Richard Garris

Executive (VP, GM), Databricks

I am a seasoned data and analytics professional with 18 years of experience both as an architect and team leader. Experienced in data architecture, data management, business transformation, data strategy, data science, traditional machine learning, machine learning platforms, open source technologies and cloud.

Past sessions

Summit 2021 Architecting Agile Data Applications for Scale

May 27, 2021 05:00 PM PT

Data analytics and reporting platforms historically have been rigid, monolithic, hard to change and have limited ability to scale up or scale down. I can't tell you how many times I have heard a business user ask for something as simple as an additional column in a report and IT says it will take 6 months to add that column because it doesn't exist in the datawarehouse. As a former DBA, I can tell you the countless hours I have spent "tuning" SQL queries to hit pre-established SLAs. This talk will talk about how to architect modern data and analytics platforms in the cloud to support agility and scalability. We will include topics like end to end data pipeline flow, data mesh and data catalogs, live data and streaming, performing advanced analytics, applying agile software development practices like CI/CD and testability to data applications and finally taking advantage of the cloud for infinite scalability both up and down.

In this session watch:
Richard Garris, Executive (VP, GM), Databricks

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Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do you deploy these ML model to a production environment? How do you embed what you've learned into customer facing data applications? In this talk I will discuss best practices on how data scientists productionize machine learning models, do a deep dive with actual case studies, and show live tutorials of a few example architectures and code in Python, Scala, Java and SQL. Session hashtag #SFexp5