SlideShare a Scribd company logo

The Analytics Data Store: Information Supply Framework

•Download as PPSX, PDF•
•
Martyn Richard Jones
Martyn Richard Jones

Using the Data Warehouse paradigm of Bill Inmon to integrate core Data Warehousing, Big Data, statistics and alternative analytics.

Technology
1 of 32
The Analytics Data Store Martyn Jones Extending the Data Warehouse Architecture Cambriano Energy 2015 -
Data Warehousing Big Data Business Intelligence Statistics Analytics
Confused by Big Data? Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com You should be!
Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com
NOW! Build me an ADS... Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com
Let’s start simple!
Enterprise Operational Data – This is data that is used in applications that support the day to day running of an organisation’s operations. Typical data items in this space are sales transactions, purchase transactions, product information, client and contact information. Enterprise Operational Data may also include complexly structured data, such as contracts and other business documents. Applications in this space may include production control, logistics and stock control, as well as purchase order, supply chain management, management accounting and human resource modules.
Enterprise Process Data – This is measurement and management data collected to show how the operational systems are performing. In the past the recording of events went down to the level of a completed transaction – with a start and an end and nothing in between, and as transactions were kept as simple as possible, to maximize performance and throughput and minimise the risk of failure, very little process data was captured. Now, especially with the advent of Business Process Management and Web Logs, we collect a whole array of transaction and process performance data that was never previously captured.
Enterprise Process Data – This is measurement and management data collected to show how the operational systems are performing. In the past the recording of events went down to the level of a completed transaction – with a start and an end and nothing in between, and as transactions were kept as simple as possible, to maximize performance and throughput and minimise the risk of failure, very little process data was captured. Now, especially with the advent of Business Process Management and Web Logs, we collect a whole array of transaction and process performance data that was never previously captured.
Internal digital data DW 3.0 Information Supply Framework External digital data Data logistics Operational Data Store Data Warehouse Analytics Data Store Data Marts Statistical Analysis Business Intelligence Scenarios Data logistics Primary data flow Secondary data flow Operational applications Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 -
EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 -
EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Data Sources – This element covers all the current sources, varieties and volumes of data available which may be used to support processes of 'challenge identification', 'option definition', decision making, including statistical analysis and scenario generation. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Core Data Warehousing – This is a suggested evolution path of the DW 2.0 model. It faithfully extends the Inmon paradigm to not only include unstructured and complex data but also the information and outcomes derived from statistical analysis performed outside of the Core Data Warehousing landscape. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Core Statistics – This element covers the core body of statistical competence, especially but not only with regards to evolving data volumes, data velocity and speed, data quality and data variety. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu INTO THE ZONE!
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Complex Data – This is unstructured or highly complexly structured data contained in documents and other complex data artefacts, such as multimedia documents. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Event Data – This is an aspect of Enterprise Process Data, and typically at a fine-grained level of abstraction. Here are the business process logs, the internet web activity logs and other similar sources of event data. The volumes generated by these sources will tend to be higher than other volumes of data, and are those that are currently associated with the Big Data term, covering as it does that masses of information generated by tracking even the most minor piece of 'behavioural data' from, for example, someone casually surfing a web site. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Infrastructure Data – This aspect includes data which could well be described as signal data. Continuous high velocity streams of potentially highly volatile data that might be processed through complex event correlation and analysis components. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Event Applicance – This puts the dynamic data collation, selection and reduction functionality as close to the point of event data generation as physically possible. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Signal Applicance – This puts the dynamic data collation, selection and reduction functionality as close to the point of continuous streaming data generation as physically possible. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Distributed Inter Process Communication – Different forms of messaging allow high volumes of data to be transmitted in near real time. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Staging and Reduction – Traditional data staging combined with in-line data reduction. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter ET(A)L – Extending ETL to include data analytics components tightly integrated into parallel ETL job streams. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter ADS – The Analytics Data Store. 1. Statistics oriented 2. Integrated by focus area 3. Variable volatility 4. Time variant Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Statistical Analysis – Qualitative analysis. Diagnostic analysis, predictive analysis, speculative analysis, data mining, data exploration, modelling. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Scenarios and outcomes – 1. Snapshots of outcomes of scenario analysis as the process of analyzing possible future events by generating alternative possible outcomes. 2. Captured outcomes of statistical analysis. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Martyn Richard Jones 2015 – martynjones.eu Core Data Warehousing Core Statistics Data Source s Message Adapter Write back – The ability to append data, update data and enrich data within the Analytics Data Store, and to provide scenario data to the Core Data Warehousing. Cambriano Energy 2015 - Published by goodstrat.com
UNCOVER UNDERSTAND USE KNOWLEDGE INFORMATION DATA The Iniciativa Information Management Pyramid Copyright © 2000-2015 Iniciativa Org, S.L. Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com STEP STEP BY
UNCOVER UNDERSTAND USE KNOWLEDGE INFORMATION DATA The Iniciativa Information Management Pyramid Copyright © 2000-2015 Iniciativa Org, S.L. DW 3.0 Aligning with Statistics Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com DW 1.0 Building the Data Warehouse DW 2.0 Adding Unstructured Data
The Analytics Data Store Martyn Jones Extending the Data Warehouse Architecture Cambriano Energy 2015 - Have any questions about the Analytics Data Store and DW 3.0? Feel free to connect via Twitter, Facebook and the Cambriano Energy website. Alternatively you can contact me via my personal web-site at martynjones.eu Also, please checkout my blog, Good Strat, which deals with organisational strategy and information management.

