Prerequisite: Familiarity with data warehousing concepts and requirements.
Norbert Kremer, Ph.D.
CBIP, AICP
Cloud Solution Architect
Analytics By Design
Norbert Kremer, Ph.D., is a recognized expert on cloud data platforms, including their scalability, performance, and cost characteristics. He has worked as a data engineer on large projects as well as a solution architect covering a wide range of cloud services. He is a Google Authorized Trainer and holds multiple Google Cloud Professional certifications. Other areas of interest include multicloud architectures, generative AI involving integration of LLMs with data in corporate data stores, and cloud cost optimization (FinOps).
Richard Winter
CEO & Principal Consultant
WinterCorp
Richard Winter is an industry expert in analytics data management at scale. Mr. Winter advises decision makers on the data strategy and the data architecture of the modern data warehouse and the data lake. He has been retained to make architecture and platform recommendations or perform engineering tests for over 50 leading enterprises, government agencies, and technology vendors. He is a recognized thought leader and an expert in platform evaluation and benchmarking, having published more than 100 technical reports and articles.
The 2020s have seen the evolution of the data warehouse into a data lakehouse architecture. This approach helps users meet demanding new requirements for artificial intelligence and data science while continuing to support traditional business intelligence cost-effectively. These new workloads are characterized by greater data variety and greater data velocity. These growing requirements are essential to the business strategies that now so often rely on data, analytics, and AI for competitive advantage.
In this masterclass, you will learn about the major cloud data platforms, their ability to support the modern data lakehouse, their key architectural features, and what makes them different from one another. This course will help you understand how these architectural differences influence performance, operating cost, scalability, and ease of development for these new analytic data requirements. We will also discuss open table formats and how they are changing lakehouse architectures.
Scaling for data analytics in the cloud is often problematic and extremely costly. Increasing use of generative AI will place yet heavier workloads on the data platform and amplify these scalability challenges. Insight into these platform architectures is therefore the key to selecting the best platform for your workloads and avoiding catastrophic outcomes.
You Will Learn
- Key concepts of modern data lakehouse architectures
- Platform architecture, scalability, and cost of operation
- Workload requirements and how to apply them in selection
- Generative AI, predictive AI, and advanced analytics inside the database
- Near real-time data and analytics
- Relevant features of leading cloud data warehouse platforms such as AWS Redshift, Microsoft Fabric, Cloudera Data Warehouse, Data Bricks Data Lakehouse, Google BigQuery, Oracle Autonomous Data Warehouse, Snowflake, and Teradata VantageCloud Lakehouse
Geared To
- Data architects
- Data strategists
- Decision recommenders/decision-makers
- Data analysts/data scientists
- Project managers
- Enterprise/cloud architects