historical

breaking up with MySQL

Breaking up is hard to do. I have been looking at this for over a month now and I'm seeing the writing on the wall.

We are still running 5.6, and we have run into a lot of issues getting the upgrade. Things like:

  1. Apparently 5.7 is so buggy that my PaaS provider suggests we don't do the upgrade.
  2. running 5.6 queries against 5.7 is a massive issue because the Group By syntax has changed, so just to get there every snippet of SQL that uses group by needs to be refactored.
  3. This is a tangent but I am very concerned about ownership of MySQL by Oracle. I am naturally cynical when it comes to private business, as I am a proponent of open-source software. I feel like businesses have become increasingly orthogonal to consumers, basically brushing them aside as they see fit, and over time it just devolves. I like to see a clear path stretching out into the sunset for the core products that I use, and for MySQL, I think those days are over.
  4. Everyone is jumping ship. I don't know anyone using MySQL anymore. Not that there is a significance there, necessarily, because I don't have that many developer friends, and I am not a blind follower, but it does perhaps suggest a simple truth - we have finally outgrown SQL and relational databases in general as the de facto answer for data storage. It is a more nuanced marketplace now.

So my angle will be to facilitate a move away from MySQL over the next months and years. Sounds like I have a lot of research to do. 

MariaDB?

PostGres?

NoSQL?

I am even looking at R right now to replace or augment our BI so things could get pretty interesting. We will see.

nosql-vs-sql-overview-1.png

There's a revolution going on in Business Intelligence part I

Just out of college, I got a job maintaining an Access database for a legal firm in Wisconsin that was vetting different types of records for legal discovery. As a 'temp worker' registered with the local staffing firm, I spent about 10 months writing queries and importing spreadsheets and scanning documents for a team of paralegals. With that experience, I was able to launch a successful software development career on my arrival to Los Angeles in 1999 and through to present day where I own my own 1-man code 'shop'. My main expertise is and has always been dealing with data, and managing the process from OLTP to OLAP and on to business intelligence, which is where a lot of progress is being made right now. Just in the last few years I have seen a preponderance of many new outfits in this space, and that is mostly a good thing but can be a bit wearying to vet so many similar products. I will be writing a few things around this topic so I am labeling this part I.

I am going to wait on doing a top list of anything, or start comparing products. But what I do want to do today is talk about Shiny. Here is a simple proof of concept that I created, where you can select a number of random 'seeds' and the application will automatically create a histogram of the normal distribution of those random numbers.

Now this might not look like much, but for me it is an absolute revolution. First of all, the speed - R is built for what you call 'medium data' analysis - which fits right between the 'small data' and the current buzz-word 'big data' (at least according to Hadley Wickham, chief scientist at RStudio). And even in the OLAP / medium data space, you run into speed issues. Constantly. Different platforms handle that differently, which I will talk about some other time, but right now let me be very clear - R-backed ShinyApp is way ahead as a service, because R is way ahead as a data crunching platform. The advantages vs. SQL are already very clear to me and the truth is that most businesses, large and small, will probably never get to a NEED for big data. And I can't possibly imagine a scenario where it would NOT benefit you to spend a long time playing with your data and creating models in R, even if you plan on going directly to Hadoop or BigTable or Redshift. It's just the right place to start.

And secondly, the workflow for generating this stuff is really revolutionary. It is certainly your best bet for having complete control over presentation at the same time of not having to render all your parameter selections with massive javascript blocks using something like AMCharts or Highcharts, both of which I have worked with extensively, both of which force you to manage a massive amount of custom javascript code inside your views to the point where it just feels messy almost immediately.

And finally, along with the speed of it, the reliability of the shiny app server is great. I have seen nothing to suggest it won't scale, and I think it will soon become a real competitor to the current leader in the white-labelled embeddable BI space, which IMHO is Mode Analytics.

Next up for this series will be to flesh out a bigger shiny app with some more meaningful data, which means I will have to navigate the date selection functionality as input, and use that to rewrite the output. I'm thinking of a day of the year slider, with maybe some heat maps so you can drag across a timeline and watch the daily micro-swings in denomination distribution which might be an aha for somebody if I hook it up to some sales data.

this is the best I have ever looked on TV

NOTE: This story was originally published on my old blog (from before I moved to Squarespace).

I would like to announce, to the media gods who are always keeping score, that this is the best I have ever looked on broadcast television. Remember me thusly when I am at the pearly gates.

screen grab from the CHEYENNE DVD (MTV 2006)

screen grab from the CHEYENNE DVD (MTV 2006)

I finally watched the dvd that I have had for 6 years now, and it is actually not a bad show. I remember how much empathy I had for her, she was such a sweet girl with so much going on and I can't imagine what it's like to have had so much success so early in her life. As turns out, I got on camera a little bit, which surprised me. My face was in the tag montage, which was cool. And the shot above is a screen capture of me walking on stage in Breckinridge, about to play keys and sing backup for CHEYENNE at our show there which was being filmed for an HD special. There are a few other shots of me in the earlier episodes, but this is far and away the biggest shot of me in terms of time and closeup level that has ever been broadcast on any channel of television. I have set my own bar in terms of media slam dunks.

The line-up was:
Cheyenne Kimball - acoustic guitar, lead vocals
Dave Krusen - drums
Joey Clement - bass, vocals
Krister Axel - keyboards, vocals
Travis Arnold - electric guitar