Recommended

Breakout: Hadoop and the Operational Data Store
Breakout: Hadoop and the Operational Data StoreBreakout: Hadoop and the Operational Data Store
Breakout: Hadoop and the Operational Data StoreCloudera, Inc.
 
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsBest Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsCloudera, Inc.
 
Data Warehouse Optimization
Data Warehouse OptimizationData Warehouse Optimization
Data Warehouse OptimizationCloudera, Inc.
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Databricks
 
5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game ChangerCaserta
 
The Future of Data Warehousing: ETL Will Never be the Same
The Future of Data Warehousing: ETL Will Never be the SameThe Future of Data Warehousing: ETL Will Never be the Same
The Future of Data Warehousing: ETL Will Never be the SameCloudera, Inc.
 
Data lake analytics for the admin
Data lake analytics for the adminData lake analytics for the admin
Data lake analytics for the adminTillmann Eitelberg
 
Keynote: The Journey to Pervasive Analytics
Keynote: The Journey to Pervasive AnalyticsKeynote: The Journey to Pervasive Analytics
Keynote: The Journey to Pervasive AnalyticsCloudera, Inc.
 

Recommended

Breakout: Hadoop and the Operational Data Store
Breakout: Hadoop and the Operational Data StoreBreakout: Hadoop and the Operational Data Store
Breakout: Hadoop and the Operational Data StoreCloudera, Inc.
 
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsBest Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsCloudera, Inc.
 
Data Warehouse Optimization
Data Warehouse OptimizationData Warehouse Optimization
Data Warehouse OptimizationCloudera, Inc.
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Databricks
 
5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game ChangerCaserta
 
The Future of Data Warehousing: ETL Will Never be the Same
The Future of Data Warehousing: ETL Will Never be the SameThe Future of Data Warehousing: ETL Will Never be the Same
The Future of Data Warehousing: ETL Will Never be the SameCloudera, Inc.
 
Data lake analytics for the admin
Data lake analytics for the adminData lake analytics for the admin
Data lake analytics for the adminTillmann Eitelberg
 
Keynote: The Journey to Pervasive Analytics
Keynote: The Journey to Pervasive AnalyticsKeynote: The Journey to Pervasive Analytics
Keynote: The Journey to Pervasive AnalyticsCloudera, Inc.
 
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionHow One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionDataWorks Summit
 
Data Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management RequirementsData Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management RequirementsSnapLogic
 
Building the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsBuilding the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
 
Hadoop Integration into Data Warehousing Architectures
Hadoop Integration into Data Warehousing ArchitecturesHadoop Integration into Data Warehousing Architectures
Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer
 
Scalable data pipeline
Scalable data pipelineScalable data pipeline
Scalable data pipelineGreenM
 
Pervasive analytics through data & analytic centricity
Pervasive analytics through data & analytic centricityPervasive analytics through data & analytic centricity
Pervasive analytics through data & analytic centricityCloudera, Inc.
 
