Prerequisite: Familiarity with data warehousing concepts and requirements.
The 2020s are going to require a modern data warehouse to meet demanding new requirements for machine learning, data variety, scale, and real-time analytics—and this will often be implemented in part or in its entirety in the cloud.
In this course, you will learn about the major data warehouse platforms, their abilities to support the modern data warehouse, key architectural features, and what makes them different from one another. With a focus on data warehousing in the cloud, this course will help you understand why data warehouse platforms are scalable in different ways.
This course will give you the technical reasons why scaling up is sometimes easy and sometimes very hard—at a level that architects, strategists, and decision makers can understand. You need this understanding to choose the best platform for your cloud data warehouse or workload—and to avoid platform mistakes that can be catastrophic.
You Will Learn
- Key concepts of modern data warehouses
- Platform architecture and scalability
- Performance and cost
- Workload requirements and how to apply them in selection
- Data and analytics variety
- Machine learning and advanced analytics inside the data warehouse
- Near real-time data and analytics
- Relevant features of leading cloud data warehouse platforms such as AWS Redshift, Azure SQL, Cloudera Data Warehouse, Google BigQuery, Oracle ADW, Snowflake, Teradata, and Yellowbrick
- Data architects
- Data strategists
- Decision recommenders/decision makers
- Data analysts/data scientists
- Project managers
- Enterprise/cloud architects