This code story describes CSE's work with ZenCity to create a data pipeline on Azure Databricks supported by a CI/CD pipeline on TravisCI. The aim of the collaboration was to create a pipeline capable of processing a stream of social posts, analyzing them, and identifying trends.
We achieved zero-downtime reconfiguration and management of the Spark Streaming job used in Project Fortis with Azure Service Bus.
The ability to correctly identify entities, such as places, people, and organizations, adds a powerful level of natural language understanding to applications. This post introduces a MIT-licensed one-click deployment to Azure for web services that lets developers get started with a wide range of natural language tasks in 5 minutes or less, by consu...
This post describes in detail the Azure Cognitive Services speech-to-text WebSocket protocol and shows how to implement the protocol in Java. This enables us to transcribe audio to text in near real-time. We then show how to feed the transcribed radio into a pipeline based on Spark Streaming for further analysis, augmentation, and aggregation. The ...
Using GraphQL and Azure to create a data processing pipeline for identifying trends and providing insights about global humanitarian crises.
