Organizations rely on data science to support innovation, competitive advantage, and efficiency, and the data scientist role is vital to this practice. But to put data science into production at scale, you need skills and methods that go beyond the scope of the data scientist. The role of data engineer has emerged to ensure that predictive models are ready for production.
The technological requirements of data science have also evolved. The cloud data warehouse has developed to address the scalability, availability, and budgetary issues that arise as the volume of data dramatically increases.
Read The Scientist, the Engineer, and the Warehouse white paper to learn what it takes to put cloud analytics into practice.
- Understand the distinct roles of the data scientist vs. data engineer.
- Find out how these roles work together with a cloud data warehouse.
- Learn how Azure Synapse Analytics is uniquely suited to address the need for governance, manageability, and elasticity at any scale.
- See how Azure Synapse Analytics fits into an effective architecture for cloud analytics.
