Further reading

The field of data engineering is vast and constantly evolving. There’s always more to learn and explore. Always prioritize staying up-to-date with the latest developments and best practices in the field.

Here are some recommended resources for further continued learning:

• AWS Big Data Blog (https://aws.amazon.com/blogs/big-data/): This blog provides a wealth of information on various topics related to big data on AWS, including ETL processes, data warehousing, and analytics.
• Python for Data Analysis, by Wes McKinney (https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662?&_encoding=UTF8&tag=embracingaugmentation-20&linkCode=ur2&linkId=7b622b5207f4c59b89834da4633457b5&camp=1789&creative=9325): This book is a comprehensive guide to using Python for data analysis. It covers various topics, including data cleaning, transformation, and visualization.
• Streaming Systems, by Tyler Akidau, Slava Chernyak,...