How should the Boulder Denver New Tech Meetup evolve?

technology photo

Photo by katerha

I attended BDNT last night and four of the presenters had cancelled the day before.  For shame!

Instead of scrambling to find new presenters, Robert Reich, the organizer for nine years(!), had a free ranging discussion about the nature of the meetup and how it could best serve its audience.  He also asked for suggestions on how to make the meetup better, from both new attendees and regulars.  People came up with a wide variety of suggestions:

  • fostering networking
  • setting up mentoring
  • focusing on technology rather than business
  • having a joint meetup with other meetups in the area

In a testimonial to the willingness of the people who run BDNT to experiment (or to the lack of imagination of participants), Robert had tried almost all the suggestions at one point or another, and they’d all been abandoned because of either lack of manpower, lack of success or lack of adoption.

Robert also mentioned that the BDNT seems to be a great ‘top of funnel’ meetup for people who are new to town.  They come the BDNT, learn about the Boulder scene, and move on to other, more focused groups and meetups.  Because so many different types of people use this meetup as a jumping off point, that can make it difficult to focus.

I think it is great that we could have such a discussion, and someone suggested ‘discussing roadblocks’ as a topic that the meetup could focus on.  Robert immediately asked ‘who has a roadblock’ and someone mentioned hiring as a tough one.  There then was a fairly free form but moderated discussion about hiring–what the best way to hire was, what the right questions for founders, employers and employees were, and how to think about risk profiles.

Unlike in past meetups I have attended, the room wasn’t packed (only about 100 people, which makes that a small BDNT).  Additionally there was very little Twitter backplane discussion (I saw three comments on the #bdnt hashtag, and two of them were mine).

There were also two of the more typical presentations, from a marijuana dispensary product search engine and a piece of hardware that modifies music based on sensor input.  I unfortunately wasn’t able to fully view either of these presentations, but it was nice to end up where I think the BDNT is unique: demos and pitches.

I don’t have any great advice for Robert on making the meetup better.  It seems that getting 100-400 people to take a few hours out of their lives to discuss new technology for almost a decade is a success any way you measure it.  If he still loves the meetup and the people he’s meeting, he should continue on.  If he doesn’t, then it might be time to quit, if he can’t find someone to take it over.

Sometimes things end.


Fun with Apache Spark And Census Data

I recently downloaded Apache Spark.  After working with HBase for a bit on my last project, it was a joy, even though I know little Scala.  I also downloaded some data from the Census Bureau (the 2010 Business Patterns, a pipe delimited file containing information on the business activity of the USA). I used the prepackaged data sources, so I didn’t have to use the Census API, which I have written about previously.

I was able to quickly start up Apache Spark on my workstation (thanks, quickstart!), and then ask some interesting questions of the data. I did all this within the Spark shell using Scala.  The dataset I downloaded has approximately 3M rows, so it is large enough to be interesting, but not large enough to need to actually use Spark.

So, what kinds of questions can you ask?  Well, given I downloaded the economic activity survey from 2010, I was interesting in knowing about different kinds of professions.  I looked at primarily at MSAs (which are “geographical region[s] with a relatively high population density at [their] core and close economic ties throughout the area”).  I did this because it was easy to filter them out with a string match, and therefore I didn’t have to look at any kind of code mapping table which I would have to dig into smaller geographic regions.

First, how many different professions are there in the Boulder Colorado MSA? This code:

val datfile = sc.textFile("../data/CB1000A1.dat")
val split_lines = datfile.map(_.split("\\|"))
val boulder = split_lines.filter(arr => arr[7].contains("Boulder, CO Metropolitan"))
val boulder_jobs = boulder.map(arr => arr(10));
boulder_jobs.count();

tells me in 2010 there were 1079 different types of jobs in the Boulder MSA.

Then I wanted to know which MSAs had the most jobs. Thanks to this SO post and the word count example, I was able to put together this query:

val countsbymsa = split_lines.map(arr => arr(7))
.filter(location => location.contains("Metropolitan Statistical Area"))
.map(location => (location,1)).reduceByKey(_+_,1).map(item => item.swap)
.sortByKey(true, 1).map(item => item.swap);
countsbymsa.saveAsTextFile("../data/msacounts");

And find out that the Los Angeles-Long Beach-Santa Ana, CA MSA has the most different jobs, at 2084 (nosing ahead of NYC by 14 jobs), and the Hinesville-Fort Stewart, GA MSA had the fewest at 700 (at least in 2010).

I didn’t end up using the XML utilities I found here, but found the wiki full of useful tips.




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