SlideShare a Scribd company logo
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
DYNAMIC DATA AUTOMATION PLATFORM
Data Warehousing design, development and support just got a lot faster.
FALL 2015
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
DATA AUTOMATION REALITIES…
Warehousing Projects…
… rarely start with a complete understanding of the full
scope of business requirements and implications of the
necessary data.
… are frequently architected based on what the technical
resources are most comfortable completing. As a result,
every project is developed in different ways, making on-
going support difficult.
… regularly start from scratch, demanding developers
rebuild/repurpose common functions.
Datawa.re…
… enables frequent and rapid changes based on a
flexible, table-driven architecture that enables frequent
and rapid changes… without the burden of massive
regression testing.
… delivers a proven, scalable, sustainable and
extensible architecture and approach that empowers
technical resources to create near-term wins… and long-
term supportability.
… accelerates productivity with a common foundation
and methodology enabling developers to immediately
begin configuring processes, mapping data, and defining
business logic.
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
DATA AUTOMATION SOLUTIONS…
How does Datawa.re impact the business?
 Rapid Set-up
 Flexibility in Design
 Accelerated Development
 Continuity in Approach
 Consistency Across Resources
 Improved Support
 Increased Responsiveness
 Reduced Resource Requirements
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
FASTER
CHEAPER
BETTER
DESIGN &
ARCHITECTURE
DEVELOPMENT &
DEPLOYMENT
TIME-TO-MARKET /
SUPPORTABILITY /
RESPONSIVENESS
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
DATAWA.RE
Cloud Data Automation Platform
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
INBOUND DATA
SYSTEM
APPLICATION
INTERNAL
EXTERNAL
DATAWA.RE PLATFORM
OUTBOUND DATA
AUDITING
NOTIFICATIONS
RULES
ENGINE
VIRTUAL
DATA SETS
DATA
MART
DATA
VAULT
IN-MEMORY
ANALYSIS
ENTERPRISE
REPORTING
ENTERPRISE
SYSTEMS
ETL +
MAPPING
DATA
PRE-PROCESSOR
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
DATAWA.RE
Key Advantages
 Dynamic Data Platform
– Data-driven set of common stored procedures and
packages create a foundation for scalable, extensible
and supportable Data Warehouse
 Process Configuration
– Centralized configuration parameters streamline and
accelerate revisions and updates to ETL variables
across the Data Warehouse
 Dynamic Data Mapping
– Table-driven mapping consolidates the enhancement
and extension of the Data Warehouse while
maintaining a stable, reliable environment
 Rules Engine
– Unified repository of business logic supports a highly
discoverable and supportable foundation for storing
and executing processes
 Auditing
– Detailed logging across the Data Warehouse
facilitates atomic-level insight into the current status,
and actions performed on, each data element.
 Notifications
– Consolidated message and attachment definitions
alert and inform users of planned and unplanned
events occurring in the Data Warehouse
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
INBOUND DATA
Capture and Organize Internal and/or External Data Sources
 Data Pre-Processor
– Standardize incoming data to extract new, changed, or
deleted records, and/or apply validation rules prior to
ETL + Mapping process
 Internal Sources & External Sources
– Access inbound data in various locations from various
systems in various formats, from raw data to extracts to APIs
– Internal or External operational or transactional systems
that contain or generate data
– Proprietary systems or Industry-standard business systems
– Datawa.re provides an unmatched platform for accessing external data
 ETL + Mapping
– Table-driven approach to common staging based on inbound file mapping,
leveraging a single SSIS package
– Table-driven ETL processing configuration (variables)
– Data Vault used for staging data
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
DATA VAULT
Staging area with multi-thread, parallel raw data storage
 Data Mapping
– Configuration Parameters Define Field-to-Field Data Change
– Single location for data additions, enhancements, and modifications
– Configuration parameters specify frequency of data export to DataVault
 Data Vault: Hub, Satellite, Link
– Flexible methodology for storing unaltered raw
data in the data warehouse
 Auditing
– All actions within Datawa.re are audited and logged at the atomic level at each step from
Pre-Processing to Rule Engine to Loading and Publishing Dim/Facts
– Collects all attributes of raw data movement between Raw Source and the DataVault
 Notifications
– Dynamic email alerts are based on templates which can contain table-defined variables
to custom recipients and content, including distribution lists, data attributes, auditing results,
and more.
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
DATA MART
Integrated, dimensional data model
 Rules Engine
– Common, table-driven storage for all business logic
(transformations / validations)
– The Rules Engine applies logic to Pre-Processor
and/or Data Mart (dim/fact creation)
 Data Mapping
– Table-driven approach defines how data is loaded into
dimensions from the DataVault (based on the data model)
– Defines Dimension and Fact table changes
– Configuration parameters specify frequency of data export to DataMart
 Dimensional Data Model
– Requires that logical dimensional data model is designed
by a data architect
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
OUTBOUND DATA
Presentation of final data elements
 Presentation / Data Distribution
– In-Memory Analysis Platforms
– Enterprise Report Platforms
– Data Distribution to an Outbound Destination
or Enterprise Systems
 Universal data reporting
– Report from any of the data elements within Datawa.re
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
Stay in the Cloud, or move to Private Cloud or On-Premise
GET STARTED IN THE CLOUD
DATAWA.RE CLOUD PRIVATE CLOUD / ON-PREMISE
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
T 1.844.313.5200
E GETSTARTED@DATAWA.RE
WDATAWA.RE
LET’S GO!
Fixed-Price, 30-day Operational Prototype
Review corporate non-
disclosure agreements,
determine key business
need, and begin
identifying key data
sources and metrics.
Business
Requirements
With clear requirements,
Datawa.re will focus on
integrating two key data
sources, developing the
supporting data
transformations and data
model.
Prototype
Development
You and a senior
Datawa.re analyst will
review the data model,
collectively validate
numbers and discuss the
presentation of the data
within your reporting
platform for guidance and
feedback.
Prototype
Validation + Review
WEEK
2+3
DAY
30
DAY
1
DAY
10
With ready-to-use operational data mart, you
can elect to continue with the Datawa.re
Team to integrate additional data sources...
Or, license the Datawa.re Platform and
continue forward with internal resources.
Project Green Light!
INTRODUCTION
DATA PLATFORM
KEY ADVANTAGES
INBOUND DATA
DATA VAULT
DATA MART
OUTBOUND DATA
3-STEP PROCESS
GET STARTED
For more information or to schedule a demo, visit www.datawa.re,
call 1.844.313.5200, or contact getstarted@datawa.re.

