Summary
In this chapter, we discussed the options for securing, monitoring, and auditing your data pipeline within AWS. Earlier in this book, we explored various options for performing data wrangling activities within AWS. Let’s summarize the data we discussed earlier in this book:
- First, we explored AWS Glue DataBrew, which helps you create a data wrangling pipeline through a GUI-based approach for every type of user. This is useful for teams who want to quickly set up a data wrangling pipeline without worrying about the coding and management aspects of the pipeline.
- We also covered SageMaker Data Wrangler, which helps users create a GUI-based data wrangling pipeline. However, it’s more closely aligned toward machine learning workloads with tighter integration with SageMaker services. This is useful for teams who are planning to manage data wrangling for model training and inference in SageMaker.
- We also explored AWS SDK for pandas, also known as awswrangler...
