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One of the biggest challenges with writing a significant piece like a book chapter or entire book is to estimate how long it will take and plan accordingly. My best reference was my PhD which was still significantly shorter than the book’s target 90,000 words. This blog post is about the book writing process as I experienced it. I hope it helps other authors setting out on such an endeavour. Since 'Guerrilla Analytics: A Practical Approach to Working with Data' is about operational aspects of agile data science, I recorded some data on the book writing process itself. Specifically, every time I finished a writing session, I recorded the number of words I’d written on that date.
In a Guerrilla Analytics environment, available tooling is often limited. There is either not enough budget, time or IT flexibility to get all the tools you want. On many jobs, I find myself using Microsoft SQL Server as the project RDBMS. Out of the box, SQL Server does not yet have a fuzzy match capability. You need to install additional tools such as SSIS to avail of fuzzy matching. Even then, SSIS is a GUI-driven application which contradicts a key Guerrilla Analytics Principle. In a Guerrilla Analytics environment, you would much rather have fuzzy match capabilities available in SQL code. This is where the following Similarity library comes in handy.