More Related Content

What's hot

DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING
DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAININGDATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING
DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING
Datawarehouse Trainings
 
Understanding System Performance
Understanding System PerformanceUnderstanding System Performance
Understanding System Performance
Teradata
 
Teradata a z
Teradata a zTeradata a z
Teradata a z
Dhanasekar T
 
Working with informtiaca teradata parallel transporter
Working with informtiaca teradata parallel transporterWorking with informtiaca teradata parallel transporter
Working with informtiaca teradata parallel transporter
Anjaneyulu Gunti
 
Teradata - Architecture of Teradata
Teradata - Architecture of TeradataTeradata - Architecture of Teradata
Teradata - Architecture of Teradata
Vibrant Technologies & Computers
 
Teradata 13.10
Teradata 13.10Teradata 13.10
Teradata 13.10
Teradata
 
Datastage free tutorial
Datastage free tutorialDatastage free tutorial
Datastage free tutorial
tekslate1
 
Towards a Reliable SDN Firewall
Towards a Reliable SDN FirewallTowards a Reliable SDN Firewall
Towards a Reliable SDN Firewall
Open Networking Summits
 
Etl - Extract Transform Load
Etl - Extract Transform LoadEtl - Extract Transform Load
Etl - Extract Transform Load
ABDUL KHALIQ
 
TPT connection Implementation in Informatica
TPT connection Implementation in InformaticaTPT connection Implementation in Informatica
TPT connection Implementation in Informatica
Yagya Sharma
 
