This document provides an agenda for a StreamProcessing.be meetup event on hosted solutions for stream processing and machine learning. The agenda includes presentations on Azure Stream Analytics and ML, Google Cloud DataFlow, and Amazon AWS ML. It also announces the next meetup focused on Apache Kafka, Akka Streams, Kinesis, and Spark Streaming. Additionally, it introduces the organizer Peter Vandenabeele and discusses his views on why stream processing is important for real-time big data processing and allowing data to serve as the system of record.

1. StreamProcessing.be
Brussels, May 27, 2015
Theme: hosted solutions for
Stream Processing and ML
#StreamProcessingBe

2. Agenda
15’ Intro (Peter)
35’ Azure Stream Analytics and ML (Jan)
5’ short break
35’ Google Cloud DataFlow (Alex)
35’ Amazon AWS ML (Nils)

3. Many thanks to
Microsoft Belux
Jan, Alex, Nils
@maasg, @svendfx
BigData.be, DataScience.be, AWS Belgium
you !

4. Next StreamProcessing.be Meetup
Thu, June 25, 2015, near Mechelen station
(looking for a location +/- 50 ppl)
● Introduction to Apache Kafka (Svend)
● Akka Streams and Kinesis (Peter)
● Understanding Spark Streaming (Gerard)

5. whoami : Peter Vandenabeele @peter_v
All Things Data (my consultancy)
current clients:
Real Impact Analytics
Telecom Analytics (emerging markets)
“Green” start-up (stealth mode)
IoT project (see next Meetup)

6. Why ?
(before anything else)

7. Why Stream Processing ?
(a personal view)

8. E.g. collaborative research (2013)
UniProt
(180 GB)
monthly update
consumer
update cost
≅
freq (1/month)
*
size (180 GB)
*
# consumers (5)
fetch + load + index
FULL data set

9. solution: Stream of updates (CDC)
Users table
continuous
updates
consumer
update cost
≅
Rate of Change
(10% / month)
*
size * # consumers
fetch + load
ONLY updates
stream
3M entries
300k updates/month
(independent of consumer update frequency)

10. Why Stream Processing ?
Real-time
*
Big Data
*
Distributed processing
(“many collaborators”)

11. Stream becomes the “master data”
● see stream as the master data (not the DB)
● allows real-time, distributed processing
● allows unification between:
○ operational teams
○ analytics teams
○ security, ...
● e.g. Kafka at LinkedIn (Kappa architecture)

12. Kafka (LinkedIn) : Martin Kleppmann
source : Martin Kleppmann
at strata Hadoop London

13. Kafka (LinkedIn) : Jay Kreps
source: Jay Kreps
on slideshare
“I ♥ Log”
Real-time Data and Apache Kafka

14. Why Stream Processing ?
Peter : real-time * (big data * distributed proc.)
Nathan Marz : recovery from human error + ...
Jay Kreps : organizational scalability + ...
Martin Kleppmann : data agility + …
YOU : ??? let’s discuss at beer ...

15. Speakers for today
● Jan Tielens (Microsoft) @jantielens
● Alex Van Boxel (Vente-Exclusive.com)
@alexvb
● Nils De Moor (Woorank) @ndemoor