Lessons learned processing 70 billion data points a day using the hybrid cloud
Lessons learned processing 70 billion data points a day using the hybrid cloudLessons learned processing 70 billion data points a day using the hybrid cloud
Lessons learned processing 70 billion data points a day using the hybrid cloudDataWorks Summit
 
Hadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseHadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseDataWorks Summit
 
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Denodo
 
Multi-tenant Hadoop - the challenge of maintaining high SLAS
Multi-tenant Hadoop - the challenge of maintaining high SLASMulti-tenant Hadoop - the challenge of maintaining high SLAS
Multi-tenant Hadoop - the challenge of maintaining high SLASDataWorks Summit
 
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the Cloud
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the CloudBring your SAP and Enterprise Data to Hadoop, Apache Kafka and the Cloud
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the CloudDataWorks Summit/Hadoop Summit
 
Hadoop: Extending your Data Warehouse
Hadoop: Extending your Data WarehouseHadoop: Extending your Data Warehouse
Hadoop: Extending your Data WarehouseCloudera, Inc.
 
Benefits of Hadoop as Platform as a Service
Benefits of Hadoop as Platform as a ServiceBenefits of Hadoop as Platform as a Service
Benefits of Hadoop as Platform as a ServiceDataWorks Summit/Hadoop Summit
 
Data lake
Data lakeData lake
Data lakeGHAZOUANI WAEL
 
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...DataWorks Summit/Hadoop Summit
 
How to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsHow to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsInformatica
 
Why and how to leverage the simplicity and power of SQL on Flink
Why and how to leverage the simplicity and power of SQL on FlinkWhy and how to leverage the simplicity and power of SQL on Flink
Why and how to leverage the simplicity and power of SQL on FlinkDataWorks Summit
 
Integrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle DatabaseIntegrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle DatabaseGwen (Chen) Shapira
 
How Big Data and Hadoop Integrated into BMC ControlM at CARFAX
How Big Data and Hadoop Integrated into BMC ControlM at CARFAXHow Big Data and Hadoop Integrated into BMC ControlM at CARFAX
How Big Data and Hadoop Integrated into BMC ControlM at CARFAXBMC Software
 
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...Bridging the gap: achieving fast data synchronization from SAP HANA by levera...
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...DataWorks Summit
 
Creating a Data Store Object
Creating a Data Store ObjectCreating a Data Store Object
Creating a Data Store ObjectMarcelo Honores
 
Big data, Analytics and 4th Generation Data Warehousing
Big data, Analytics and 4th Generation Data WarehousingBig data, Analytics and 4th Generation Data Warehousing
Big data, Analytics and 4th Generation Data WarehousingMartyn Richard Jones
 

More Related Content

What's hot

How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionHow One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionDataWorks Summit
 
Data Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management RequirementsData Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management RequirementsSnapLogic
 
Building the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsBuilding the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
 
Hadoop Integration into Data Warehousing Architectures
Hadoop Integration into Data Warehousing ArchitecturesHadoop Integration into Data Warehousing Architectures
Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer
 
Scalable data pipeline
Scalable data pipelineScalable data pipeline
Scalable data pipelineGreenM
 
Pervasive analytics through data & analytic centricity
Pervasive analytics through data & analytic centricityPervasive analytics through data & analytic centricity
Pervasive analytics through data & analytic centricityCloudera, Inc.
 
