Big Data RA Topics 1 Industries Data Characteristics “V”s Curation Processing Changes E, T, L Scalable Infrastructure Management Security Data Sources.

Slides:



Advertisements
Similar presentations
NIST BIG DATA WG Reference Architecture Subgroup Intermediate Report Co-chairs: Orit Levin (Microsoft) James Ketner (AT&T) Don Krapohl (Augmented Intelligence)
Advertisements

© Copyright 2014 OSIsoft, LLC. Presenter Name/s Presented by Best Practices for the OSIsoft UC and Slide Template.
CS 791z Graduate Topics on Software Engineering
Reference Architecture Subgroup NIST Big Data Public Working Group Reference Architecture Subgroup September 30, 2013 Co-chairs: Orit LevinMicrosoft James.
SAS® Data Integration Solution
Components and Architecture CS 543 – Data Warehousing.
Data Warehouse Toolkit Introduction. Data Warehouse Bill Inmon's paradigm: Data warehouse is one part of the overall business intelligence system. An.
NIST Big Data Public Working Group Reference Architecture Subgroup September 30, 2013 Co-chairs: Orit LevinMicrosoft James KetnerAT&T Don KrapohlAugmented.
NIST BIG DATA WG Reference Architecture Subgroup Meeting Agenda Co-chairs: Orit Levin (Microsoft) James Ketner (AT&T) Don Krapohl (Augmented Intelligence)
NIST Big Data Public Working Group
8/15/2013NIST Big Data WG / Ref Arch Subgroup1 NIST Big Data Program Alignment: Roadmap & Reference Architecture Version 1.3 Roadmap Subgroup NIST Big.
A Comparsion of Databases and Data Warehouses Name: Liliana Livorová Subject: Distributed Data Processing.
Copyright © 2012, SAS Institute Inc. All rights reserved. BIG DATA ANALYTICS FOR DEVELOPMENT.
Copyright © 2014 Pearson Education, Inc. 1 It's what you learn after you know it all that counts. John Wooden Key Terms and Review (Chapter 6) Enhancing.
© 2012 TeraMedica, Inc. Big Data: Challenges and Opportunities for Healthcare Joe Paxton Healthcare and Life Sciences Sales Leader.
Basic Concepts of Datawarehousing An Overview Prasanth Gurram.
Intro to MIS – MGS351 Databases and Data Warehouses Chapter 3.
Understanding Data Warehousing
Devices change the picture billion.
K E Y : SW Service Use Big Data Information Flow SW Tools and Algorithms Transfer Application Provider Visualization Access Analytics Curation Collection.
Ch 5. The Evolution of Analytic Processes
material assembled from the web pages at
NIST BIG DATA WG Reference Architecture Subgroup Draft Co-chairs: Orit Levin (Microsoft) James Ketner (AT&T) Don Krapohl (Augmented Intelligence) August.
Benchmarking Interactive Social Networking Actions Shahram Ghandeharizadeh Director of Database Lab Computer Science Department University of Southern.
OLAP & DSS SUPPORT IN DATA WAREHOUSE By - Pooja Sinha Kaushalya Bakde.
NIST Big Data Public Working Group Security and Privacy Subgroup Presentation September 30, 2013 Arnab Roy, Fujitsu Akhil Manchanda, GE Nancy Landreville,
Data Warehousing Data Mining Privacy. Reading Bhavani Thuraisingham, Murat Kantarcioglu, and Srinivasan Iyer Extended RBAC-design and implementation.
5-1 McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
NIST BIG DATA WG Reference Architecture Subgroup Intermediate Report Co-chairs: Orit Levin (Microsoft) James Ketner (AT&T) Don Krapohl (Augmented Intelligence)
K E Y : SW Service Use Big Data Information Flow SW Tools and Algorithms Transfer Transformation Provider Visualization Access Analytics Curation Collection.
 Understand the basic definitions and concepts of data warehouses  Describe data warehouse architectures (high level).  Describe the processes used.
8/20/2013NIST Big Data WG / Roadmap Subgroup1 Architecture Storage Architecture Processing Architecture Resource Managers Architecture Infrastructure Architecture.
NIST BIG DATA WG Reference Architecture Subgroup Draft Co-chairs: Orit Levin (Microsoft) James Ketner (AT&T) Don Krapohl (Augmented Intelligence) August.
Pengantar Sistem Informasi Data Resource Management.
IoT Meets Big Data Standardization Considerations
Data Resource Management Agenda What types of data are stored by organizations? How are different types of data stored? What are the potential problems.
K E Y : DATA SW Service Use Big Data Information Flow SW Tools and Algorithms Transfer Hardware (Storage, Networking, etc.) Big Data Framework Scalable.
Role Activity Sub-role Functional Components Control Data Software.
Big Data Yuan Xue CS 292 Special topics on.
Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 8: Data Warehousing.
Big Data Security Issues in Cloud Management. BDWG Big Data Working Group Researchers 1: Data analytics for security 2: Privacy preserving 3: Big data-scale.
MarkLogic The Only Enterprise NoSQL Database Presented by: Aashi Rastogi ( ) Sanket Patel ( )
Big Data & Test Automation
Background Information: Big Data Systems Vs Relational Database:
Intro to MIS – MGS351 Databases and Data Warehouses
WEB APPLICATION Diagram Template
Big Data Management – Fall 2016
Original Slides by Nathan Twitter Shyam Nutanix
Data and Analytics Diagram Template
G063 - Data flow diagrams.
Overview of MDM Site Hub
IoT Diagram Template IBM Cloud Architecture Center
Enabling Scalable and HA Ingestion and Real-Time Big Data Insights for the Enterprise OCJUG, 2014.
CTCS EBI Update CTCS Meeting – July 24, 2014
Microservices Diagram Template
Data Warehousing and Data Mining By N.Gopinath AP/CSE
Databases and Data Warehouses Chapter 3
المحاضرة 4 : مستودعات البيانات (Data warehouse)
Components of the Data Warehouse Michael A. Fudge, Jr.
G063 - Data flow diagrams.
Clouds & Containers: Case Studies for Big Data
PLEASE DO NOT DELETE THIS SLIDE
Systems Practice Use the next slide in this powerpoint as a template to identify 10 different types of systems. 4 of your slides need to be natural systems.
API Management Diagram Icons
Big DATA.
Systems Practice Use the next slide in this powerpoint as a template to identify 5 different types of systems. 2 of your slides need to be natural systems.
Security Diagram Template
On Premise High Availability DR Template
DBA Capture Diagram Template
Presentation transcript:

