Ep. 168 Data In Motion with Kenny Gorman, Head of Streaming at MongoDB

In this enlightening episode, we have a conversation with Kenny Gorman, a key figure at MongoDB who focuses on data in motion and streaming data. We delve into the essential role of streaming data in the data-centric world of today, discussing its applications in diverse fields such as fraud detection, IoT devices, power grid management, and manufacturing.

Kenny enlightens us about the three primary patterns related to streaming data and MongoDB's importance as both a source and a destination for this data. He also shares the challenges developers face in terms of making sense of high velocity data, distilling information, and adjusting their mental models to work effectively with streaming data.

Kenny gives us a glimpse into MongoDB's roadmap, focusing on expanding its capabilities in the streaming data space to make developers' work easier and more efficient. He emphasizes MongoDB's efforts to enhance functionality, offer new features, and make things more accessible to their customers.

For those interested in further learning, Kenny recommends checking out MongoDB's documentation on Kafka Connect. He also mentions his upcoming participation at MongoDB .local New York City.

This episode is a must-listen for anyone involved in data management, particularly those keen on understanding and leveraging the power of streaming data. Don't miss out on Kenny's insightful thoughts and expert advice on this rapidly evolving field.

• Introduction (00:00 - 01:00): Introduction of the podcast and Kenny Gorman, an expert on data in motion and streaming data at MongoDB.
• Importance of Streaming Data (01:01 - 05:30): Kenny discusses the growing importance of streaming data, its applications in various fields including fraud detection, IoT devices, power grid management, and manufacturing, and how it's changing the way we view and use data.
• Three Patterns Related to Streaming Data (05:31 - 15:00): Kenny explains three primary patterns related to streaming data and the role of MongoDB as a source and destination for this data.
• Challenges in Streaming Data (15:01 - 23:00): Kenny delves into the challenges developers face when dealing with streaming data, including the difficulty in making sense of high velocity data, the need to distill meaningful information, and the necessary shift in mental models.
• Best Practices for Developers (23:01 - 29:30): Kenny shares some advice and best practices for developers working with streaming data and MongoDB, emphasizing the need to understand Kafka and how it can connect to MongoDB.
• MongoDB's Roadmap for Streaming Data (29:31 - 34:00): Kenny gives a glimpse into MongoDB's roadmap for streaming data, discussing their focus on enhancing functionality, introducing new features, and making things more accessible to their customers.
• Resources for Further Learning (34:01 - 36:00): Kenny recommends checking out MongoDB's documentation on Kafka Connect for those interested in learning more about streaming data and its applications.
• Upcoming Events (36:01 - 38:00): Kenny mentions his upcoming participation at dot local New York City and encourages listeners to attend.
• Conclusion (38:01 - End): The podcast host thanks Kenny for his time and the valuable insights he shared during the interview.