Lessons learned processing 70 billion data points a day using the hybrid cloud
Lessons learned processing 70 billion data points a day using the hybrid cloudLessons learned processing 70 billion data points a day using the hybrid cloud
Lessons learned processing 70 billion data points a day using the hybrid cloudDataWorks Summit
 
Hadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseHadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseDataWorks Summit
 
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Denodo
 
Multi-tenant Hadoop - the challenge of maintaining high SLAS
Multi-tenant Hadoop - the challenge of maintaining high SLASMulti-tenant Hadoop - the challenge of maintaining high SLAS
Multi-tenant Hadoop - the challenge of maintaining high SLASDataWorks Summit
 
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the Cloud
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the CloudBring your SAP and Enterprise Data to Hadoop, Apache Kafka and the Cloud
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the CloudDataWorks Summit/Hadoop Summit
 
Hadoop: Extending your Data Warehouse
Hadoop: Extending your Data WarehouseHadoop: Extending your Data Warehouse
Hadoop: Extending your Data WarehouseCloudera, Inc.
 
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...DataWorks Summit/Hadoop Summit
 
How to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsHow to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsInformatica
 
Why and how to leverage the simplicity and power of SQL on Flink
Why and how to leverage the simplicity and power of SQL on FlinkWhy and how to leverage the simplicity and power of SQL on Flink
Why and how to leverage the simplicity and power of SQL on FlinkDataWorks Summit
 
Integrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle DatabaseIntegrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle DatabaseGwen (Chen) Shapira
 
How Big Data and Hadoop Integrated into BMC ControlM at CARFAX
How Big Data and Hadoop Integrated into BMC ControlM at CARFAXHow Big Data and Hadoop Integrated into BMC ControlM at CARFAX
How Big Data and Hadoop Integrated into BMC ControlM at CARFAXBMC Software
 
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...Bridging the gap: achieving fast data synchronization from SAP HANA by levera...
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...DataWorks Summit
 

What's hot (20)

How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionHow One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 Million
 
Data Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management RequirementsData Lakes: 8 Enterprise Data Management Requirements
Data Lakes: 8 Enterprise Data Management Requirements
 
Building the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsBuilding the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake Analytics
 
Hadoop Integration into Data Warehousing Architectures
Hadoop Integration into Data Warehousing ArchitecturesHadoop Integration into Data Warehousing Architectures
Hadoop Integration into Data Warehousing Architectures
 
Scalable data pipeline
Scalable data pipelineScalable data pipeline
Scalable data pipeline
 
Pervasive analytics through data & analytic centricity
Pervasive analytics through data & analytic centricityPervasive analytics through data & analytic centricity
Pervasive analytics through data & analytic centricity
 
Lessons learned processing 70 billion data points a day using the hybrid cloud
Lessons learned processing 70 billion data points a day using the hybrid cloudLessons learned processing 70 billion data points a day using the hybrid cloud
Lessons learned processing 70 billion data points a day using the hybrid cloud
 
Hadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseHadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data Warehouse
 
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
 
Multi-tenant Hadoop - the challenge of maintaining high SLAS
Multi-tenant Hadoop - the challenge of maintaining high SLASMulti-tenant Hadoop - the challenge of maintaining high SLAS
Multi-tenant Hadoop - the challenge of maintaining high SLAS
 
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the Cloud
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the CloudBring your SAP and Enterprise Data to Hadoop, Apache Kafka and the Cloud
Bring your SAP and Enterprise Data to Hadoop, Apache Kafka and the Cloud
 
Hadoop: Extending your Data Warehouse
Hadoop: Extending your Data WarehouseHadoop: Extending your Data Warehouse
Hadoop: Extending your Data Warehouse
 
Benefits of Hadoop as Platform as a Service
Benefits of Hadoop as Platform as a ServiceBenefits of Hadoop as Platform as a Service
Benefits of Hadoop as Platform as a Service
 
Data lake
Data lakeData lake
Data lake
 
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...
Worldpay - Delivering Multi-Tenancy Applications in A Secure Operational Plat...
 
How to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsHow to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
How to Architect a Serverless Cloud Data Lake for Enhanced Data Analytics
 
Why and how to leverage the simplicity and power of SQL on Flink
Why and how to leverage the simplicity and power of SQL on FlinkWhy and how to leverage the simplicity and power of SQL on Flink
Why and how to leverage the simplicity and power of SQL on Flink
 
Integrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle DatabaseIntegrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle Database
 
How Big Data and Hadoop Integrated into BMC ControlM at CARFAX
How Big Data and Hadoop Integrated into BMC ControlM at CARFAXHow Big Data and Hadoop Integrated into BMC ControlM at CARFAX
How Big Data and Hadoop Integrated into BMC ControlM at CARFAX
 
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...Bridging the gap: achieving fast data synchronization from SAP HANA by levera...
Bridging the gap: achieving fast data synchronization from SAP HANA by levera...
 

