The Operational Analytics Book Club has published its second volume, covering chapters 4-7 of the book on The Fundamentals of Data Engineering. The authors emphasize the importance of focusing on abstract concepts rather than concrete tools and technologies to create value from data. The data landscape is evolving rapidly, with new tools and technologies emerging daily, making it essential for organizations to regularly evaluate their data stack and prioritize business needs. Containers have become a powerful operational technology, allowing developers to package up dependencies for software scripts to run on any machine without worrying about the details of said machine. The build vs buy debate highlights the importance of considering whether building custom solutions will create a competitive advantage or free up resources within the team. Data storage is a cornerstone of the data engineering lifecycle, with various types such as hard drives, solid-state drives, and random access memory being discussed. Understanding how these storage solutions work can bolster credibility as a data engineer, and block and object storage are recommended for specific use cases.