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yeah<00:00:12.530><c> prakash</c><00:00:13.429><c> chockalingam</c><00:00:13.879><c> solutions</c>

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yeah prakash chockalingam solutions
 

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yeah prakash chockalingam solutions
architect<00:00:14.990><c> and</c><00:00:15.410><c> data</c><00:00:15.619><c> bricks</c>

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the<00:00:25.220><c> best</c><00:00:25.430><c> thing</c><00:00:25.580><c> is</c><00:00:25.789><c> yeah</c><00:00:26.090><c> well</c><00:00:26.330><c> I</c><00:00:26.359><c> must</c>

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the best thing is yeah well I must
 

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the best thing is yeah well I must
architects<00:00:27.420><c> I</c><00:00:27.570><c> work</c><00:00:27.810><c> with</c><00:00:27.960><c> customers</c><00:00:28.199><c> on</c><00:00:28.769><c> a</c>

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architects I work with customers on a
 

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architects I work with customers on a
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lot of data related problems so it's
 

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lot of data related problems so it's
very<00:00:33.329><c> exciting</c><00:00:33.809><c> to</c><00:00:34.170><c> share</c><00:00:34.500><c> that</c><00:00:34.829><c> with</c><00:00:35.070><c> other</c>

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very exciting to share that with other
 

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very exciting to share that with other
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data engineers and other people in the
 

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data engineers and other people in the
industry

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it<00:00:49.310><c> is</c><00:00:49.460><c> always</c><00:00:50.180><c> exciting</c><00:00:50.540><c> how</c><00:00:51.020><c> like</c><00:00:51.860><c> a</c><00:00:51.890><c> lot</c><00:00:52.100><c> of</c>

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it is always exciting how like a lot of
 

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it is always exciting how like a lot of
companies<00:00:52.550><c> are</c><00:00:52.760><c> trying</c><00:00:52.910><c> to</c><00:00:53.060><c> be</c><00:00:53.120><c> more</c>

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companies are trying to be more
 

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companies are trying to be more
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data-driven like rather than trying to
 

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data-driven like rather than trying to
be<00:00:55.370><c> more</c><00:00:55.840><c> making</c><00:00:56.840><c> decisions</c><00:00:57.260><c> based</c><00:00:58.270><c> on</c><00:00:59.270><c> some</c>

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be more making decisions based on some
 

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be more making decisions based on some
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key individuals hypothesis so we are now
 

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key individuals hypothesis so we are now
kind<00:01:05.540><c> of</c><00:01:07.000><c> trying</c><00:01:08.000><c> to</c><00:01:08.060><c> be</c><00:01:08.180><c> a</c><00:01:08.210><c> state</c><00:01:08.780><c> as</c><00:01:09.050><c> much</c>

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kind of trying to be a state as much
 

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kind of trying to be a state as much
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data driven as possible hard to make
 

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data driven as possible hard to make
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decisions so so yeah that's that's the
 

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decisions so so yeah that's that's the
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most exciting part and data sometime
 

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most exciting part and data sometime
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most of the times proves a lot of
 

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most of the times proves a lot of
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people's intuition wrong huh it's fun to
 

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people's intuition wrong huh it's fun to
see<00:01:22.460><c> that</c>

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you

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of<00:01:34.390><c> the</c>

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of the
 

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of the
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so my talk was on Spock streaming and
 

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so my talk was on Spock streaming and
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like the best practices for it so the
 

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like the best practices for it so the
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main thing would be to users to kind of
 

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main thing would be to users to kind of
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how run a streaming job in production at
 

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how run a streaming job in production at
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scale so that's the key thing so people
 

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scale so that's the key thing so people
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can start with development but getting
 

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can start with development but getting
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into production has been always a tough
 

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into production has been always a tough
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thing so one of the primary more
 

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thing so one of the primary more
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objective for my talk was to kind of
 

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objective for my talk was to kind of
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give that insight to easily move from
 

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give that insight to easily move from
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development to production not scale we
 

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development to production not scale we
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have any key advantage oh I don't know
 

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have any key advantage oh I don't know
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I'm not a data scientist I'm more of a
 

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I'm not a data scientist I'm more of a
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data engineer so as far as so I'm not
 

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data engineer so as far as so I'm not
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very qualified to give that advice I
 

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very qualified to give that advice I
think

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