Big Data RA Topics 1 Industries Data Characteristics “V”s Curation Processing Changes E, T, L Scalable Infrastructure Management Security Data Sources (stakeholders) Consumers (stakeholders) Outputs 7/29/2013 version 0.2 Gary Mazzaferro , 2013

Data Sources Requirements/Capability Template Gary Mazzaferro , Please Note: Diagrams in this slide deck are not stylized or intended to represent a finished product. These diagrams are only presented to serve as a discussion point about diagram organization approach and content. Governance Security Compliance Infrastructure Curation Analytics Data Processing Load Extract Transform Batch Real-time Interactive Validation Summarization Aggregation Velocity Variety Volume Streaming Feeds Legacy Data Marts Operational Databases Non-Traditional Data Data Stores File Stores Compute Networking 7/29/2013 version 0.2

Downward Flow Template Gary Mazzaferro , Curation 5Vs Data Sources Industries/Consumers Outputs Processing Security Manage ment Requirements Origins Recipients Quantifiables Activities Participants Concept FlowConcept Flow Please Note: Diagrams in this slide deck are not stylized or intended to represent a finished product. These diagrams are only presented to serve as a discussion point about diagram organization approach and content. 7/29/2013 version 0.2

Upward Flow Template Gary Mazzaferro , Curation Data Sources Industries/Consumers Outputs Processing Security Manage ment Requirements Origins Recipients Quantifiables Activities Participants 5Vs Concept FlowConcept Flow Please Note: Diagrams in this slide deck are not stylized or intended to represent a finished product. These diagrams are only presented to serve as a discussion point about diagram organization approach and content. 7/29/2013 version 0.2

Consum ers Outputs Left To Right Flow Template Gary Mazzaferro , Curation Governance Infrastructure, Mgt, Sec. Processing 5Vs Data Sources Requirements Origins Recipients Quantifiables Activities Fabric Concept Flow Please Note: Diagrams in this slide deck are not stylized or intended to represent a finished product. These diagrams are only presented to serve as a discussion point about diagram organization approach and content. 7/29/2013 version 0.2

History 7/29/2013 version 0.2 Gary Mazzaferro , DateEditAuthorReason CreationGary MazzaferroConceptualization Added data stores to slide 1Gary MazzaferroRequest, Bob Marcus