Numberate master presentation

NUMBERATE 8.0
Rhino
Technology Overview
Numberate Data Warehousing & Business Intelligence Solutions December 2, 2014

Numberate Rapid Warehouse Solution
The Numberate Rapid Warehouse Solution is a data integration
solution that maximises the cost efficiency of data oriented business
intelligence environments.
It builds on over a 15 Years of experience delivering these solutions to
a range of corporate and government clients who needed to leverage
business growth from their organisations underlying data assets.

The Numberate Rapid Warehouse Solution differs from other solutions
because of its flexibility, its non-proprietary architecture and its speed of
implementation.
Based around the concept of business rules, it incorporates a high
degree of reusability allowing customers to leverage and extend their
existing investments in data warehousing and business intelligence.

The Numberate Rapid Warehouse Solution is the right solution to meet the needs
of your business for going forward. You need to give your IT professionals
tools that;
1. Allow them to focus on your business rather than our technology.
2. Provide options and alternatives rather than mandating yet another upgrade or
add on.
3. Deliver in weeks rather than months.
4. Are driven by your business rules and processes rather than driving your
business broke.
Data Warehousing & Business Intelligence Solutions

Solution Requirements: Business
THE CHALLENGE
 Business users don’t
understand their data
 Information is power
“Its distribution is
threatening”
 New information, or new
views, changes
requirements
 Timeliness is everything
“Yesterday’s issues are
meaningless today.”
THE NEEDS
• Involve business to
– Confirm solutions
– Change their
mind
• Rapid development
through automation
• No penalties for
rework
• Return control of data
and distribution to the
business

Solution Requirements: ETL
THE CHALLENGE
 Data from disparate systems doesn’t match
 Cross systems business rules difficult to
model
 Dirty data blows out exception handling
 Users change their mind
 Upstream systems constantly change
THE NEEDS
 Capture business rules once
 Allow users to prototype inside the ETL
process
 Automate the process
• Maintaining the rules
• Defining the transforms
• Defining the target environment
 Provide capacity for rework

Solution Requirements: Project
THE CHALLENGE
 Development program midstream
 Fixed price contract
 Existing tool sets
 Existing development partners
 Cost constrained project
 Project schedule pressure
THE NEEDS
 No upfront investment
 Fixed price
 Reduce development schedules
 Reduce cost and time for work packages
 Provide alternate approaches
Data Warehousing & Numberate Data Warehous iBng u& Bsuisnineesss Inste llIignentcee Slloilugtioensnce SolDuecteimobner s2, 2014

A different approach
Value Proposition
The Numberate Rapid Warehouse Solution automates the process of building
and maintaining a warehouse
→ Open, off the shelf, commodity technologies.
→Solution prototyped before decision.
→ Performance benchmarked before full implementation
→ Independent of target technology

A different approach to building
a data warehouse
Solution Space
Warehouse Development Environment
 Rapid transformations
 Prototyping
 Maintenance
Warehouse Integration Environment
 All major tools and RDBMS’s
Warehouse Enhancements
 Storage
 Front end
 Backup archive and recovery

Numberate Architecture
Front End
Power Users
Operational
Systems
Operational
Systems
Detailed Data
Cache or Store
ODBC
Standard Reporting Tools
Logical
Views
RDBMS API
RDBMS
Repository
or Data
Warehouse
RDBMS ODBC
ETL Environment
Operational
Systems

Numberate Matrix
Physical
Data
Elements
Additional
Load
Code
Raw
Data
Raw Data
Load
Execute
Data
Code
Generation
Engine
User Data Request
Result Set
End User
Logical
Database
Definition
Request
User Data
User
Query
Builder
Result
Metadata
Manager
User
Element
Security
Intersection
Numberate Matrix
Numberate Matrix Manager

Technology Features
 Platform Transparency
 Uses inexpensive, standard components:
 hardware,
 software
 Disk
 Uses any storage devices orthogonally
 disk, tape, CD ROM,
 Independent of front end tools and back end data stores
Horizontally scalable on any platform
 Does not require identical hardware

Value Proposition
Single metadata
repository
 Extract and transform
data
 Define warehouse
environment
 Define third party
environments
 Maintain, schedule and
operate warehouse
Scalable storage
solution
 Terabyte enabled
 Uses standard server /
disk environments
 Proprietary partitioning
technology

Metadata- Magic or Myth
 Multiple standards
 Multiple repositories
 Residing in multiple separate systems
 Addresses only a partial view of the environment
 Not integrated with operational environments
 Usually managed as a task additional to the
implementation