Viewers also liked

Creating a Data Store Object
Creating a Data Store ObjectCreating a Data Store Object
Creating a Data Store ObjectMarcelo Honores
 
Big data, Analytics and 4th Generation Data Warehousing
Big data, Analytics and 4th Generation Data WarehousingBig data, Analytics and 4th Generation Data Warehousing
Big data, Analytics and 4th Generation Data WarehousingMartyn Richard Jones
 
Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)mark madsen
 
Sales and Marketing Channel Management
Sales and Marketing Channel ManagementSales and Marketing Channel Management
Sales and Marketing Channel ManagementIndankal suresh
 
The Importance of Sales and Relationship Management in any Business
The Importance of Sales and Relationship Management in any BusinessThe Importance of Sales and Relationship Management in any Business
The Importance of Sales and Relationship Management in any BusinessXavier Jenkins
 
A brief introduction to Knowledge Management
A brief introduction to Knowledge ManagementA brief introduction to Knowledge Management
A brief introduction to Knowledge ManagementMartyn Richard Jones
 
Design Principles for a Modern Data Warehouse
Design Principles for a Modern Data WarehouseDesign Principles for a Modern Data Warehouse
Design Principles for a Modern Data WarehouseRob Winters
 
Marketing and its importance
Marketing and its importanceMarketing and its importance
Marketing and its importanceabdul basit
 
Source of Data in Research
Source of Data in ResearchSource of Data in Research
Source of Data in ResearchManu K M
 
Big Data Solutions for Healthcare
Big Data Solutions for HealthcareBig Data Solutions for Healthcare
Big Data Solutions for HealthcareOdinot Stanislas
 
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureBuilding an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureJames Serra
 
Data Collection-Primary & Secondary
Data Collection-Primary & SecondaryData Collection-Primary & Secondary
Data Collection-Primary & SecondaryPrathamesh Parab
 
What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?Health Catalyst
 

Viewers also liked (14)

Creating a Data Store Object
Creating a Data Store ObjectCreating a Data Store Object
Creating a Data Store Object
 
Big data, Analytics and 4th Generation Data Warehousing
Big data, Analytics and 4th Generation Data WarehousingBig data, Analytics and 4th Generation Data Warehousing
Big data, Analytics and 4th Generation Data Warehousing
 
Importance of Sales forecasting
Importance of Sales forecastingImportance of Sales forecasting
Importance of Sales forecasting
 
Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)
 
Sales and Marketing Channel Management
Sales and Marketing Channel ManagementSales and Marketing Channel Management
Sales and Marketing Channel Management
 
The Importance of Sales and Relationship Management in any Business
The Importance of Sales and Relationship Management in any BusinessThe Importance of Sales and Relationship Management in any Business
The Importance of Sales and Relationship Management in any Business
 
A brief introduction to Knowledge Management
A brief introduction to Knowledge ManagementA brief introduction to Knowledge Management
A brief introduction to Knowledge Management
 
Design Principles for a Modern Data Warehouse
Design Principles for a Modern Data WarehouseDesign Principles for a Modern Data Warehouse
Design Principles for a Modern Data Warehouse
 
Marketing and its importance
Marketing and its importanceMarketing and its importance
Marketing and its importance
 
Source of Data in Research
Source of Data in ResearchSource of Data in Research
Source of Data in Research
 
Big Data Solutions for Healthcare
Big Data Solutions for HealthcareBig Data Solutions for Healthcare
Big Data Solutions for Healthcare
 
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureBuilding an Effective Data Warehouse Architecture
Building an Effective Data Warehouse Architecture
 
Data Collection-Primary & Secondary
Data Collection-Primary & SecondaryData Collection-Primary & Secondary
Data Collection-Primary & Secondary
 
What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?
 

