While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top. The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing - or data streaming - exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available. In this talk, I’ll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most prominent characteristics. I’ll conclude by describing in detail one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. I’ll go through the all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform, I’ll try to impress attendees with a working demo implementation of it.

1. @nicolas_frankel
Introduction to Data
Streaming

2. @nicolas_frankel
• Former developer, team lead,
architect, blah-blah
• Developer Advocate
• Curious about Kubernetes
Me, myself and I

3. @nicolas_frankel
Hazelcast
HAZELCAST IMDG is an operational,
in-memory, distributed computing
platform that manages data using
in-memory storage, and performs
parallel execution for breakthrough
application speed and scale.
HAZELCAST JET is the ultra fast,
application embeddable, 3rd
generation stream processing
engine for low latency batch
and stream processing.

4. @nicolas_frankel
• Why streaming?
• Streaming approaches
• Hazelcast Jet
• Open Data
• General Transit Feed Specification
• The demo!
• Q&A
Schedule

5. @nicolas_frankel
Data was neatly stored in SQL
databases
In a time before our time…

6. @nicolas_frankel
• Analytics
• Supermarket sales in the last hour?
• Reporting
• Banking account annual closing
The need for Extract Transform Load

7. @nicolas_frankel
• Constraints
• Joints
• Normal forms
What SQL really means

8. @nicolas_frankel
• Normalized vs. denormalized
• Correct vs. fast
Writes vs. reads

9. @nicolas_frankel
• Different actors
• With different needs
• Using the same database?
The need for ETL

10. @nicolas_frankel
The batch model
1. Extract
2. Transform
3. Load

11. @nicolas_frankel
Batches are everywhere!

12. @nicolas_frankel
• Scheduled at regular intervals
• Daily
• Weekly
• Monthly
• Yearly
• Run in a specific amount of time
Properties of batches

13. @nicolas_frankel
• When the execution time overlaps
the next execution schedule
• When the space taken by the data
exceeds the storage capacity
• When the batch fails mid-execution
• etc.
Oops

14. @nicolas_frankel
• Parallelize everything
• Map - Reduce
• Hadoop
• NoSQL
• Schema on Read vs. Schema on Write
Big data!

15. @nicolas_frankel
• Keep a cursor
• And only manage “chunks” of data
• What about new data coming in?
Or chunking?

16. @nicolas_frankel
Event-Driven Programming
“Programming paradigm in which the flow of the
program is determined by events such as user
actions (mouse clicks, key presses), sensor outputs, or
messages from other programs or threads”
-- Wikipedia

17. @nicolas_frankel
Event Sourcing
“Event sourcing persists the state of a business entity
such an Order or a Customer as a sequence of state-
changing events. Whenever the state of a business
entity changes, a new event is appended to the list of
events. Since saving an event is a single operation, it is
inherently atomic. The application reconstructs an
entity’s current state by replaying the events.”
-- https://microservices.io/patterns/data/event-sourcing.html

18. @nicolas_frankel
• Ordered append-only log
• e.g. MySQL binlog
Database internals

19. @nicolas_frankel
Make everything event-based!

20. @nicolas_frankel
• Memory-friendly
• Easily processed
• Pull vs. push
• Very close to real-time
• Keeps derived data in-sync
Benefits

21. @nicolas_frankel
From finite datasets to infinite

22. @nicolas_frankel
Streaming is smart ETL
Processing
Ingest
In-Memory
Operational
Storage
Combine
Join, Enrich,
Group, Aggregate
Stream
Windowing, Event-
Time
Processing
Compute
Distributed and
Parallel
Computation
Transform
Filter, Clean,
Convert
Publish
In-Memory,
Subscriber
Notifications
Notify if response
time is 10% over 24
hour average, second
by second

23. @nicolas_frankel
• Real-time dashboards
• Decision making
• Recommendations
• Stats (gaming, infrastructure
monitoring)
• Prediction - often based on
algorithmic prediction
• Push stream through ML model
• Complex Event Processing
Use Case: Analytics and Decision Making

24. @nicolas_frankel
• Kafka
• Pulsar
Persistent event-storage systems

25. @nicolas_frankel
• Distributed
• On-disk storage
• Messages sent and read from a
topic
• Publish-subscribe
• Queue
• Consumer can keep track of the
offset
Kafka

26. @nicolas_frankel
• Apache Flink
• Amazon Kinesis
• IBM Streams
• Hazelcast Jet
• Apache Beam
• Abstraction over some of the above
• …
In-memory stream processing engines

27. @nicolas_frankel
• Apache 2 Open Source
• Single JAR
• Leverages Hazelcast IMDG
• Unified batch/streaming API
• (Hazelcast Jet Enterprise)
Hazelcast Jet

28. @nicolas_frankel
Hazelcast Jet

29. @nicolas_frankel
• Declaration (code) that defines and
links sources, transforms, and
sinks
• Platform-specific SDK (Pipeline API
in Jet)
• Client submits pipeline to the
Stream Processing Engine (SPE)
Concept: Pipeline

30. @nicolas_frankel
• Running instance of pipeline in SPE
• SPE executes the pipeline
• Code execution
• Data routing
• Flow control
• Parallel and distributed execution
Concept: Job

31. @nicolas_frankel
Imperative model
final String text = "...";
final Map<String, Long> counts = new HashMap<>();
for (String word : text.split("W+")) {
Long count = counts.get(word);
counts.put(count == null ? 1L : count + 1);
}

