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Data Engineering with AWS: Closing Thoughts on This Book Retelling Series

We made it. Twenty posts. Fourteen chapters. One very thorough book about building data pipelines on Amazon Web Services.

When I started this retelling series back in December, I said the goal was to walk you through “Data Engineering with AWS” by Gareth Eagar in plain language, one chapter at a time. No jargon walls. No assumed knowledge. Just the concepts, the tools, and how they fit together. I hope I delivered on that.

Golang DSA Chapter 10 Part 2: Cache Management and Space Allocation

Welcome back. In Part 1 we went through garbage collection algorithms, reference counting, mark-and-sweep, and generational collection. Now let’s finish up Chapter 10 of “Learn Data Structures and Algorithms with Golang” by Bhagvan Kommadi. This second half covers cache management, space allocation on the stack, pointers, memory management tips, and profiling.

Data Engineering With AWS Chapter 14: Wrapping Up the Learning Journey

This is post 20 in my Data Engineering with AWS retelling series.

We made it. Thirteen chapters of pipelines, transforms, orchestration, security, querying, visualization, and machine learning. Chapter 14 is the final chapter. It does not introduce a new AWS service. Instead, it zooms out. Way out. It looks at how data engineering works in the real world, shows you case studies from Spotify and Netflix, and points you toward where the field is heading next.

Terraform Cert Guide Chapter 10: Terraform Cloud and Enterprise Explained

Up to this point in the book, everything has been about running Terraform on your own machine. Local state files, local commands, you doing everything manually. That works fine when it’s just you. But the moment your team grows beyond a couple of people, things get messy fast. Chapter 10 introduces the solutions HashiCorp built for exactly that problem: Terraform Cloud and Terraform Enterprise.

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