Metadata
Business
Rules
Numberate
Metadata
User
Regts
Source
database
Existing
Metadata
SOURCES
Numberate metadata
generates efficient,
error free code for
the entire warehouse
environment
Manual
Inputs
Existing
Datamart warehouse
USES
Extraction
Routines Define
Numberate
Metadata
Staging
area
Transformation
programs
Define
warehouse
tables
Generate
tests
Define
table
loads
Load
frontend
metadata

Numberate Metadata – It’s Alive..
 Active
 Automated
 Accurate
 Essential
 Self documenting
 End to end
 Reusable
 Extensible
 Pervasive
 One and done approach to
build a warehouse
 The tool manages the
project work
 Reduces effort, time and
cost
 Maintain the warehouse
through the metadata

Numberate Kernel
Numberate Unplugged
Code Repository
RWS
Admin
Interface
RWS
User
Interface
Metadata activates
program objects
Metadata
Repository
3rd Party
Metadata Interfaces

Staging & Test area
 Source transactional data is partitioned for parallel processing.
 Partitioned at table level.
 Can be repartitioned for performance tuning with minimal
rework.
 Parallel operations come from standard systems multi
threading.
Summaries
And Derived
Data
Standard Operating System
Java Environment
Numberate Environment

Unstrained Integration
 By architecture
 Hub and spoke
 Enterprise Focused
 Data Marts
 Normalised / Schemas
 ETL
 Direct database load
 Parallel ETL processes
 Auditable
 Low maintenance costs
 Prototyping/ Front end
 Derived from metadata
 Low cost
 Rapid development times

Integration Services
Front End Environments
New Datamarts
Emerging Technologies
Numberate Integration Layer
DW
Legacy Repositories
Data & Event Bus

Data quality makes
inferred relationships
difficult to code
Transformation
Engine(s)
Transformation
Engine(s)
Typical Transformation Architecture
Transformation Engines are
single stream, linear processes
Target logical
models cannot be
validated by users
without the data
Disparate systems
cannot sustain
desired relationships
Data Architectures
and models may need
to be radically altered:
Performance
Data Quality
Business needs

Traditional Transformation Process
Iterate the Stream
Oracle
Source
System
Oracle
Source
System
Teradata
Target
System
Teradata
Target
System
Cobol
data
extraction
process
Cobol
data
extraction
process
Fastload
or
Multiload
Fastload
or
Multiload
Teradata
SQL
Programs
Teradata
SQL
Programs
Transformation processes occur at all
points
• Difficult to plan
• Hard to code manually
• Very hard to change or rework
Single stream approach
• Limits transformation models
• Requires many iterations.

Traditional Transformation Process
1. Use the warehouse for ETL
2. Co-ordinate many environments
3. Co-ordinate multiple teams
4. High project management load
5. High documentation load
6. Target environment is not independent of ETL stream

Non Proprietary Architecture
The Numberate Rapid Warehouse Solution leverages the power
of modern multi- tasking operating systems and advanced
storage solutions to provide scalable data environments that can
operate in near real time and provide rapid access to complex
queries in business time.

ETL Strengths
 ETL has full units of parallelism
 Multi staged ETL developed as one process
 Implemented as different streams
SAS
Utilities
SQL
C / Cobol
 Sophisticated high speed pattern
matching
 Matrix Input Loader
 Generic Data Conversion Module
 Saves tens of thousands of lines of code
 Generated code is error free
 Metadata driven
 End to end – including warehouse definition

Elements of Compatibility
 STORAGE
 Disk / tape / CD-rom
 Appear as RDBMS views
 Low cost storage
 Suitable for transactional histories
 DATA ARCHITECTURE
 Normalised data models
 Star Schemas
 Snowflake Schemas
 DATA STORAGE
 Mix and match capability
 Oracle / DB2 / Teradata/SAS
 Native file systems

Meeting Your Needs
End to end automation reduces time and cost
-Provide faster, cheaper work packages
Capture existing sources of metadata
-Warehouse work already completed
-Definitions in old repositories and data marts
Provide you with more options
-Transformation can be done on
• Mainframe
• Data Warehouse
• Unix
-Move between platforms as run time option
Solution includes a RAD / prototyping environment
-Model and confirm business requirements
-More accurate reporting specifications

If your organisation is ready to move to a
new level of robust solutions that actually
reduce your IT spend, then you are
probably ready for the

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