[Demo] Design Event-Driven Microservices for Cloud → Register Now

Online Talk

Extracting value from IoT data with Azure and Confluent Cloud

Watch Now

Available On-Demand

Due to explosion of IoT, we have streaming data that needs to be processed in real-time. This needs to be made available for applications as well as analytics scenarios such as anomaly detection. This webinar presents a solution using Confluent Cloud on Azure, Azure Cosmos DB and Azure Synapse Analytics which can be connected in a secure way within Azure VNET using Azure Private link configured on Kafka clusters.

Abhishek is a Senior Program Manager in the Azure Cosmos DB team at Microsoft. Previously, in his role as a Developer Advocate, he worked on Kafka, Databases, Kubernetes and related open-source projects. He is also a Confluent Community Catalyst, loves technical writing and sharing knowledge through blogs, books, etc.

Gianluca is partner solution engineer at Confluent, responsible for technical enablement of partners in EMEA. With over 10 years of experience covering different roles (solution engineer, professional services consultant & trainer, and developer) in different countries (Italy, Ireland, and Germany), he has experience across event streaming, big data, business intelligence, and data integration. In his leisure time, he is studying toward his economics degree, reads about tech, plays guitar and enjoys discovering the world again through his daughter’s eyes.

Additional Resources

cc demo

Confluent Cloud Demo

Join us for a live demo of Confluent Cloud, the industry’s only fully managed, cloud-native event streaming platform powered by Apache Kafka
kafka microservices

Kafka Microservices

In this online talk series, learn key concepts, use cases and best practices to harness the power of real-time streams for microservices architectures
Image-Event-Driven Microservices-01

e-book: Microservices Customer Stories

See how five organizations across a wide range of industries leveraged Confluent to build a new class of event-driven microservices