32. @nicolas_frankel
Declarative model
Map<String, Long> counts = lines.stream()
.map(String::toLowerCase)
.flatMap(
line -> Arrays.stream(line.split("W+"))
)
.filter(word -> !word.isEmpty())
.collect(Collectors.groupingBy(
word -> word, Collectors.counting())
);

33. @nicolas_frankel
• Multiple nodes
• Scalable storage and performance
• Elasticity
• Data stored, partitioned and
replicated
• No single point of failure
What Distributed Means to Hazelcast

34. @nicolas_frankel
Distributed Parallel Processing
Pipeline p = Pipeline.create();
p.drawFrom(Sources.<Long, String>map(BOOK_LINES))
.flatMap(line -> traverseArray(line.getValue().split("W+")))
.filter(word -> !word.isEmpty())
.groupingKey(wholeItem())
.aggregate(counting())
.drainTo(Sinks.map(COUNTS));
Data
Sink
Data
Source
from aggrmap filter to
Translate declarative code to a Directed Acyclic Graph

35. @nicolas_frankel
Node 1
Distributed Parallel Processing
read cmb
map
+
filter
acc sink
read cmb
map
+
filter
acc
Node 2
read cmb
map
+
filter
acc
sinkread cmb
map
+
filter
acc
Data
Source
Data
Sink
sink
sink

36. @nicolas_frankel
« Open data is the idea that some
data should be freely available to
everyone to use and republish as
they wish, without restrictions from
copyright, patents or other
mechanisms of control. »
--https://en.wikipedia.org/wiki/Open_data
Open Data

37. @nicolas_frankel
• France:
• https://www.data.gouv.fr/fr/
• Switzerland:
• https://opendata.swiss/en/
• European Union:
• https://data.europa.eu/euodp/en/data/
Some Open Data initiatives

38. @nicolas_frankel
1. Access
2. Format
3. Standard
4. Data correctness
Challenges

39. @nicolas_frankel
• Download a file
• Access it interactively through a
web-service
Access

40. @nicolas_frankel
In general, Open Data means Open
Format
• PDF
• CSV
• XML
• JSON
• etc.
Format

41. @nicolas_frankel
• Let’s pretend the format is XML
• Which grammar is used?
• A shared standard is required
• Congruent to a domain
Standard

42. @nicolas_frankel
Data correctness
"32.TA.66-43","16:20:00","16:20:00","8504304"
"32.TA.66-44","24:53:00","24:53:00","8500100"
"32.TA.66-44","25:00:00","25:00:00","8500162"
"32.TA.66-44","25:02:00","25:02:00","8500170"
"32.TA.66-45","23:32:00","23:32:00","8500170"

43. @nicolas_frankel
General Transit Feed Specification
”The General Transit Feed Specification (GTFS) […]
defines a common format for public transportation
schedules and associated geographic information.
GTFS feeds let public transit agencies publish their
transit data and developers write applications that
consume that data in an interoperable way.”

44. @nicolas_frankel
GTFS static model
Filename Required Defines
agency.txt Required Transit agencies with service represented in this dataset.
stops.txt Required Stops where vehicles pick up or drop off riders. Also defines stations and station entrances.
routes.txt Required Transit routes. A route is a group of trips that are displayed to riders as a single service.
trips.txt Required
Trips for each route. A trip is a sequence of two or more stops that occur during a specific
time period.
stop_times.txt Required Times that a vehicle arrives at and departs from stops for each trip.
calendar.txt
Conditionally
required
Service dates specified using a weekly schedule with start and end dates. This file is required
unless all dates of service are defined in calendar_dates.txt.
calendar_dates.txt
Conditionally
required
Exceptions for the services defined in the calendar.txt. If calendar.txt is omitted, then
calendar_dates.txt is required and must contain all dates of service.
fare_attributes.txt Optional Fare information for a transit agency's routes.

45. @nicolas_frankel
GTFS static model
Filename Required Defines
fare_rules.txt Optional Rules to apply fares for itineraries.
shapes.txt Optional Rules for mapping vehicle travel paths, sometimes referred to as route alignments.
frequencies.txt Optional
Headway (time between trips) for headway-based service or a compressed representation of fixed-
schedule service.
transfers.txt Optional Rules for making connections at transfer points between routes.
pathways.txt Optional Pathways linking together locations within stations.
levels.txt Optional Levels within stations.
feed_info.txt Optional Dataset metadata, including publisher, version, and expiration information.
translations.txt Optional Translated information of a transit agency.
attributions.txt Optional Specifies the attributions that are applied to the dataset.

46. @nicolas_frankel
GTFS dynamic model

47. @nicolas_frankel
• Open Data
• GTFS static available as
downloadable .txt files
• GTFS dynamic available as a REST
endpoint
Use-case: Swiss Public Transport

48. @nicolas_frankel
The available data model
Where’s the position?!

49. @nicolas_frankel
• Source: web service
• Split into trip updates
• Enrich with static trip data
• Enrich with static stop times data
• Transform hours into timestamp
• Enrich with static location data
• Sink: Hazelcast IMDG
The dynamic data pipeline

50. @nicolas_frankel
Architecture overview

51. @nicolas_frankel

52. @nicolas_frankel
Recap
• Streaming has a lot of benefits
• Leverage Open Data
• It’s the Wild West out there
• No standards
• Real-world data sucks!
• But you can get cool stuff done

53. @nicolas_frankel
• https://blog.frankel.ch/
• @nicolas_frankel
• https://jet.hazelcast.org/
• https://bit.ly/opendataswiss
• https://bit.ly/gtransportfs
• https://bit.ly/jet-train
Thanks a lot!