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So You Want to Write a Book…

Well, we did it.  I finally finished the book that I had been working on with my co-author for the last two years.  I thought I’d write a short post on my experiences writing a technical book and getting it published.  I know many people think about writing books, and I’d like to share my experiences so that others might learn from lessons that I learned the hard way.  Overall, it was an absolutely amazing experience and I have a feeling that the adventure is only beginning….

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Why don’t Data Scientists use Splunk?

I am currently attending the Splunk .conf in Orlando, and a director at Accenture asked me this question, which I thought merited a blog post.  Why don’t data scientists use or like Splunk.  The inner child in me was thinking, “Splunk isn’t good at data science”, but the more seasoned professional in me actually articulated a more logical and coherent answer, which I thought I’d share whilst waiting for a talk to start.  Here goes:

I cannot pretend to speak for any community of “data scientists” but it is true that I know a decent number of data scientists, some very accomplished and some beginners, and not a one would claim to use Splunk as one of their preferred tools.  Indeed, when the topic of available tools comes up among most of my colleagues and the word Splunk is mentioned, it elicits groans and eye rolls.  So let’s look at why that is the case:

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Can you use Machine Learning to detect Fake News?

Someone recently asked me for assistance with a university project whereby they were asked to predict whether a given article was fake news or not.  They had a target accuracy of 70%.  Since the topic of fake news has been in the news a lot, it made me think about how I would approach this problem and whether it is even possible to use machine learning to identify fake news.  At first glance, this problem might be comparable to spam detection, however the problem is actually much more complicated.  In an article on The VergeDean Pomerleau of Carnegie Mellon University states:

“We actually started out with a more ambitious goal of creating a system that could answer the question ‘Is this fake news, yes or no?’ We quickly realized machine learning just wasn’t up to the task.” 

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Drilling Security Data

Last Friday, the Apache Drill released Drill version 1.14 which has a few significant features (plus a few that are really cool!) that will enable you to use Drill for analyzing security data.  Drill 1.14 introduced:

  • A logRegex reader which enables Drill to read anything you can describe with a Regex
  • An image metadata reader, which enables you to query images
  • A suite a of GIS functionality
  • A collection of phonetic and string distance functions which can be used for approximate string matching.  

These suite of functionality really expands what is possible with Drill, and makes analysis of many different types of data possible.  This brief tutorial will walk you through how to configure Apache Drill to query log files, or any file really that can be matched with a regex.

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My Ideal Workspace

As more and more research is showing that the open office design actually reduces productivity (here) and (here), I recently shared a post on LinkedIn about how github “de-broed” their workspace, but I thought I’d share my thoughts on what I like, and don’t like in a work space.  Above is a picture of my home office with some labels.  Not specifically labeled is that there is plenty of natural light.  One of the most depressing places I ever worked was a windowless cube farm where the developers liked to leave the lights off.  I was going out of my mind!!

  1. A Door:  My ideal workspace has a door so that when privacy is needed, I can close the door and when it is not, I can open it.
  2. A clock:  I know computers have clocks, but having a big visible clock is really helpful for making sure things run on time.
  3. A comfortable chair, with foot rest:  If I’m doing tech work for a long time, I don’t want to be sitting on something that will cause trips to the chiropractor.
  4. Big Monitors:  I’m a big fan of multiple, large monitors, as they really increase productivity.
  5. Music:  I like to listen to music, especially when coding.  When I’m working in more public spaces, I have headphones…
  6. Stress Relief:  I play trombone and when things get stressful, one can always reduce some stress by playing some Die Walkure …. LOUDLY.
  7. Lots of Geek Books:  Nothing sets the stage for coding than being surrounded by O’Reilly geek books.
  8. Family Photos or other Personal Items:  I do my best work in a space that feels like my own, so I think it is important that people can have a space with some of their personal items that feels like their own.   Hence… I’m not a fan of hoteling or workspaces that set people up to work on large tables.

What do you like in a work space?

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