Enterprise Analytic Warehousing
Data warehouse architecture and strategy designed for analytics
This course will guide participants through the evolving world of data management for the purposes of providing and supporting analytics and analytical processing. You’ll learn frameworks, concepts, and techniques that will enable you to evaluate the strengths and weaknesses of different architectures, strategies, and solutions. We will both praise and critique concepts like lambda architectures, Kafka-based data fabrics, traditional data warehousing, and big data solutions. The course will cover the development of logical structures, relationships, and hierarchies, both discovered and assigned. Unlike in traditional data warehousing, you’ll learn about the advantages of creating distinctions between processing/query engines and storage layers and determining strategies for data capture and use. Having implemented analytics and data warehouse systems for many of America’s largest corporations and organizations for more than 30 years, Dan and Tim will share their experience and lessons learned.
What you'll learn-and how you can apply it
- Learn modern frameworks to use for enterprise analytic warehousing strategy development including lambda architecture, data fabric, data-value based architecture
- Learn how to evaluate the strengths and weaknesses of proposed solutions and architectures so that you can make better decisions for your organization
- Hear practical, real-world insights from experts who work with enterprise data every day
- Acquire the ability to value different data sources and determine its worth for analytics
- You’ll learn what you need to do to strengthen and broaden your analytics foundation so it delivers maximum decision-making value
This training course is for you because...
- You sit at the intersection of analytics and data management and want to develop strategies that avoid dead-end technologies
- You’re an experienced developer and want to better understand the landscape for analytics and data management.
- You’re a C-suite executive with responsibilities across your organization and want others to recognize the value and importance of your corporate data
- You’re an IT strategist and want to hear from experienced practitioners who have seen and worked with a wide range of systems.
- You’re an analytics expert and want to better understand the challenges and strategies of putting together enterprise architectures to support analytics and analytical processing.
- You should have basic knowledge of analytics and their business use cases.
- You should have basic knowledge of data processing and data strategy.
- The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition, book, Ralph Kimball https://learning.oreilly.com/library/view/the-data-warehouse/9781118530801/
- Agile Data Warehouse Design, video, Michael Blaha https://learning.oreilly.com/videos/agile-data-warehouse/9781771374095
About your instructors
Tim Vlamis is an expert in the visualization of data and the design of business intelligence dashboards, Tim combines a strong background in the application of business intelligence (BI), analytics, and machine learning with extensive experience in business modeling and valuation analysis. Tim has worked with dozens of America’s largest corporations and leading government and science organizations in the design of their business intelligence dashboards and data visualization programs. Tim has assisted several high-tech startups, led partnership formations and dissolutions in Europe, Australia, Hong Kong, Canada, and India, and has negotiated acquisitions in Mexico and Canada. He earned his Professional Certified Marketer (PCM) designation from the American Marketing Association and is an active speaker on business analytics and data visualization topics as well as machine learning, predictive analytics, and analytic warehousing. In addition to a life-long study of business processes, systems, and theories, Tim is a passionate student of complexity theory, the history of mathematics, and the principles of design. Tim earned an MBA from Northwestern University’s Kellogg School of Management and a BA in Economics from Yale University. Tim is a named contributor to multiple Oracle University courses on predictive analytics and machine learning and often serves as an expert instructor for them.
Dan Vlamis is President and founder of Vlamis Software Solutions, a boutique consultancy which has led more than 200 Business Analytics implementations for more than 25 years at many of the world’s leading organizations. Recognized by Oracle as an Oracle ACE Director and on the editorial board of Oracle Magazine, he consults with Oracle Product Management regularly. Dan covers Oracle BI and related products through his popular blog at www.vlamis.com/blog. Dan was a co-author on the Oracle Press books Data Visualization for Oracle BI 11g and Oracle Essbase and Oracle OLAP - The Guide to Oracle's Multidimensional Solution. Dan Vlamis holds a degree in Computer Science from Brown University. Dan has been a popular speaker at major Oracle conferences such as Oracle OpenWorld, Collaborate, and ODTUG Kscope for two decades and is known for his live demos of Oracle software. As an Oracle Business Intelligence Warehousing and Analytics User Community (BIWA) board member, he chaired BIWA Summit/Analytics and Data Summit in 2014, 2015, 2016, 2017, 2018 and 2019.
The timeframes are only estimates and may vary according to how the class is progressing
Segment 1: How Analytic Warehousing Differs from Traditional Data Warehousing (15 minutes)
- Need for consistent, accurate, timely data has not changed
- Analytic use cases are broader and more variant than ever
- Technical differences between analytic warehousing and traditional data warehousing
- Analytic warehousing offers significant improvements in value delivered
Segment 2: Architecture and Analytic Warehousing (30 minutes)
- Fundamental differences between cloud-based architectures and on-premise architectures
- Different types of data stores including Hadoop-based data lakes, cloud data stores, and other terabyte/petabyte sized solutions
- Data streams
- Data federation, data orchestration, and data fabrics
Segment 3: Understanding Data (45 minutes)
- Move the algorithms to the data
- The case for master data management and data quality management
- Data valuation for analytics
- Rationale and methods for data aggregation
- Data normalization and denormalization
- Data classifications and strategy maps
Break (10 minutes)
Segment 4: Processing Data for Analytics (45 minutes)
- Dimensional Modeling, best practices and tradeoffs
- Data augmentation and enhancement
- Discovered data structures
- Analytic processing for visualizations and displays
- Machine learning and high value use cases
Segment 5: Analytic Warehousing in the Real World (20 minutes)
- Security and privacy
- Scalability and robustness
- Limitations and challenges
- The future for analytic warehousing
Segment 6: Questions and Answers (15 minutes)
Course wrap-up and next steps