Similar to The Analytics Data Store: Information Supply Framework

Itron and Teradata: Active Smart Grid Analytics
Itron and Teradata: Active Smart Grid AnalyticsItron and Teradata: Active Smart Grid Analytics
Itron and Teradata: Active Smart Grid AnalyticsTeradata
 
IBM Maximo for Utilities
IBM Maximo for UtilitiesIBM Maximo for Utilities
IBM Maximo for UtilitiesVincent Kwon
 
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial ServicesRecorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial ServicesChris Holden
 
Driving the Data Pipelines for Connected Vehicles with Spring Cloud Data Flow
Driving the Data Pipelines for Connected Vehicles with Spring Cloud Data FlowDriving the Data Pipelines for Connected Vehicles with Spring Cloud Data Flow
Driving the Data Pipelines for Connected Vehicles with Spring Cloud Data FlowVMware Tanzu
 
IIR_conferentie_1.2[1]
IIR_conferentie_1.2[1]IIR_conferentie_1.2[1]
IIR_conferentie_1.2[1]Marc Govers
 
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...IDC4EU
 
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...DataBench
 
Big Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonBig Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonIBM Danmark
 
Optimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big DataOptimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big DataCloudera, Inc.
 
Réinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de DenodoRéinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de DenodoDenodo
 
Roadshow Chicago - Introduction
Roadshow Chicago - IntroductionRoadshow Chicago - Introduction
Roadshow Chicago - IntroductionInfluxData
 
DDMA 14 mei 2009 Business Intelligence case Ahold
DDMA 14 mei 2009 Business Intelligence case Ahold DDMA 14 mei 2009 Business Intelligence case Ahold
DDMA 14 mei 2009 Business Intelligence case Ahold DDMA
 
November 2009 - Walking on thin ice… from SOA to EDA
November 2009 - Walking on thin ice… from SOA to EDANovember 2009 - Walking on thin ice… from SOA to EDA
November 2009 - Walking on thin ice… from SOA to EDAJBug Italy
 
SNS practice: Generating ETL
SNS practice: Generating ETLSNS practice: Generating ETL
SNS practice: Generating ETLdelostilos
 
Using Grid data analytics to protect revenue, reduce network losses and impro...
Using Grid data analytics to protect revenue, reduce network losses and impro...Using Grid data analytics to protect revenue, reduce network losses and impro...
Using Grid data analytics to protect revenue, reduce network losses and impro...Schneider Electric
 
BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...
BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...
BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...Big Data Value Association
 

Similar to The Analytics Data Store: Information Supply Framework (20)

Itron and Teradata: Active Smart Grid Analytics
Itron and Teradata: Active Smart Grid AnalyticsItron and Teradata: Active Smart Grid Analytics
Itron and Teradata: Active Smart Grid Analytics
 
IBM Maximo for Utilities
IBM Maximo for UtilitiesIBM Maximo for Utilities
IBM Maximo for Utilities
 
Recorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial ServicesRecorded Future News Analytics for Financial Services
Recorded Future News Analytics for Financial Services
 
Driving the Data Pipelines for Connected Vehicles with Spring Cloud Data Flow
Driving the Data Pipelines for Connected Vehicles with Spring Cloud Data FlowDriving the Data Pipelines for Connected Vehicles with Spring Cloud Data Flow
Driving the Data Pipelines for Connected Vehicles with Spring Cloud Data Flow
 
IIR_conferentie_1.2[1]
IIR_conferentie_1.2[1]IIR_conferentie_1.2[1]
IIR_conferentie_1.2[1]
 
[EN] From ECM Enterprise Content Management to EIM Enterprise Information Man...
[EN] From ECM Enterprise Content Management to EIM Enterprise Information Man...[EN] From ECM Enterprise Content Management to EIM Enterprise Information Man...
[EN] From ECM Enterprise Content Management to EIM Enterprise Information Man...
 
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
 
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
Building a Bridge between Technical and Business Benchmarking, Gabriella Catt...
 
