How to Build an IoT Data Lake

The Internet of Things (IoT) is one of the driving forces for the increase in today’s data volume and diversity. IoT platforms must enable users to leverage incoming data for immediate analysis/action as well as long-term historical analytics. For the latter, the data needs to be stored efficiently and made available for large-scale analytics.

This talk discusses both the challenges and solutions of using data lakes for long-term storage of IoT data, including:

How data is moved from the IoT platform to the data lake
How data is organized
How efficient querying by consumers (e.g., IoT platform user interface, business intelligence tools and machine learning applications) is achieved

Speakers

Chris Furlong

Chris Furlong is an experienced, versatile, and commercially astute IT leader with extensive expertise in product management and marketing, product development and pre-sales consultancy. Chris is focused on business intelligence, analytics, and data management technologies and is currently working in various areas of IoT.

Tim Doernemann

Tim Doernemann graduated with a degree in computer science from the University of Marburg, Germany, in 2006. During his doctoral program at the Distributed Systems Research Group of University of Marburg he worked on various topics around scheduling and quality of service for high-performance computing, grid computing and cloud computing applications. Since 2012, he has workeds as a developer and architect at the intersection of IoT and data analytics.

Ready to Get Started? Here Are Some Resources to Help

Webinars

Smart Data – Smart Factory with Octotronic and Dremio

Guides

What Is a Data Lakehouse?

The data lakehouse is a new architecture that combines the best parts of data lakes and data warehouses. Learn more about the data lakehouse and its key advantages.

Whitepaper

Simplifying Data Mesh for Self-Service Analytics on an Open Data Lakehouse

The adoption of data mesh as a decentralized data management approach has become popular in recent years, helping teams overcome challenges associated with centralized data architecture.