Applying Geospatial Analytics at a Massive Scale Using Kafka, Spark and Elasticsearch on DC/OS

Explore how DC/OS and Mesos enable massive-scale geospatial analytics using Kafka, Spark, and Elasticsearch in this 41-minute conference talk by Adam Mollenkopf from Esri. Discover Esri's approach to establishing a foundational operating environment for high-velocity IoT data consumption, streaming analytics, and high-volume spatiotemporal data storage and querying. Learn about making DC/OS applications portable across public cloud providers, private cloud providers, and on-premise environments. Gain insights into real-time and big data capabilities in the ArcGIS platform through demonstrations and examples of temporal operators, aggregate points, and aggregate bins. Understand the deployment process, including installing DC/OS, Kafka, and Elasticsearch, and explore challenges such as autoscaling, metrics, stateful processing, and recurring batch analytics using Apache Spark and Metronome.

Introduction
Traditional approach
DCOS
Trinity
Project Trinity
Examples
Temporal Operators
Aggregate Points
Aggregate Bins
Demo
Deployment Portability
Deploy across different environments
Installing DCOS
Installing Kafka
Installing Elasticsearch
Kafka Source
Map Interface Demo
Simulations
Challenges
Open Source Extensions
Autoscaling
Metrics
Stateful Processing
Batch Analysis
Recurring Analytics