Voxxed Days Banff 2019
from Friday 20 September to Saturday 21 September 2019.
Guido Schmutz works for Trivadis, an IT services provider located in central Europe. He has more than 30 years of technology experience. At Trivadis he leads the Trivadis Architecture Board. He has long-time experience as developer, coach, trainer, and architect in the area of building complex Java EE and SOA-based solutions. In the past few years he mostly worked in Big Data / Fast Data projects with technologies such as Hadoop, Spark, Cassandra, Kafka as well as container technologies such as Docker, Mesos and Kubernetes. Guido is an Oracle Groundbreaker Ambassador and ACE director and a regular speaker at international conferences such as CodeOne, JavaOne, Kafka Summit, Voxxed Days, UKOUG conference and DOAG.
See also https://guidoschmutz.wordpress.com/
Most data visualization solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualization capabilities. One option is to first persist the data into a data store and then use a traditional data visualization solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualization tools might already integrate with the specific data store. An other option is to use a Streaming Visualization solution. This talk presents different architecture blueprints for integrating data visualization into a fast data solutions.
An important underlying concept behind location-based applications is called geofencing. Geofencing is a process that allows acting on users and/or devices who enter/exit a specific geographical area, known as a geo-fence. A geo-fence can be dynamically generated—as in a radius around a point location, or a geo-fence can be a predefined set of boundaries (such as secured areas, buildings, boarders of counties, states or countries). Geofencing lays the foundation for realising use cases around fleet monitoring, asset tracking, phone tracking across cell sites, connected manufacturing, ride-sharing solutions and many others. Many of the use cases mentioned above require low-latency actions taken place, if either a device enters or leaves a geo-fence or when it is approaching such a geo-fence. That’s where streaming data ingestion and streaming analytics and therefore the Kafka ecosystem comes into play. This session will present how location analytics applications can be implemented using Kafka and KSQL & Kafka Streams. It highlights the exiting features available out-of-the-box and then shows how easy it is to extend it by custom defined functions (UDFs).