A key component of a modern cloud data architecture is a robust framework for data movement and transformation. Amazon Web Services, one of the world’s leading cloud platforms, offers numerous tools which can help in this area.

Understanding the tooling options and how they may fit within different architectures is an important step in designing the right data transformation framework for your organization.

A prerequisite to any data analysis, reporting, or data science initiative is having data that is cleaned and prepared appropriately for that activity. But what frameworks, design patterns, and tools should we use within our organizations to support this data enrichment? Making these decisions requires an understanding of our organizational skillsets, strategies, and current architectures in combination with an understanding of the technical tooling possibilities. In this recorded webcast Thorogood Data & Analytics consultant Scott Stieritz explores these technical possibilities within AWS and talks about how each might fit in with different organizational approaches.

What to expect

This webcast includes a presentation and demo, focusing on the following topics:

• Introduction to the AWS platform and its data movement and transformation offerings
• Hands-on look at common data transformation architecture patterns
• Assessment of strengths and considerations of various AWS tooling choices based on different organizational needs

Is it for you?

• Would you like a better understanding of the technical possibilities for data transformation and enrichment within AWS?
• Are you trying to understand key factors in tooling choices within your architecture?
• Do you want a perspective on advantages and challenges of various data engineering design patterns in AWS?