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For several decades, data architecture has focused almost exclusively on the management of analytical data. Most data architecture teams have given little attention to operational data while the scope, variety, and complexity of operational systems and operational data has expanded substantially.
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As the operational data landscape has evolved and expanded, data architecture processes and practices have lagged behind. Data sprawl and data disparity increase the cost and complexity of operational data management. These data management challenges inhibit data integration, system interoperability, and business agility. Rethinking operational data architecture is an essential step to overcoming these barriers.
This 4-hour, 14-minute course describes concepts and considerations for rethinking and modernizing operational data architecture. It focuses on positive and proactive data management practices for operational data, and the underlying architectural processes and patterns to enable and reinforce those practices.
Prerequisite: Operational Data Architecture, Part 1: The Operational Data Landscape
You will learn:
- Architectural concepts, constructs, and techniques to manage data sprawl and data disparity
- Concepts and management practices for distributed data, homogeneous data, and heterogeneous data
- Concepts and management practices for data conflicts including semantic and schema conflicts
- Master data management (MDM) and reference data management (RDM) principles and practices
- Operational data architecture patterns including operational data store (ODS), data hubs, data brokers, and more
- Design techniques for adaptable and sustainable architecture
This course is geared towards:
- Current and aspiring data architects
- Data scientists and data engineers
- Data warehousing and data lake designers and developers
- Data and information systems program and project managers
Module 0: About the Course (2 mins)
Module 1: Distribution, Data Silos, and Data Conflicts (47 min)
- Managing Distributed Data
- Managing Homogenous & Heterogeneous DataAutomation Systems
- Managing Conflicting Database Schema
Module 2: Data Integration Architecture (51 min)
- Managing Master & Reference Data
- Managing Semantic Models
Module 3: Technical Operational Data Architecture (60 min)
- Operational Data Store
- Publish/Subscribe Paradigm
- Operational Data Hub
- Service Oriented Architecture
- Related Data Integration Technologies
Module 4: The Physical Architecture (50 min)
- Case Study
- Architecture Requirements
- Modeling Your Processes
Module 5: Implementation & Management (40 min)
- Identifying Architecture Issues
- Current State, Future State, & Gap Analysis
- Implementation Timeline
- Managing the Architecture
Module 6: Summary, Conclusions, & Next Steps (4 min)
Click
–here- to download a more detailed outline of this course.
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This exam tests knowledge and understanding of basic concepts, principles, and terminology of analytics.
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You will be tested in these areas:
- Processes, practices, and patterns to manage distributed data
- Processes, practices, and patterns to manage homogenous and heterogeneous data
- Techniques for managing conflicts in data semantics and in-database schema
- Processes, practices, and patterns to manage master and reference data
- Semantic modeling and schema integration techniques
- Operational data architecture patterns including ODS, data hubs & brokers, publish & subscribe patterns, operational data hub, and SOA patterns
- Integration technologies including ETL, replication, virtualization, and federation
- Data architecture design, deployment, and management processes and practices
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Additional Information
Number of Questions: 23
Time Limit: 46 minutes
Passing Score: 70%
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Once you pass the exam, you will receive a Certificate of Education
documenting that you have demonstrated mastery of the topic. Course
exams count towards eLC certification programs. Visit our
Certification page for more information about our various programs.
We recommend that you take detailed notes and review the course material multiple times before taking this exam.
Click here to learn more about CIMP exams.
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