Enhancing Data Quality in MQTT-Based IoT Data Pipelines
Watch Webinar
Chapters
- 00:00 - Introduction
- 02:06 - Importance of Data Quality
- 03:39 - MQTT and Data Quality
- 05:04 - MQTT Data Validation Using Schemas and Policies
- 09:34 - How to Maximize the Business Value of Your Data using HiveMQ Data Hub
- 12:40 - Use Cases Discussing Why You Need Clean Data
- 19:34 - Demo
- 38:59 - Q&A
Webinar Overview
In IoT and IIoT environments using MQTT brokers for data transmission, data producers continuously dispatch MQTT messages across diverse topics. At the same time, a variety of services harness this data to construct the essential application logic, commonly known as a data pipeline.
In an enterprise set-up, the sheer number of data producers and consumers can be staggering, reaching millions in some instances. Managing this vast ecosystem presents significant challenges, and enforcing certain behaviors is necessary to keep producers and consumers decoupled and to enhance the resilience of data pipelines. We have you covered to address this challenge.
Watch Stefan Frehse, Engineering Manager at HiveMQ, Michal Piasecki, Product Manager at HiveMQ, and Michael Parisi, Product Marketing Manager at HiveMQ, delve into the world of MQTT data management with a focus on maximizing data quality and integrity. The webinar recording shows how schema validation and policy enforcement capabilities within an MQTT broker ensure data quality and integrity, ultimately maximizing the business value of the data.
Key Takeaways From this On-Demand Webinar:
Get practical strategies and best practices for managing MQTT data in high-volume, dynamic environments.
Learn how to harness the power of an integrated policy engine to streamline your data management processes and drive business success.
Get valuable insights into how organizations can ensure seamless data flow and maximize the business value of data transported by an MQTT platform.
Who Should Watch this On-Demand Webinar:
This webinar recording is for anyone in the IoT or IIoT space looking to enhance the data quality of their MQTT-based IoT data pipelines.
Michael Parisi
Mike Parisi was a Product Marketing Manager at HiveMQ who owned positioning for the core HiveMQ platform and HiveMQ Data Hub. Mike specializes in hosting training sessions and go-to-market plans for new products and features. He has extensive experience helping SaaS companies launch new high impact products and he revels in bringing together people and technology.
Stefan Frehse
Stefan Frehse is Engineering Manager at HiveMQ. He earned a Ph.D. in Computer Science from the University of Bremen and has worked in software engineering and in c-level management positions for 10 years. He has written many academic papers and spoken on topics including formal verification of fault tolerant systems, debugging and synthesis of reversible logic.
Michal Piasecki
Michal Piasecki was formerly a Senior Technical Product Manager at HiveMQ, with experience in delivering highly scalable products in the IoT space. With a background in Control Engineering and expertise in Smart Manufacturing and Industry 4.0, his experience uniquely positions him to excel in delivering highly scalable products in the IoT space.
