Visualizing Geospatial Data at Scale

As rare diseases are so rare, patients are often misdiagnosed for many years, or never correctly diagnosed. Electronic health records hold much important information that can be used to correctly diagnose and treat these patients, but identification of phenotypic sets is hard to extract from reams of data.

Key topics discussed during the webinar: *Data in Motion pitfalls and opportunities *Validating data doesn’t equate to slowing down your process *Comparing and contrasting approaches *The missing link in data governance explained

See what a JSON document looks like, understand how to read it, and learn how to convert it to a standard relational data model.

Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake a place for unmodeled and vast data will be provisioned widely in 2019. Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.