Transcript: DBTB INT Adam Mollenkopf

hi my name is Adam Mollenkopf i work at ESRI we make geospatial software data by the base a great event because there's lots of very knowledgeable speakers as well as attendees that you can interact with it's a very intimate event which is very nice as well because everybody's very approachable there's lots of different topics going on everything from from law to machine learning to big data to everything so the topics are very diverse and the speakers are very diverse and very in-depth as far as their knowledge data so cool these days because it's exploding so data is everywhere there's micro weather stations on the top of buses there's co2 sensors there's this Internet of Things emergence that's happening is just causing a surge of new data that's coming through that we need to have big data systems to be able to consume and just do analytics in real time as the data is coming in as well as do analytics after the fact and do that in a performant way so I think one of the coolest things that I've been working on recently is this data center operating system open source project or D cos and D cos basically allows you a common operating environment to run all of these things and do that full stack of things without having to worry about the infrastructure I think the big insight at least said I would hope to get across and my session that I'm doing is that you don't have to manage things on your own so managing spark managing Kafka and managing Cassandra managing elasticsearch becomes a difficult and challenging task to manage all that and know everything about those different frameworks using dcos or data center operating system basically gives you a foundation to deploy these things where you not to worry about the infrastructure you don't have to worry about what's running on what machine everything becomes ephemeral and if something dies then it just gets spun up on another machine and so you don't have to worry about things that you typically had to worry about in the past so it's kind of like putting your your data center on autopilot and then letting the resiliency capabilities of the DCOs take care of that for you I think to become a data scientist it's kind of challenging because there's lots of different definitions about what a data scientist is these days but a data scientist to me I think the first most important thing would be what is it that you're trying to accomplish or are you trying to solve so know your domain well that's probably the most important thing for a data scientist it's easy to get hung up on technologies you know spark and flink and all these other things that are coming out but just focus on what is the domain that you're trying to solve and does this tool help you solve your problem or not and not to get hung up on the technology so much but just focus on the domain aspect of things that would be my advice to somebody that's new in the field for for data scientist