Webinars

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. In response, 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.Cloud data lakes are cost-efficient and scale almost infinitely. However, data lakes only provide low-level primitives that must be used appropriately to unleash their full potential.This talk discusses both the challenges and solutions of using data lakes as the essential building blocks for long-term storage of IoT data, such as how data is moved from the IoT platform to the data lake, how data is organized, and how efficient querying by various consumers (e.g., IoT platform user interface, business intelligence tools and machine learning applications) is achieved.

Topics Covered

Data Lake Storage

Speakers

Tim Doernemann

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.

Michael Cammert

Michael Cammert

Michael Cammert graduated in with a degree in computer science from the University of Marburg, Germany, in 2002. During his time as a research assistant at the Database Research Group of University of Marburg, he worked on data stream processing. In 2007 he co-founded the complex event processing startup RTM Realtime Monitoring GmbH which was acquired by Software AG in 2010. Since then, he has worked as a developer and manager on various projects, currently at the intersection of IoT and data analytics. In 2014, Michael received his Ph.D. from the University of Marburg.

Ready to Get Started? Here Are Some Resources to Help

Case Study

Case Study

Dremio Supports Moonfare’s High-Performance Culture with a High-Performance Lakehouse

Moonfare replaced a PostgreSQL-based data warehouse on Amazon Web Services (AWS) with a Dremio data lakehouse to offer data engineers, analysts and business users a high performance platform for business intelligence and predictive analytics empowering them to make better data-driven decisions.

read more

Case Study

Case Study: DB Cargo Gives Users the Green Light to All Data with Dremio

Deutsche Bahn Group (DB) is one of the world's leading mobility and logistics companies. The DB Cargo business unit manages DB's rail freight business.

read more
Case Study

Case Study

Case Study: Amazon Accelerates Supply Chain Decision Making with Dremio

Amazon's Supply Chain Finance Analytics team developed a new analytics architecture with Dremio to simplify ETL processes, accelerate queries, and provide analytics on a unified view of the data.

read more

Get Started Free

No time limit - totally free - just the way you like it.

Sign Up Now

See Dremio in Action

Not ready to get started today? See the platform in action.

Watch Demo

Talk to an Expert

Not sure where to start? Get your questions answered fast.

Contact Us