts for every event along with its context as

denormalized flat tables

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

Massively Parallel Processing Systems - VerticaColumnar databases with Bit Map indexes – ParStreamRDBMS – MS SQL Server

StreamCentral and Enterprise BI as part of an Enterprise Service Bus architecture

Real-time event data published to operational applications

and dashboardsA

PI

Push

Pull

Push / Pull


Thank you for your time

Contact us for a demonstration

Stephen WellsCEO - Virtus IT LtdE: [email protected]: +44 77 111 30879

Raheel RetiwallaCTO - Virtus IT LtdE: [email protected]: +1 617 901 8370

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