Data flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousingData flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousing
Dr. Dipti Patil
 
Datastage Introduction To Data Warehousing
Datastage Introduction To Data Warehousing Datastage Introduction To Data Warehousing
Datastage Introduction To Data Warehousing
Vibrant Technologies & Computers
 
Get started with data migration
Get started with data migrationGet started with data migration
Get started with data migration
Thinqloud
 
Best Practices – Extreme Performance with Data Warehousing on Oracle Database
Best Practices – Extreme Performance with Data Warehousing on Oracle DatabaseBest Practices – Extreme Performance with Data Warehousing on Oracle Database
Best Practices – Extreme Performance with Data Warehousing on Oracle Database
Edgar Alejandro Villegas
 
Data Migration Solutions
Data Migration SolutionsData Migration Solutions
Data Migration Solutions
Muhammad Riyaan
 
Ch 7 Physical D B Design
Ch 7  Physical D B  DesignCh 7  Physical D B  Design
Ch 7 Physical D B Design
guest8fdbdd
 
Lsmw ppt in SAP ABAP
Lsmw ppt in SAP ABAPLsmw ppt in SAP ABAP
Lsmw ppt in SAP ABAP
Aabid Khan
 
Case Study: A Multi-Source Time Variant Data warehouse
Case Study: A Multi-Source Time Variant Data warehouseCase Study: A Multi-Source Time Variant Data warehouse
Case Study: A Multi-Source Time Variant Data warehouse
tarun kumar sharma
 
Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)
Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)
Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)
Andreas Buckenhofer
 
Five Tuning Tips For Your Datawarehouse
Five Tuning Tips For Your DatawarehouseFive Tuning Tips For Your Datawarehouse
Five Tuning Tips For Your Datawarehouse
Jeff Moss
 

What's hot (20)

DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING
DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAININGDATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING
DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING
 
Understanding System Performance
Understanding System PerformanceUnderstanding System Performance
Understanding System Performance
 
Teradata a z
Teradata a zTeradata a z
Teradata a z
 
Working with informtiaca teradata parallel transporter
Working with informtiaca teradata parallel transporterWorking with informtiaca teradata parallel transporter
Working with informtiaca teradata parallel transporter
 
Teradata - Architecture of Teradata
Teradata - Architecture of TeradataTeradata - Architecture of Teradata
Teradata - Architecture of Teradata
 
Teradata 13.10
Teradata 13.10Teradata 13.10
Teradata 13.10
 
Datastage free tutorial
Datastage free tutorialDatastage free tutorial
Datastage free tutorial
 
Towards a Reliable SDN Firewall
Towards a Reliable SDN FirewallTowards a Reliable SDN Firewall
Towards a Reliable SDN Firewall
 
Etl - Extract Transform Load
Etl - Extract Transform LoadEtl - Extract Transform Load
Etl - Extract Transform Load
 
TPT connection Implementation in Informatica
TPT connection Implementation in InformaticaTPT connection Implementation in Informatica
TPT connection Implementation in Informatica
 
Data flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousingData flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousing
 
Datastage Introduction To Data Warehousing
Datastage Introduction To Data Warehousing Datastage Introduction To Data Warehousing
Datastage Introduction To Data Warehousing
 
Get started with data migration
Get started with data migrationGet started with data migration
Get started with data migration
 
Best Practices – Extreme Performance with Data Warehousing on Oracle Database
Best Practices – Extreme Performance with Data Warehousing on Oracle DatabaseBest Practices – Extreme Performance with Data Warehousing on Oracle Database
Best Practices – Extreme Performance with Data Warehousing on Oracle Database
 
Data Migration Solutions
Data Migration SolutionsData Migration Solutions
Data Migration Solutions
 
Ch 7 Physical D B Design
Ch 7  Physical D B  DesignCh 7  Physical D B  Design
Ch 7 Physical D B Design
 
Lsmw ppt in SAP ABAP
Lsmw ppt in SAP ABAPLsmw ppt in SAP ABAP
Lsmw ppt in SAP ABAP
 
