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Spark Summit 2016
Who am I?
Looking for a Machine
Learning Summer
Intern!
bit.ly/nzzml
Spark Summit
2016
US / Europe & open to the world
Trend #1:
Spark 2.0
Trend #1: Spark 2.0
Trend #2:
RDD’s, DF’s, DS’s
RDD’s, DF’s, DS’s ... Why?
RDD’s, DF’s, DS’s ... Why?
RDD’s, DF’s, DS’s ... Why?
RDD’s, DF’s, DS’s ... Why?
+
Prefer DF’s & DS’s
over RDD’s!
RDD’s, DF’s, DS’s ... Why?
Demo ...
Trend #3:
Streaming 2.0
“The simplest way to do
streaming analytics, is when
you don’t have to worry about
streaming.”
Streaming 2.0
Streaming 2.0
Demo ...
Streaming 2.0
val StructuredStream = sqlContext.read.format(“json”).stream
(src_path)
StructuredStream.
select($"constant_Value").
groupBy($"constant_Value").
count.
write.
format("parquet").
save("/tmp/out/value.parquet").
startStream()
Trend #4:
GraphFrames
Trend #4: GraphFrames
Trend #4: GraphFrames
http://graphframes.github.io/
Demo ...
Trend #5:
SparkR is
catching up
Trend #5: SparkR is catching up
Trend #6:
Deep-Learning
DNNs are coming:
Watch it closely!
Insight #1:
Big Players ...
… big community
Insight #2:
Same issues
everywhere ...
The user-
mailinglist is
your best friend!
Insight #3:
Stream,
Compute, Dump
Use Spark (streaming) what it’s
meant for: realtime computation,
not serving!
Insight #4:
4 Best practices
GroupByKey! GroupByKey?
Circumvent Skew by “Salting”
Key: Foo
Salted Key: Foo + random(1,saltDim)
Think about resource allocation!
--num-executors
--executor-cores
--executor-memory
? !
You know, window functions ...
first value, last value, rank,
Looking for a Machine
Learning Summer
Intern!
bit.ly/nzzml
Checkout
TechTuesday!
meetup.com/
Tech-Tuesday-Zurich

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"Spark Summit 2016: Trends & Insights" -- Zurich Spark Meetup, July 2016