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you

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my<00:00:13.840><c> name's</c><00:00:13.990><c> Tim</c><00:00:14.230><c> I'm</c><00:00:14.380><c> the</c><00:00:14.590><c> CEO</c><00:00:14.830><c> and</c><00:00:14.920><c> co-founder</c>

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my name's Tim I'm the CEO and co-founder
 

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my name's Tim I'm the CEO and co-founder
of<00:00:15.519><c> data</c><00:00:15.760><c> log</c><00:00:22.680><c> my</c><00:00:23.680><c> path</c><00:00:23.890><c> was</c><00:00:23.920><c> quite</c>

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of data log my path was quite
 

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of data log my path was quite
interesting<00:00:24.760><c> um</c><00:00:25.060><c> I</c><00:00:25.539><c> was</c><00:00:25.810><c> introduced</c><00:00:26.529><c> to</c><00:00:26.529><c> scala</c>

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interesting um I was introduced to scala
 

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interesting um I was introduced to scala
by<00:00:27.970><c> one</c><00:00:28.180><c> of</c><00:00:28.270><c> my</c><00:00:28.359><c> friends</c><00:00:28.810><c> Nicholas</c><00:00:29.260><c> who's</c><00:00:29.560><c> to</c>

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by one of my friends Nicholas who's to
 

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by one of my friends Nicholas who's to
the<00:00:29.800><c> CTO</c><00:00:30.250><c> and</c><00:00:30.460><c> co-founder</c><00:00:30.550><c> of</c><00:00:30.970><c> get</c><00:00:31.210><c> links</c><00:00:31.570><c> and</c>

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the CTO and co-founder of get links and
 

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the CTO and co-founder of get links and
he<00:00:33.489><c> just</c><00:00:33.760><c> touted</c><00:00:34.300><c> the</c><00:00:34.600><c> beauty</c><00:00:35.500><c> and</c><00:00:35.650><c> simplicity</c>

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he just touted the beauty and simplicity
 

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he just touted the beauty and simplicity
of<00:00:36.190><c> actor</c><00:00:36.579><c> systems</c><00:00:36.970><c> like</c><00:00:37.840><c> occas</c><00:00:38.260><c> and</c><00:00:38.559><c> so</c><00:00:39.370><c> I</c><00:00:39.820><c> was</c>

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of actor systems like occas and so I was
 

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of actor systems like occas and so I was
kind<00:00:40.180><c> of</c><00:00:40.300><c> introduced</c><00:00:40.780><c> to</c><00:00:41.200><c> scala</c><00:00:41.559><c> by</c><00:00:42.430><c> just</c>

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kind of introduced to scala by just
 

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kind of introduced to scala by just
wanting<00:00:43.000><c> to</c><00:00:43.120><c> learn</c><00:00:43.239><c> about</c><00:00:43.420><c> akka</c><00:00:43.750><c> and</c><00:00:44.079><c> how</c><00:00:44.530><c> we</c>

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wanting to learn about akka and how we
 

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wanting to learn about akka and how we
might<00:00:44.860><c> be</c><00:00:44.920><c> able</c><00:00:45.100><c> to</c><00:00:45.309><c> leverage</c><00:00:45.600><c> some</c><00:00:46.600><c> of</c><00:00:46.630><c> those</c>

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might be able to leverage some of those
 

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might be able to leverage some of those
programming<00:00:47.410><c> paradigms</c><00:00:47.890><c> to</c><00:00:48.640><c> scale</c><00:00:49.600><c> out</c><00:00:49.750><c> data</c>

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programming paradigms to scale out data
 

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programming paradigms to scale out data
logs<00:00:50.200><c> data</c><00:00:50.500><c> processing</c><00:00:51.070><c> capabilities</c><00:00:51.640><c> and</c><00:00:52.600><c> I</c>

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logs data processing capabilities and I
 

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logs data processing capabilities and I
just<00:00:53.680><c> started</c><00:00:53.950><c> to</c><00:00:54.129><c> learn</c><00:00:54.250><c> Scala</c><00:00:54.579><c> kind</c><00:00:55.000><c> of</c><00:00:55.059><c> has</c>

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just started to learn Scala kind of has
 

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just started to learn Scala kind of has
a<00:00:55.539><c> function</c><00:00:56.050><c> of</c><00:00:56.140><c> wanting</c><00:00:56.500><c> to</c><00:00:56.620><c> use</c><00:00:56.800><c> acha</c><00:01:02.969><c> so</c><00:01:03.969><c> my</c>

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a function of wanting to use acha so my
 

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a function of wanting to use acha so my
ideal<00:01:04.449><c> sack</c><00:01:04.720><c> is</c><00:01:04.899><c> very</c><00:01:05.110><c> very</c><00:01:05.289><c> diverse</c><00:01:05.740><c> just</c>

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ideal sack is very very diverse just
 

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ideal sack is very very diverse just
because<00:01:06.970><c> I</c><00:01:07.000><c> felt</c><00:01:07.750><c> very</c><00:01:07.990><c> very</c><00:01:08.319><c> many</c><00:01:08.530><c> holes</c><00:01:08.830><c> with</c>

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because I felt very very many holes with
 

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because I felt very very many holes with
an<00:01:09.220><c> organization</c><00:01:09.390><c> anywhere</c><00:01:10.390><c> from</c><00:01:10.600><c> kind</c><00:01:11.200><c> of</c><00:01:11.259><c> a</c>

