A Quick Trip
Boarded the 17x King County Metro Bus at 6:11am yesterday. The trip was entirely uneventful until we arrived in Belltown in downtown Seattle. A guy with only his underwear on boarded the bus. Yup, you got that right, just like those underwear nightmares people have! That guy was living this situation for whatever unknown reasons. He didn’t make to much fuss though and eventually settled into one of the back seats on our packed bus.
Downtown as I got off I was pleasantly surprised that Victrola was open in the newly leased Amazon building near Pine & Pike on 3rd. I remember it being built and opening, but for some reason had completely forgotten it was there until today. Having a Victrola at this location sets it up perfectly for the mornings one needs to get to the airport. It’s located in a way that you can get off any connecting bus, grab an espresso at Victrola, and then enter the downtown tunnel to board LINK to the airport. Previously, the only option was really to wait until you get to the airport and buy some of the lackluster espresso coffee trash options they have at SEATAC.
All the things in between then occurred. The flight to San Jose, the oddball Taxi ride to Santa Clara DataStax HQ, then the Lyft ride to Caltrain to ride into downtown San Francisco to my hotel. Twas’ an excellently chill ride into the city and even a little productive. A good night of sleep and then up and at em’. Headed to the nearest Blue Bottle, which was on the way to my next effort of the trip. Blue Bottle was solid as always, and into the studios I went.
At this point of this quick trip I’ve just finished shooting a few new episodes of the Distributed Data Show (subscribe) with Jeff Carpenter (@jscarp) and Amanda Moran (@amandadatastax). Just recently I also got to shoot an episode with Patrick McFadin (@patrickmcfadin) too. We’ve been enjoying a number of conversations around multi-cloud considerations, what going multi-cloud means, and even what exactly does multi-cloud even really mean. It’s been fun and now I’m putting together some additional blog posts and related material to follow the posts with more detail. So keep an eye out for the Distributed Data Shows. You can also take the lazy route and subscribe to be notified when new episodes are released or you could even set your calendar for every Tuesday, because we follow an old school traditional schedule approaches to episode releases.
I’ve included a recap on a few of the recent episodes below, which you may want to check out just to get an idea of what’s to come. Great as a listen while commuting, something to put on while you’re relaxing a bit but want to think about or listen to a conversation on a topic, or just curious what’s up!
In this last episode DataStax Vanguard Lead Chelsea Navo (@mazdagirluk) joined in to talk about how the team helps enterprises meet the challenges posed by disrupting technology and competitors. Some of the highlights of the conversation include:
1:57 Defining what exactly enterprise transformation is.
4:00 Trends including retooling batch workloads around real-time requirements, handing larger data sets and the uber popular use of streaming data we see today.
7:37 Techniques on getting teams trained up on cutting-edge technology.
9:46 There’s an argument for “skateboard solutions“! I now want to write up a whole practice around this!
16:27 Data modeling. Challenges of. Challenges around. Data modeling, it’s a good topic of exploration.
25:56 The data layer is still typically the hardest part of your application! (The assertion is made; agree, disagree, or ??)
Jonathan Lacefield (@jlacefie) joins this episode to discuss the history of DataStax Enterprise Graph, common use cases, and future directions including multi-model enhancements.
1:56 – Jonathan gives us a quick overview of graph database history at DataStax – Titan Aurelius, Apache TinkerPop, and key figures like Marco Rodriguez (@twarko) and Matthias Broecheler (@mbroecheler).
5:48 – Using Cassandra as the pluggable storage layer for Titan led to the creation of DataStax Enterprise Graph
8:40 – The natural relationship between graph processing and analytics/graph analytics. The trend is toward abstracting the operational/analytic choice from the application developer.
17:28 – Problems being solved with graph databases – Customer 360, Authorization, Entity Resolution.
22:22 – Continued support for multi-model, hybrid cloud, writing data once and accessing through the right API for the job – CQL, SQL, Gremlin
27:37 – Graph query languages like Gremlin, Cypher require a paradigm shift for developers – domain specific languages provide a way to minimize this learning curve.
A Network of Excellent People
Amidst those episodes, and good people to follow on many of the topics we discuss and will be discussing on the show, give these people a follow. Lot’s of good content, good thinking – sometimes controversial – and a lot of smarts among them all.