Book Review: Seveneves by Neal Stephenson

I can't remember when or why I picked up Seveneves by Neal Stephenson. It was published back in 2015 and I've read and enjoyed some of his other works, but by the time I started reading this I had forgotten the details on what Seveneves was about. Nevertheless, it ended up being an incredible book with so many interesting details. I went back and looked at the blurb and realized how much is promised in it:

What would happen if the world were ending?
A catastrophic event renders the earth a ticking time bomb. In a feverish race against the inevitable, nations around the globe band together to devise an ambitious plan to ensure the survival of humanity far beyond our atmosphere, in outer space.
But the complexities and unpredictability of human nature coupled with unforeseen challenges and dangers threaten the intrepid pioneers, until only a handful of survivors remain . . .
Five thousand years later, their progeny—seven distinct races now three billion strong—embark on yet another audacious journey into the unknown . . . to an alien world utterly transformed by cataclysm and time: Earth.
A writer of dazzling genius and imaginative vision, Neal Stephenson combines science, philosophy, technology, psychology, and literature in a magnificent work of speculative fiction that offers a portrait of a future that is both extraordinary and eerily recognizable. As he did in Anathem, Cryptonomicon, the Baroque Cycle, and Reamde, Stephenson explores some of our biggest ideas and perplexing challenges in a breathtaking saga that is daring, engrossing, and altogether brilliant.

Below follows my review for this book with only minor spoilers (that are also in the blurb above).

Data Science: MongoDB Sky Searches with Geospatial Queries

This is the fourth and, for now, final set of posts in my tutorial on using MongoDB No-SQL databases for astronomical work. We've created a database of Brown Dwarf objects making use of Python 3.7's Dataclasses, we've also stored header metadata for a variety of FITS files, and we've written functions to perform cone searches using HEALPix. Today, we're looking again at how to query the sky, but this time using MongoDB's built-in geospatial's functionality. As before, I provide a Jupyter notebook where those interested can follow along.