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an organization anywhere from kind of a
 

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an organization anywhere from kind of a
deep<00:01:11.530><c> learning</c><00:01:11.709><c> researcher</c><00:01:12.429><c> to</c><00:01:12.789><c> a</c><00:01:13.149><c> backend</c>

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deep learning researcher to a backend
 

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deep learning researcher to a backend
engineer<00:01:13.840><c> and</c><00:01:14.259><c> also</c><00:01:14.470><c> a</c><00:01:14.530><c> front-end</c><00:01:14.920><c> engineer</c>

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engineer and also a front-end engineer
 

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engineer and also a front-end engineer
so<00:01:15.640><c> my</c><00:01:16.330><c> ideal</c><00:01:16.630><c> stack</c><00:01:16.929><c> is</c><00:01:17.170><c> just</c><00:01:17.470><c> finding</c><00:01:17.920><c> the</c>

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so my ideal stack is just finding the
 

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so my ideal stack is just finding the
right<00:01:18.160><c> tool</c><00:01:18.369><c> to</c><00:01:18.429><c> solve</c><00:01:18.850><c> the</c><00:01:19.030><c> the</c><00:01:19.330><c> right</c>

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right tool to solve the the right
 

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right tool to solve the the right
problem<00:01:19.720><c> for</c><00:01:20.470><c> us</c><00:01:20.500><c> the</c><00:01:20.860><c> back</c><00:01:21.039><c> end</c><00:01:21.310><c> in</c><00:01:21.729><c> the</c><00:01:22.030><c> back</c>

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problem for us the back end in the back
 

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problem for us the back end in the back
end<00:01:22.390><c> that's</c><00:01:22.720><c> generally</c><00:01:23.319><c> Scala</c><00:01:23.910><c> Anaka</c><00:01:24.910><c> and</c><00:01:25.709><c> for</c>

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end that's generally Scala Anaka and for
 

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end that's generally Scala Anaka and for
all<00:01:27.069><c> my</c><00:01:27.250><c> other</c><00:01:27.429><c> roles</c><00:01:27.670><c> then</c><00:01:28.080><c> that</c><00:01:29.080><c> the</c><00:01:29.380><c> staff</c>

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all my other roles then that the staff
 

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all my other roles then that the staff
differs<00:01:30.190><c> greatly</c><00:01:30.399><c> so</c><00:01:30.970><c> we</c><00:01:31.119><c> use</c><00:01:31.209><c> tensor</c><00:01:31.569><c> fro</c><00:01:31.780><c> for</c>

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differs greatly so we use tensor fro for
 

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differs greatly so we use tensor fro for
deep<00:01:32.170><c> learning</c><00:01:32.319><c> we</c><00:01:32.709><c> use</c><00:01:32.830><c> polymer</c><00:01:33.280><c> on</c><00:01:33.429><c> the</c>

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deep learning we use polymer on the
 

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deep learning we use polymer on the
front<00:01:33.729><c> end</c><00:01:33.880><c> that</c><00:01:34.390><c> kind</c><00:01:34.569><c> of</c><00:01:34.599><c> stuff</c><00:01:40.200><c> company</c>

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front end that kind of stuff company
 

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front end that kind of stuff company
it's<00:01:41.709><c> it's</c><00:01:42.039><c> neither</c><00:01:42.250><c> i'm</c><00:01:42.880><c> really</c><00:01:43.090><c> excited</c><00:01:43.179><c> to</c>

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it's it's neither i'm really excited to
 

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it's it's neither i'm really excited to
learn<00:01:43.929><c> about</c><00:01:44.250><c> some</c><00:01:45.250><c> of</c><00:01:45.369><c> the</c><00:01:45.459><c> progress</c><00:01:45.700><c> that</c>

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learn about some of the progress that
 

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learn about some of the progress that
spark<00:01:46.390><c> is</c><00:01:46.509><c> making</c><00:01:46.869><c> so</c><00:01:47.830><c> there's</c><00:01:48.039><c> a</c><00:01:48.099><c> talk</c><00:01:48.520><c> about</c>

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spark is making so there's a talk about
 

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spark is making so there's a talk about
beyond<00:01:49.660><c> shuffling</c><00:01:50.110><c> and</c><00:01:50.259><c> how</c><00:01:50.410><c> IBM</c><00:01:50.709><c> might</c><00:01:51.130><c> go</c>

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beyond shuffling and how IBM might go
 

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beyond shuffling and how IBM might go
out<00:01:51.459><c> and</c><00:01:51.670><c> help</c><00:01:52.090><c> scale</c><00:01:52.770><c> spark</c><00:01:53.770><c> there's</c><00:01:54.160><c> also</c><00:01:54.340><c> a</c>

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out and help scale spark there's also a
 

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out and help scale spark there's also a
great<00:01:54.759><c> data</c><00:01:54.970><c> frame</c><00:01:55.209><c> spark</c><00:01:55.569><c> talk</c><00:01:55.810><c> that</c><00:01:56.560><c> I'm</c>

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great data frame spark talk that I'm
 

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great data frame spark talk that I'm
looking<00:01:56.950><c> forward</c><00:01:57.130><c> to</c>

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looking forward to
 

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looking forward to
you

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you