Internet of Things (IoT) data frequently has a location and time component. Getting value out of this "geotemporal" data can be tricky. We'll explore when and how to leverage Cassandra, DSE Search and DSE Analytics to surface meaningful information from your geotemporal data.

1. When and Where are all the Things:
Geotemporal IoT Search and Analytics

2. Esri
Geographic Information System (GIS)
• Environmental Systems Research Institute (ESRI) was founded in 1969
• Esri develops GIS software
• Global Company with over 350,000 user organizations worldwide
Headquarters in Redlands, CA 80 Esri distributors worldwide

3. When and Where are all the Things
Agenda

4. High Velocity & Volume Geotemporal IoT Data
Use Cases
• Volunteered Geographic Information (VGI):
- GeoRSS, Instagram, Twitter, OpenStreetMap, …
• Moving Objects:
- Aircraft, Drones, Trucks, Cars, Railways, Vessels, People, …
• Sensor Networks:
- Weather Stations, Road Traffic, Utility Networks, Environmental Sensors, …
When and Where
are all the Things?

5. DataStax Enterprise
Applied to the IoT

6. Ingestion
of high velocity geotemporal IoT data

7. Ingestion
of high velocity & volume geotemporal IoT data
Ingestion When and Where
are all the Things?
• Sustain a single node throughput of at least tens of thousands of events per second
• Achieve near linear scalability of throughput when adding additional nodes
• Gracefully handle bursty data

8. Apache Kafka
Publish-subscribe messaging rethought as a distributed commit log
• Fast
- single broker can handle hundreds of MBs of reads and writes per second
• Scalable
- data streams are partitioned and spread over a cluster of machines
• Durable
- messages are persisted to disk and replicated within the cluster
• Distributed
- cluster-centric design that offers strong durability and fault-tolerance guarantees

9. Apache Spark
A fast and general engine for large-scale data processing
• Unified big data processing
- write streaming jobs the same way you write batch jobs
- can combine streaming with batch and interactive queries
• Spark apps can be written in Java, Scala, Python, and R

10. 1 node cluster benchmark c4.2xlarge (Windows 2012 Server R2): 8 vCPU, 15 GiB, 100GB SSD, 1,000 Mbps EBS
High Velocity & Volume Ingestion
Ingest 1 node
Spark Streaming
w/ Kafka
132k

11. High Velocity & Volume Ingestion
2 node cluster benchmark c4.2xlarge (Windows 2012 Server R2): 8 vCPU, 15 GiB, 100GB SSD, 1,000 Mbps EBS
Ingest 1 node 2 node
Spark Streaming
w/ Kafka
132k 282k

12. Streaming Analytics
on high velocity & volume geotemporal IoT data

13. Streaming Analytics
of high velocity & volume geotemporal IoT data
When and Where are all the Things?
Streaming
Analytics
• Configure the flow of events,
- the filtering and analytic steps to perform,
- what ingestion stream(s) to apply them to,
- and where to send the results.
Ingestion

14. KafkaUtils.createStream(ssc, …)
.map( event => FieldEnricher.enrich(event, …) )
.filter( event => IncidentDetector.evaluate(event, …) )
.map( event => FieldEnricher.enrich(event, …) )
.map( event => FieldMapper(event, …))
.saveTo…
=> DAG(Directed Acyclic Graph)
• Configure the flow of events,
- the filtering and analytic steps to perform,
- what ingestion stream(s) to apply them to,
- and where to send the results.
of high velocity & volume geotemporal IoT data
Streaming Analytics

15. GIS Tools for Hadoop
http://esri.github.io/gis-tools-for-hadoop/
• Esri Geometry API for Java:
- Geometry objects: points, lines, polygons
- Spatial relations: intersects, touches, overlaps, …
- Spatial operations: buffer, cut, union, …
• Spatial Framework for Hadoop
- Includes Spatial UDFs (User Defined Functions) that extend Hive
• GeoProcessing Tools for Hadoop
Ch. 8 Geospatial & Temporal Data Analysis

16. Demo
New York Taxi Cab Location Density Monitoring
High Velocity Geotemporal Analytics

17. Storage & Search
of high velocity & volume geotemporal IoT data

18. Storage
of high velocity & volume geotemporal IoT data
Ingestion Streaming
Analytics
Storage + Query
• Sustain a single-node write throughput of at least tens of thousands of events per second
• Achieve growth in volume capacity & write throughput when adding additional nodes

