As technology advances, new ways to store, process, and analyze data emerge. For example, large database systems, which require a lot of storage space, have been moved to the cloud and made remotely accessible to many users. These kinds of data infrastructures require business leaders to understand modern data systems and their working principles fully. Use this course to get to grips with the key differences between legacy data systems and modern infrastructures and explore crucial concepts related to modern data infrastructures. By the end of the course, you'll be able to argue why new age data infrastructures are necessary and traditional data systems are limited.

In this course, you will look into the data mesh architecture and the process of selecting data platforms which best fulfill the needs of a large, data-driven organization. Begin by delving into two approaches to managing the data in an organization: a centralized data team and a data mesh architecture, which is a more federated approach. Explore how a data mesh allows individual domain teams in an organization to manage their own data as long as it is made available to other teams and adheres to certain standards. Next, discover the various considerations for selecting data-related tools in an organization. You will get a glimpse into Apache Kafka and RabbitMQ, two widely used messaging tools, and will see use cases where each of them excel. Finally, you will look into two use cases for data stores: one for a web or mobile app and another for a team performing data analysis. Here, you will look into the use of Apache Cassandra and the Snowflake platform.