Webinar
Automate the Boring Stuff with User Defined Functions: Enhancing Performance for Analytics at Scale
MEET THE HOSTS: Dave Rench McCauley, Solutions Architect, Kinetica & Zhe Wu, Professional
Services, Kinetica In this Tech Talk we’ll discuss combining the optimization benefits of SQL with the
flexibility of a procedural language. By using User Defined Functions (UDFs) with the Kinetica
Streaming Data Warehouse, developers, analysts, and others can automate processes like ETL, common
analytical tasks, and data re-labeling to improve performance, especially when working with massive
datasets. We’ll discuss what’s unique about Kinetica’s UDF functionality and how it fits into the
context of other popular UDF implementations, in addition to providing a demo of Kinetica’s
capabilities in this arena. IN THIS TECH TALK WE’LL COVER: The value of User Defined Functions
Spark/Hive vs. SQL vs. Kinetica – benefits of each for UDF An overview of UDFs in Kinetica Distributed
vs. non-distributed UDFs Real-world use cases for UDFs Demo
MEET THE HOSTS: Dave Rench McCauley, Solutions Architect, Kinetica & Zhe Wu,
Professional Services, Kinetica
In this Tech Talk we'll discuss combining the optimization benefits of SQL with the flexibility of a
procedural language. By using User Defined Functions (UDFs) with the Kinetica Streaming Data
Warehouse, developers, analysts, and others can automate processes like ETL, common analytical tasks,
and data re-labeling to improve performance, especially when working with massive datasets. We'll
discuss what's unique about Kinetica's UDF functionality and how it fits into the context of other
popular UDF implementations, in addition to providing a demo of Kinetica's capabilities in this arena.
IN THIS TECH TALK WE'LL COVER:
- The value of User Defined Functions
- Spark/Hive vs. SQL vs. Kinetica – benefits of each for UDF
- An overview of UDFs in Kinetica
- Distributed vs. non-distributed UDFs
- Real-world use cases for UDFs
- Demo
