Amplifying Timeseries Analytics: The Synergy of Seeq and Databricks

Brent Railey, Manager of Data Science, Chevron Phillips Chemical

Chris Herrera, Head of API & Interoperability, Seeq

Jai Karve, Solutions Architect, Databricks

As the concept of the “cloud lakehouse” matures and develops, Databricks has emerged as a key player in this space. Databricks is excellent at applying large-scale compute to data engineering, data science, and AI problems. However, Databricks is not a specialized time-series analytics tool, and does possess not out-the-box connectivity to industrial data sources.

On the other hand, Databricks has become a common store of IIoT data, with many companies landing the time-series data in their lakehouses and cloud datastore that Databricks compute engines can access.

Combining Seeq with Databricks together can have tremendous benefits–with Seeq being both a provider and a customer to Databricks. This session with outline patterns and practices, along with lessons learned, for Seeq and Databricks customers to scale-up their machine learning, IIoT, etc capability by conneqting (see what I did there?) Seeq and Databricks together.