Genomics in the Azure Cloud

Conclusion

Well, well, well…if you’re reading this sentence, either you’re a champion reader who made it through the whole book or you are one of those crazy people who likes to read the end of a book first. Either way, I’m glad you’re here as I have some final thoughts for you.

Looking Backward

A fun fact that I learned in graduate school is that much of the high-performance computing hardware and software that we use today was born out of necessity from scientific fields (namely, physics and genomics) that needed to process tons of data. This all happened before the (tacky) big data term was coined.

Then when businesses began adopting this notion that data was the new oil, the appetite for “big data” was accelerated overnight. Using cloud-based tools, businesses saw the benefit of flexibility and scale in handling what was becoming a hoarder situation of data.

Baby Azure

I was first introduced to Microsoft Azure in its early days as a consultant at a Microsoft partner firm in 2014. The UI was totally different, and the service offerings were considerably more limited. However, the potential was already there to build flexible and scalable databases, virtual machines, and more.

Since then, it’s been exciting to see the adoption of the cloud become a key to success for many organizations. Now, many of the bigger clouds (Azure, Amazon Web Services, and Google Cloud Platform) offer literally hundreds of services to fit virtually any task.

What Else?

In this book, we’ve covered ...