19. Cassandra
A Distributed Database with real-world Scalability
• Distributed, Scalable, and Highly Available
• Continuous Availability
- no single point of failure
• Easy data distribution across multiple data centers
• Spark Cassandra Connector
- https://github.com/datastax/spark-cassandra-connector

20. High velocity & volume storage c4.2xlarge (Windows 2012 Server R2): 8 vCPU, 15 GiB, 100GB SSD, 1,000 Mbps EBS
Storage 1 node 2 node 3 node 4 node 5 node
C* 23k 97k 141k 180k 220k
5 Node Cassandra Cluster Write Throughput
Ingest 1 node 2 node
Spark + Kafka 132k 282k

21. Ingestion Streaming
Analytics
Search
Storage + Query
• Efficiently access and search a large volume of data
- Query by any combination of id, time, space, and attributes
Search
high velocity & volume geotemporal IoT data

22. Search
high velocity & volume geotemporal IoT data
• Efficiently access and search a large volume of data
- Query by any combination of id, time, space, and attributes
- Made possible via DSE Search = C*/Solr + Lucene spatial types

23. Visualization
of high velocity & volume geotemporal IoT data

24. Visualization
of high velocity & volume geotemporal IoT data
DesktopWeb Device
Ingestion Streaming
Analytics
Search
Storage + Query
• ArcGIS API for JavaScript
- A lightweight way to embed maps in web apps
- Renders any Map or Feature Service compliant source
- https://www.esri.com/library/whitepapers/pdfs/geoservices-rest-spec.pdf
Visualization

25. High Velocity & Volume Visualization
Requirements
• Render with ability to do aggregation
- Aggregations calculated at various levels of detail and are specific to each user session
- when zoomed in raw features are returned and rendered

26. High Velocity & Volume Visualization
Requirements
• Render with ability to do aggregation
- Aggregations calculated at various levels of detail and are specific to each user session
- when zoomed in raw features are returned and rendered

27. High Velocity & Volume Visualization
Requirements
• Render with ability to do aggregation
- Aggregations calculated at various levels of detail and are specific to each user session
- when zoomed in raw features are returned and rendered

28. High Velocity & Volume Visualization
Aggregation

29. Demo
Ingestion, Storage, Continuous Analytics, and Visualization
High Velocity & Volume

30. Batch Analytics
of high velocity & volume geotemporal IoT data

31. Batch Analytics
of high velocity & volume geotemporal IoT data
DesktopWeb Device
Ingestion
Visualization
Streaming
Analytics
Batch
Analytics
Search
Storage + Query

32. High Velocity & Volume Analytics
Continuous and Batch Analytics

33. Customer Example
of applying geotemporal batch analytics on big data

34. Port of Rotterdam, courtesy of Frank Cremer
Vessel and Port Usage Behavioral Analytics
• 8th largest port in the world
• Largest port in Europe

35. Polyline Track Tool
Speed Tool
Line Crosses Tool
Density Tool
Port of Rotterdam
Vessel and Port Usage Behavioral Analytics

36. Port of Rotterdam
Polyline Track Analytics

37. Port of Rotterdam
Polyline Track Analytics

38. Port of Rotterdam
Density Analytics

39. Port of Rotterdam
Line Crosses Analytics

40. Port of Rotterdam
Line Crosses Analytics

41. The challenge of counting

42. D
d
Δ
(Lat,lon)
Where is Δ≃ 0 ?
Port of Rotterdam
Dredging Prioritization

43. Port of Rotterdam
Dredging Prioritization

44. When and Where are all the Things
Geotemporal IoT Search and Analytics Summary
• When working with high velocity & volume geotemporal IoT data we have found the best
technology selections are as follows:
- Ingestion = Spark Streaming + Kafka
- Streaming Analytics = Spark Streaming + GIS Tools for Hadoop
- Storage & Search = DataStax Enterprise + Spark Cassandra Connector
- Batch Analytics = DataStax Enterprise + Spark Core + GIS Tools for Hadoop
- Visualization = ArcGIS API for JavaScript
- GIS Tools for Hadoop
- Can be used as a basis to add spatial geometries, relations, and operators to Spark
- http://esri.github.io/gis-tools-for-hadoop/

45. Thank you