Introduction
This is Abe from the Lakehouse Department of the GLB Division. I wrote an article summarizing the content of the session based on the report by Gibo, who is participating in Data + AI SUMMIT2023 (DAIS) on site.
Articles about the session at DAIS are summarized on the special site below. I would appreciate it if you could see this too.
Let's get down to business!
Rivian data utilization and introduction of Databricks Lakehouse
This time, I'm featuring a talk on how to effectively automate a secure Lakehouse using Databricks. Rivian engineer Vadivel Selvaraj and manager Jason Shiverick spoke.
Aimed at data engineers, data architects, data scientists, and business leaders.
Collection and utilization of vehicle data
Rivian collects terabytes of data per day from 32,000 vehicles. This data is used for purposes such as:
- Improved product design
- Fleet health monitoring
- Improved customer experience
By utilizing these data, Rivian aims to improve business efficiency and competitiveness.
Data integration with Databricks Lakehouse
Rivian uses Databricks Lakehouse to aggregate data and integrate it into Delta Lake. This reduces architectural complexity and makes data easier to manage and analyze.
Databricks Lakehouse has the following features.
- Architecture that combines data warehouse and data lake functionality
- Enables high-speed data processing and analysis
- Enhanced security and data governance
Automate resource provisioning with Terraform
This talk introduced how to effectively automate a secure Lakehouse using Databricks. Among them, automation of resource provisioning using Terraform was taken up.
Terraform has the following features.
- You can manage your infrastructure with code
- Compatible with multiple cloud providers
- Create, update, and delete resources automatically
Terraform enables efficient resource provisioning for Databricks Lakehouse. This can help accelerate data utilization and grow your business.
Summary
Rivian leverages vast amounts of vehicle data effectively with Databricks Lakehouse. In addition, by automating resource provisioning using Terraform, we support the promotion of data utilization and business growth. In the future, optimization of data utilization and introduction of new technologies are expected.
Conclusion
This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.
Translated by Johann
Thank you for your continued support!