Case Study: A Multi-Source Time Variant Data warehouse
Case Study: A Multi-Source Time Variant Data warehouseCase Study: A Multi-Source Time Variant Data warehouse
Case Study: A Multi-Source Time Variant Data warehouse
 
Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)
Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)
Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)
 
Five Tuning Tips For Your Datawarehouse
Five Tuning Tips For Your DatawarehouseFive Tuning Tips For Your Datawarehouse
Five Tuning Tips For Your Datawarehouse
 

Similar to Datawa.re: Data warehouse design, development and support just got alot faster

BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICS
BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICSBIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICS
BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICS
TIBCO Spotfire
 
From Data Warehouse to Lakehouse
From Data Warehouse to LakehouseFrom Data Warehouse to Lakehouse
From Data Warehouse to Lakehouse
Modern Data Stack France
 
Pentaho etl-tool
Pentaho etl-toolPentaho etl-tool
Pentaho etl-tool
Sreenivas Kappala
 
Building the DW - ETL
Building the DW - ETLBuilding the DW - ETL
Building the DW - ETL
ganblues
 
Oracle migrations and upgrades
Oracle migrations and upgradesOracle migrations and upgrades
Oracle migrations and upgrades
Durga Gadiraju
 
20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.ppt20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.ppt
SumathiG8
 
BW Adjusting settings and monitoring data loads
BW Adjusting settings and monitoring data loadsBW Adjusting settings and monitoring data loads
BW Adjusting settings and monitoring data loads
Luc Vanrobays
 
C19013010 the tutorial to build shared ai services session 2
C19013010 the tutorial to build shared ai services session 2C19013010 the tutorial to build shared ai services session 2
C19013010 the tutorial to build shared ai services session 2
Bill Liu
 
Amit Kumar_Resume
Amit Kumar_ResumeAmit Kumar_Resume
Amit Kumar_Resume
Amit Kumar
 
Informatica Training | Informatica PowerCenter | Informatica Tutorial | Edureka
Informatica Training | Informatica PowerCenter | Informatica Tutorial | EdurekaInformatica Training | Informatica PowerCenter | Informatica Tutorial | Edureka
Informatica Training | Informatica PowerCenter | Informatica Tutorial | Edureka
Edureka!
 
Insync10 goldengate
Insync10 goldengateInsync10 goldengate
Insync10 goldengate
InSync Conference
 
Tide data warehousesolutionfort24_nayamsoft_flyer
Tide data warehousesolutionfort24_nayamsoft_flyerTide data warehousesolutionfort24_nayamsoft_flyer
Tide data warehousesolutionfort24_nayamsoft_flyer
Dileep Sankar
 
Technical Deck Delta Live Tables.pdf
Technical Deck Delta Live Tables.pdfTechnical Deck Delta Live Tables.pdf
Technical Deck Delta Live Tables.pdf
Ilham31574
 
Data Warehouse 101
Data Warehouse 101Data Warehouse 101
Data Warehouse 101
PanaEk Warawit
 
Exchange mailbox admin
Exchange mailbox adminExchange mailbox admin
Exchange mailbox admin
Luis Martinez
 
Introduction to data vault ilja dmitrijev
Introduction to data vault   ilja dmitrijevIntroduction to data vault   ilja dmitrijev
Introduction to data vault ilja dmitrijev
Ilja Dmitrijevs
 
Data-ware Housing
Data-ware HousingData-ware Housing
Data-ware Housing
Prof.Nilesh Magar
 
Novidades do SQL Server 2016
Novidades do SQL Server 2016Novidades do SQL Server 2016
Novidades do SQL Server 2016
Marcos Freccia
 
60141457-Oracle-Golden-Gate-Presentation.ppt
60141457-Oracle-Golden-Gate-Presentation.ppt60141457-Oracle-Golden-Gate-Presentation.ppt
60141457-Oracle-Golden-Gate-Presentation.ppt
padalamail
 
Sql server 2016 new features
Sql server 2016 new featuresSql server 2016 new features
Sql server 2016 new features
Ajeet pratap Singh
 

Similar to Datawa.re: Data warehouse design, development and support just got alot faster (20)

BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICS
BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICSBIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICS
BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICS
 
From Data Warehouse to Lakehouse
From Data Warehouse to LakehouseFrom Data Warehouse to Lakehouse
From Data Warehouse to Lakehouse
 
Pentaho etl-tool
Pentaho etl-toolPentaho etl-tool
Pentaho etl-tool
 
Building the DW - ETL
Building the DW - ETLBuilding the DW - ETL
Building the DW - ETL
 
Oracle migrations and upgrades
Oracle migrations and upgradesOracle migrations and upgrades
Oracle migrations and upgrades
 
20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.ppt20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.ppt
 
BW Adjusting settings and monitoring data loads
BW Adjusting settings and monitoring data loadsBW Adjusting settings and monitoring data loads
BW Adjusting settings and monitoring data loads
 
C19013010 the tutorial to build shared ai services session 2
C19013010 the tutorial to build shared ai services session 2C19013010 the tutorial to build shared ai services session 2
C19013010 the tutorial to build shared ai services session 2
 
Amit Kumar_Resume
Amit Kumar_ResumeAmit Kumar_Resume
Amit Kumar_Resume
 
Informatica Training | Informatica PowerCenter | Informatica Tutorial | Edureka
Informatica Training | Informatica PowerCenter | Informatica Tutorial | EdurekaInformatica Training | Informatica PowerCenter | Informatica Tutorial | Edureka
Informatica Training | Informatica PowerCenter | Informatica Tutorial | Edureka
 
Insync10 goldengate
Insync10 goldengateInsync10 goldengate
Insync10 goldengate
 
Tide data warehousesolutionfort24_nayamsoft_flyer
Tide data warehousesolutionfort24_nayamsoft_flyerTide data warehousesolutionfort24_nayamsoft_flyer
Tide data warehousesolutionfort24_nayamsoft_flyer
 
Technical Deck Delta Live Tables.pdf
Technical Deck Delta Live Tables.pdfTechnical Deck Delta Live Tables.pdf
Technical Deck Delta Live Tables.pdf
 
Data Warehouse 101
Data Warehouse 101Data Warehouse 101
Data Warehouse 101
 
Exchange mailbox admin
Exchange mailbox adminExchange mailbox admin
Exchange mailbox admin
 
Introduction to data vault ilja dmitrijev
Introduction to data vault   ilja dmitrijevIntroduction to data vault   ilja dmitrijev
Introduction to data vault ilja dmitrijev
 
Data-ware Housing
Data-ware HousingData-ware Housing
Data-ware Housing
 
Novidades do SQL Server 2016
Novidades do SQL Server 2016Novidades do SQL Server 2016
Novidades do SQL Server 2016
 
60141457-Oracle-Golden-Gate-Presentation.ppt
60141457-Oracle-Golden-Gate-Presentation.ppt60141457-Oracle-Golden-Gate-Presentation.ppt
60141457-Oracle-Golden-Gate-Presentation.ppt
 
Sql server 2016 new features
Sql server 2016 new featuresSql server 2016 new features
Sql server 2016 new features
 

Recently uploaded

Measures in SQL (SIGMOD 2024, Santiago, Chile)
Measures in SQL (SIGMOD 2024, Santiago, Chile)Measures in SQL (SIGMOD 2024, Santiago, Chile)
Measures in SQL (SIGMOD 2024, Santiago, Chile)
Julian Hyde
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptxLORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
lorraineandreiamcidl
 
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemUI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
Peter Muessig
 
How to write a program in any programming language
How to write a program in any programming languageHow to write a program in any programming language
How to write a program in any programming language
Rakesh Kumar R
 
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdfUnveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
brainerhub1
 
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CDKuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
rodomar2
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOMLORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
lorraineandreiamcidl
 
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeA Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Aftab Hussain
 
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
Łukasz Chruściel
 
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfRevolutionizing Visual Effects Mastering AI Face Swaps.pdf
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
Undress Baby
 
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
mz5nrf0n
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j
 
