pklee

Where is this recommended ? Also SPARQL as a query language is about "how" you want to get to your data (the relationships you traverse changes your final result) as opposed to SQL which is about "what" you want, set algebra. Not to mention the super grotesque format and the performance.

That is really cool. How did you go about designing the language. Did you go for a full lexer and parser ? or it was more functional. Curious to understand how you built it.

Very interesting article on earnest and very well done. The author seems to argue that there is a bond like investment instrument that SaaS are starting to become. The S curve the author talks about is a continuum in my limited observation. There are companies at EVERY ONE OF THOSE points i.e. in the past as well as in the future there are commodity companies. It is not clear to me why he target SaaS to be that.

great talk.. !!

haha.. those were the days.. where you would get someone's resume over finger :)

I came in thinking.. god it better not be him.. it better not be him.. so sad to hear this.. I loved his book on erlang. Just an amazing mind

I believe the GWT was aimed at AWT rendered on the web and I am also not sure it was "compiling" it as much as it was translating it.

I read this someplace.. thought it is fun to share.. we went from spaghetti (mainframe) code to lasagna (stacks / tiered architecture) to ravioli (micro-services) :)

For me, gets really fascinating towards the last part. How he relates human anatomy to the domestication. Large eyes, smaller face etc.

R.I.P Dick Dale - https://www.youtube.com/watch?v=mKpsuGMeqHI amazing piece and probably the only piece I have heard !!

That is really cool.. had not seen this before.. I do think the intention is api access to this data not browser.

The beauty of RDF is it can support schema promiscuity i.e. you can have many schemas for your same data. You can do that with OWL. In typical graph databases you are fixed on nodes, properties and edges, but in RDF you can choose what should be node and properties arbitrarily. My issue with RDF has been performance works great for categorical, relationship heavy data, but not so much for numerical data.

I completely agree with this. In the recent past, I have been moving from ppt to writing articles. The clarity you get while you write it demonstrates how much you know. It is like thinking about a picture and then drawing it, you have to focus on a lot of details to get it right. When we think the mind doesn't focus on those

A book called "Lords of Strategy" tracks the consulting "apple" thing. It is a good read. Sorry for duplications. I think overall management consulting is a world ready to be distrupted (if not already). Regurgitation sold as overpriced thoughtware

pretty cool.. thanks for sharing... I could not work on the app itself, but that is ok.

Just looked at stimulus. Really cool framework. circa 2008 or so there was this concept of non-intrusive javascript where you bind js to elements. So you could wysiwyg html and then inject js where you need it. Thought that was a neat idea and like what stimulus does it. Any real life experience from anyone ?

Couple of quick points a. If you are primarily dealing with categorical data - strings as opposed to numbers, graphs are pretty good for storage, retrieval and visualization. Categorical - genes, diseases etc. and require a lot of graph algorithms eigenvalue, shortest path etc. Biggest difference in querying is - in SQL you say "what" you want, in SPARQL / Gremlin you say "how" you want it i.e. what relationships to take b. Graph as a representation format shines, but as a storage mechanism, have not found it to be optimal. Many go for graph as a layer on top of RDBMS c. RDF is better in terms of standardization instead of prop. Graph Database. This is because you can arbitrarily decide what should be a vertex vs. what should be a property. In things like Neo4J it gets fixed once you decide. Virtuoso comes pretty close since it implements RDF on a RDBMS (my limited understanding) d. It is good for representing knowledge / metadata (atleast RDF) but again I would stay away from representing data. e. Your choice of graph algorithms typically ends up being what comes prepackaged (say gremlin etc.), or you take it intermediary and use algorithms there (Networkx / igraph (igraph is awesome)) or writing your own (this is not trivial typically) f. Many pointed about the schema, I actually think this is the advantage of RDF. My typical workflow is to start with RDF, do my basic stuff on RDF until I have a good understanding of what are the queries and therefore optimal schema and then migrate to RDBMS as needed. Trying to do large scale on RDF on a laptop infra is not optimal

I would try to use some combination of RDBMS with runtime graph like igraph. YMMV.

I have not used it, I have heard of it. Have you used it, would love to get your ideas / thoughts on it.

Do not know the cost point of TAMR. Aware that it is expensive. There are a couple of tools that come to mind, metacat (as someone pointed out), wherehows (linkedin), apache atlas (not sure who contributed). The issue is also to look at not just RDBMS but also RDF, Graph and then abstract it through a semantic layer.

Maastar - slightly adjacent idea. Research papers sometimes involve data through which the hypothesis is made, getting access to this data can help understand hypothesis better. It can also fuel other ideas for people. Would be great to also have workspace where people can engage in active data-sleuthing through these shared spaces and workbenches.

https://www.tamr.com/ - Metadata management for the enterprise as I understand it.

We use this to impute the schema. Has anyone thought about taking this creating something like an open-source TAMR ? Would love to hear ideas around it if someone has.

Very cool. So much better than loading youtube videos. Would be great if there is a way to annotate parts of it. Apologies if it is a feature creep :(

Docker and had a new found appreciation for it. Then went to doing a cluster set up for kubernetes (Funny story : Did not know how to stop kubernetes cluster :) Tried to stop the cluster by killing my images, kept respawning) and eventually settled on a simpler architecture with nginx on front and zk for coordination. It was a lot of fun :)

What an amazing talk. I do stream monitoring and have heard about planaria and have seen specimens. I did not know it is such an amazing creature. What a beautiful talk.