Big Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter JönssonBig Data & Analytics, Peter Jönsson
Big Data & Analytics, Peter Jönsson
 
Optimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big DataOptimizing Regulatory Compliance with Big Data
Optimizing Regulatory Compliance with Big Data
 
Réinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de DenodoRéinventez le Data Management avec la Data Virtualization de Denodo
Réinventez le Data Management avec la Data Virtualization de Denodo
 
Roadshow Chicago - Introduction
Roadshow Chicago - IntroductionRoadshow Chicago - Introduction
Roadshow Chicago - Introduction
 
DDMA 14 mei 2009 Business Intelligence case Ahold
DDMA 14 mei 2009 Business Intelligence case Ahold DDMA 14 mei 2009 Business Intelligence case Ahold
DDMA 14 mei 2009 Business Intelligence case Ahold
 
November 2009 - Walking on thin ice… from SOA to EDA
November 2009 - Walking on thin ice… from SOA to EDANovember 2009 - Walking on thin ice… from SOA to EDA
November 2009 - Walking on thin ice… from SOA to EDA
 
SNS practice: Generating ETL
SNS practice: Generating ETLSNS practice: Generating ETL
SNS practice: Generating ETL
 
Using Grid data analytics to protect revenue, reduce network losses and impro...
Using Grid data analytics to protect revenue, reduce network losses and impro...Using Grid data analytics to protect revenue, reduce network losses and impro...
Using Grid data analytics to protect revenue, reduce network losses and impro...
 
Big Data
Big DataBig Data
Big Data
 
BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...
BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...
BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. T...
 
It takes a village (to raise a ML model)
It takes a village (to raise a ML model)It takes a village (to raise a ML model)
It takes a village (to raise a ML model)
 
Intro to supply chain.pptx
Intro to supply chain.pptxIntro to supply chain.pptx
Intro to supply chain.pptx
 

Recently uploaded

CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessPixlogix Infotech
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?Igalia
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 

Recently uploaded (20)

CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 

The Analytics Data Store: Information Supply Framework

  • 1. The Analytics Data Store Martyn Jones Extending the Data Warehouse Architecture Cambriano Energy 2015 -
  • 2.
  • 3. Data Warehousing Big Data Business Intelligence Statistics Analytics
  • 4. Confused by Big Data? Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com You should be!
  • 5. Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com
  • 6. NOW! Build me an ADS... Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com
  • 7.
  • 9. Enterprise Operational Data – This is data that is used in applications that support the day to day running of an organisation’s operations. Typical data items in this space are sales transactions, purchase transactions, product information, client and contact information. Enterprise Operational Data may also include complexly structured data, such as contracts and other business documents. Applications in this space may include production control, logistics and stock control, as well as purchase order, supply chain management, management accounting and human resource modules.
  • 10. Enterprise Process Data – This is measurement and management data collected to show how the operational systems are performing. In the past the recording of events went down to the level of a completed transaction – with a start and an end and nothing in between, and as transactions were kept as simple as possible, to maximize performance and throughput and minimise the risk of failure, very little process data was captured. Now, especially with the advent of Business Process Management and Web Logs, we collect a whole array of transaction and process performance data that was never previously captured.
  • 11. Enterprise Process Data – This is measurement and management data collected to show how the operational systems are performing. In the past the recording of events went down to the level of a completed transaction – with a start and an end and nothing in between, and as transactions were kept as simple as possible, to maximize performance and throughput and minimise the risk of failure, very little process data was captured. Now, especially with the advent of Business Process Management and Web Logs, we collect a whole array of transaction and process performance data that was never previously captured.
  • 12. Internal digital data DW 3.0 Information Supply Framework External digital data Data logistics Operational Data Store Data Warehouse Analytics Data Store Data Marts Statistical Analysis Business Intelligence Scenarios Data logistics Primary data flow Secondary data flow Operational applications Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 -
  • 13. EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 -
  • 14. EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Data Sources – This element covers all the current sources, varieties and volumes of data available which may be used to support processes of 'challenge identification', 'option definition', decision making, including statistical analysis and scenario generation. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 15. EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Core Data Warehousing – This is a suggested evolution path of the DW 2.0 model. It faithfully extends the Inmon paradigm to not only include unstructured and complex data but also the information and outcomes derived from statistical analysis performed outside of the Core Data Warehousing landscape. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 16. EDW ADS DM DM DM Statistical analysis ETL T/ETL ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Message Adapter Message Queue OLTP Staging ODS ETLT/ETL Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 TL DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Message Adapter Core Statistics – This element covers the core body of statistical competence, especially but not only with regards to evolving data volumes, data velocity and speed, data quality and data variety. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 17. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu INTO THE ZONE!
  • 18. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Complex Data – This is unstructured or highly complexly structured data contained in documents and other complex data artefacts, such as multimedia documents. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 19. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Event Data – This is an aspect of Enterprise Process Data, and typically at a fine-grained level of abstraction. Here are the business process logs, the internet web activity logs and other similar sources of event data. The volumes generated by these sources will tend to be higher than other volumes of data, and are those that are currently associated with the Big Data term, covering as it does that masses of information generated by tracking even the most minor piece of 'behavioural data' from, for example, someone casually surfing a web site. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 20. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Infrastructure Data – This aspect includes data which could well be described as signal data. Continuous high velocity streams of potentially highly volatile data that might be processed through complex event correlation and analysis components. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 21. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Event Applicance – This puts the dynamic data collation, selection and reduction functionality as close to the point of event data generation as physically possible. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 22. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Signal Applicance – This puts the dynamic data collation, selection and reduction functionality as close to the point of continuous streaming data generation as physically possible. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 23. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Distributed Inter Process Communication – Different forms of messaging allow high volumes of data to be transmitted in near real time. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 24. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Staging and Reduction – Traditional data staging combined with in-line data reduction. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 25. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter ET(A)L – Extending ETL to include data analytics components tightly integrated into parallel ETL job streams. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 26. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter ADS – The Analytics Data Store. 1. Statistics oriented 2. Integrated by focus area 3. Variable volatility 4. Time variant Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 27. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Statistical Analysis – Qualitative analysis. Diagnostic analysis, predictive analysis, speculative analysis, data mining, data exploration, modelling. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 28. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Core Data Warehousing Core Statistics Data Source s Message Adapter Scenarios and outcomes – 1. Snapshots of outcomes of scenario analysis as the process of analyzing possible future events by generating alternative possible outcomes. 2. Captured outcomes of statistical analysis. Cambriano Energy 2015 - Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
  • 29. ADS Statistical analysis ET(A)L Staging & Reduction Signal Appliance Message Adapter Message Queue Infrastructur e Data Write back Complex data Event Data Event Appliance Scenario 1 Scenario 2 Scenario 3 DW 3.0 Information Supply Framework Martyn Richard Jones 2015 – martynjones.eu Core Data Warehousing Core Statistics Data Source s Message Adapter Write back – The ability to append data, update data and enrich data within the Analytics Data Store, and to provide scenario data to the Core Data Warehousing. Cambriano Energy 2015 - Published by goodstrat.com
  • 30. UNCOVER UNDERSTAND USE KNOWLEDGE INFORMATION DATA The Iniciativa Information Management Pyramid Copyright © 2000-2015 Iniciativa Org, S.L. Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com STEP STEP BY
  • 31. UNCOVER UNDERSTAND USE KNOWLEDGE INFORMATION DATA The Iniciativa Information Management Pyramid Copyright © 2000-2015 Iniciativa Org, S.L. DW 3.0 Aligning with Statistics Martyn Richard Jones 2015 – martynjones.eu Cambriano Energy 2015 - Published by goodstrat.com DW 1.0 Building the Data Warehouse DW 2.0 Adding Unstructured Data
  • 32. The Analytics Data Store Martyn Jones Extending the Data Warehouse Architecture Cambriano Energy 2015 - Have any questions about the Analytics Data Store and DW 3.0? Feel free to connect via Twitter, Facebook and the Cambriano Energy website. Alternatively you can contact me via my personal web-site at martynjones.eu Also, please checkout my blog, Good Strat, which deals with organisational strategy and information management.