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR SolutionsTransform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
TheSMSPoint
 
socradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdfsocradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdf
SOCRadar
 
E-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet DynamicsE-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet Dynamics
Hornet Dynamics
 
Webinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for EmbeddedWebinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for Embedded
ICS
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
Octavian Nadolu
 
Graspan: A Big Data System for Big Code Analysis
Graspan: A Big Data System for Big Code AnalysisGraspan: A Big Data System for Big Code Analysis
Graspan: A Big Data System for Big Code Analysis
Aftab Hussain
 
Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language ProcessorsFundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
Rakesh Kumar R
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j
 

Recently uploaded (20)

Measures in SQL (SIGMOD 2024, Santiago, Chile)
Measures in SQL (SIGMOD 2024, Santiago, Chile)Measures in SQL (SIGMOD 2024, Santiago, Chile)
Measures in SQL (SIGMOD 2024, Santiago, Chile)
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptxLORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
 
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemUI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
 
How to write a program in any programming language
How to write a program in any programming languageHow to write a program in any programming language
How to write a program in any programming language
 
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdfUnveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
 
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CDKuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOMLORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
 
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeA Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
 
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
 
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfRevolutionizing Visual Effects Mastering AI Face Swaps.pdf
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
 
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
 
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR SolutionsTransform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
 
socradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdfsocradar-q1-2024-aviation-industry-report.pdf
socradar-q1-2024-aviation-industry-report.pdf
 
E-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet DynamicsE-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet Dynamics
 
Webinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for EmbeddedWebinar On-Demand: Using Flutter for Embedded
Webinar On-Demand: Using Flutter for Embedded
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
 
Graspan: A Big Data System for Big Code Analysis
Graspan: A Big Data System for Big Code AnalysisGraspan: A Big Data System for Big Code Analysis
Graspan: A Big Data System for Big Code Analysis
 
Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language ProcessorsFundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
 

