Data engineering is critical to being able to use and interpret data in a meaningful way, whether that be for reporting and visualizations or advanced analytics and machine learning.  AWS Data Engineering services provide access to data quickly and efficiently while offering flexibility based on your usage patterns.

Business Analysts, Data Analysts, and Data Scientists alike need access to large data sets. When engineering your data to be accessed by a wide audience, it is important to know who is using your data, which data they need, for what cadence they need it, and what the data will be used for.

Thorogood consultants Archana Krishna and Emily Dentinger show how AWS is investing in the development of new and existing tools to cater to a range of technically skilled users, including low-code and no-code services. We explore some of these new features within AWS Glue, DataBrew, and SageMaker, and discuss their integration into your AWS data platform alongside longstanding vital services, such as Redshift and S3.

What we cover:

• Discuss why the integration of AWS data engineering services are fundamental
• Explore tools and design patterns to support common usage scenarios
• Explain how the interconnectedness of AWS services unlock greater possibilities for both IT and Business users
• Highlight key functionalities of some powerful and exciting AWS tools

Is it for you?

• Do you have large amounts of data that need to be processed quickly?
• Are you looking for ways to make your AWS platform more efficient? Do a range of technically skilled users in your organization want access to the same data?
• Are you looking to incorporate services like AWS Glue, DataBrew, SageMaker into your AWS platform?