Datawa.re: Data warehouse design, development and support just got alot faster

  • 1. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE DYNAMIC DATA AUTOMATION PLATFORM Data Warehousing design, development and support just got a lot faster. FALL 2015 INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 2. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE DATA AUTOMATION REALITIES… Warehousing Projects… … rarely start with a complete understanding of the full scope of business requirements and implications of the necessary data. … are frequently architected based on what the technical resources are most comfortable completing. As a result, every project is developed in different ways, making on- going support difficult. … regularly start from scratch, demanding developers rebuild/repurpose common functions. Datawa.re… … enables frequent and rapid changes based on a flexible, table-driven architecture that enables frequent and rapid changes… without the burden of massive regression testing. … delivers a proven, scalable, sustainable and extensible architecture and approach that empowers technical resources to create near-term wins… and long- term supportability. … accelerates productivity with a common foundation and methodology enabling developers to immediately begin configuring processes, mapping data, and defining business logic. INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 3. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE DATA AUTOMATION SOLUTIONS… How does Datawa.re impact the business?  Rapid Set-up  Flexibility in Design  Accelerated Development  Continuity in Approach  Consistency Across Resources  Improved Support  Increased Responsiveness  Reduced Resource Requirements INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED FASTER CHEAPER BETTER DESIGN & ARCHITECTURE DEVELOPMENT & DEPLOYMENT TIME-TO-MARKET / SUPPORTABILITY / RESPONSIVENESS
  • 4. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE DATAWA.RE Cloud Data Automation Platform INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED INBOUND DATA SYSTEM APPLICATION INTERNAL EXTERNAL DATAWA.RE PLATFORM OUTBOUND DATA AUDITING NOTIFICATIONS RULES ENGINE VIRTUAL DATA SETS DATA MART DATA VAULT IN-MEMORY ANALYSIS ENTERPRISE REPORTING ENTERPRISE SYSTEMS ETL + MAPPING DATA PRE-PROCESSOR
  • 5. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE DATAWA.RE Key Advantages  Dynamic Data Platform – Data-driven set of common stored procedures and packages create a foundation for scalable, extensible and supportable Data Warehouse  Process Configuration – Centralized configuration parameters streamline and accelerate revisions and updates to ETL variables across the Data Warehouse  Dynamic Data Mapping – Table-driven mapping consolidates the enhancement and extension of the Data Warehouse while maintaining a stable, reliable environment  Rules Engine – Unified repository of business logic supports a highly discoverable and supportable foundation for storing and executing processes  Auditing – Detailed logging across the Data Warehouse facilitates atomic-level insight into the current status, and actions performed on, each data element.  Notifications – Consolidated message and attachment definitions alert and inform users of planned and unplanned events occurring in the Data Warehouse INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 6. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE INBOUND DATA Capture and Organize Internal and/or External Data Sources  Data Pre-Processor – Standardize incoming data to extract new, changed, or deleted records, and/or apply validation rules prior to ETL + Mapping process  Internal Sources & External Sources – Access inbound data in various locations from various systems in various formats, from raw data to extracts to APIs – Internal or External operational or transactional systems that contain or generate data – Proprietary systems or Industry-standard business systems – Datawa.re provides an unmatched platform for accessing external data  ETL + Mapping – Table-driven approach to common staging based on inbound file mapping, leveraging a single SSIS package – Table-driven ETL processing configuration (variables) – Data Vault used for staging data INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 7. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE DATA VAULT Staging area with multi-thread, parallel raw data storage  Data Mapping – Configuration Parameters Define Field-to-Field Data Change – Single location for data additions, enhancements, and modifications – Configuration parameters specify frequency of data export to DataVault  Data Vault: Hub, Satellite, Link – Flexible methodology for storing unaltered raw data in the data warehouse  Auditing – All actions within Datawa.re are audited and logged at the atomic level at each step from Pre-Processing to Rule Engine to Loading and Publishing Dim/Facts – Collects all attributes of raw data movement between Raw Source and the DataVault  Notifications – Dynamic email alerts are based on templates which can contain table-defined variables to custom recipients and content, including distribution lists, data attributes, auditing results, and more. INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 8. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE DATA MART Integrated, dimensional data model  Rules Engine – Common, table-driven storage for all business logic (transformations / validations) – The Rules Engine applies logic to Pre-Processor and/or Data Mart (dim/fact creation)  Data Mapping – Table-driven approach defines how data is loaded into dimensions from the DataVault (based on the data model) – Defines Dimension and Fact table changes – Configuration parameters specify frequency of data export to DataMart  Dimensional Data Model – Requires that logical dimensional data model is designed by a data architect INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 9. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE OUTBOUND DATA Presentation of final data elements  Presentation / Data Distribution – In-Memory Analysis Platforms – Enterprise Report Platforms – Data Distribution to an Outbound Destination or Enterprise Systems  Universal data reporting – Report from any of the data elements within Datawa.re INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 10. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE Stay in the Cloud, or move to Private Cloud or On-Premise GET STARTED IN THE CLOUD DATAWA.RE CLOUD PRIVATE CLOUD / ON-PREMISE INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 11. T 1.844.313.5200 E GETSTARTED@DATAWA.RE WDATAWA.RE LET’S GO! Fixed-Price, 30-day Operational Prototype Review corporate non- disclosure agreements, determine key business need, and begin identifying key data sources and metrics. Business Requirements With clear requirements, Datawa.re will focus on integrating two key data sources, developing the supporting data transformations and data model. Prototype Development You and a senior Datawa.re analyst will review the data model, collectively validate numbers and discuss the presentation of the data within your reporting platform for guidance and feedback. Prototype Validation + Review WEEK 2+3 DAY 30 DAY 1 DAY 10 With ready-to-use operational data mart, you can elect to continue with the Datawa.re Team to integrate additional data sources... Or, license the Datawa.re Platform and continue forward with internal resources. Project Green Light! INTRODUCTION DATA PLATFORM KEY ADVANTAGES INBOUND DATA DATA VAULT DATA MART OUTBOUND DATA 3-STEP PROCESS GET STARTED
  • 12. For more information or to schedule a demo, visit www.datawa.re, call 1.844.313.5200, or contact getstarted@datawa.re.