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Data Legends Podcast with Wes Gelpi: Special 2 Part Series

Part 1 - Wes Gelpi: The Untapped Potential of Kubernetes

Leading a team of data and analytics professionals isn’t easy; it takes more than just understanding the goal. It’s about the journey and how the people on the journey collaborate.

Wes Gelpi, Director of Research & Development at SAS, joins us in a special 2-part episode. Gelpi has a rich history of taking challenging situations and running with them. From working in compensation to overhauling HR systems, they have a knack for getting to the heart of an issue and working with a team to find the best solution.

In part 1, Wes dives into his day-to-day efforts at SAS:

  • The two ways to lead a team for the best return on creativity
  • Why Kubernetes is an untapped potential for cloud application management
  • What critical skills are needed for a team to effectively work together


Wes Gelpi: The Untapped Potential of Kubernetes


Team leadership tools to peak creativity ROI

As a practiced expert in data analytics and custom engineering, Wes Gelpi shares how he jump-started his career in the virtual world. With a background in human resource analytics and spending some time in the retail sector, Wes decided it was time to take his skills to the next level.

Gelpi began developing data science functions for internal HR use and built custom data engineering visualizations and virtualization technology.

Now, directing independent research and development systems, Wes focuses on computing workload management to Kubernetes software engineering while managing multiple teams of professionals. When speaking about the groups Wes manages, he shares two ways to motivate and inspire his team to work creatively.

“The first is freedom for creativity, freedom for the mind to wander and think creatively about a problem. The second is connectivity to customer impact.” –Wes Gelpi

To produce work that returns your investment and connects the customer to the product, Wes has found it crucial to create a space that allows engineers' minds to think creatively.


A hidden mine of untapped potential

Considering all the different types of technology, Wes Gelpi believes Kubernetes, an automating software management system, is rising in this field.

“Kubernetes can be leveraged to drive stronger resiliency within your platforms and applications. It helps teams be more innovative by giving them a safe space to do work.” –Wes Gelpi

Gelpi agrees the adoption of Kubernetes by other companies is currently low. However, the world of data analytics and software engineering has yet to recognize the potential benefits.

Undoubtedly, when companies unbury the gold mine that is Kubernetes, they will appreciate the well of applications and benefits.

Read: A Simplified Guide to Kubernetes Monitoring


Critical skills to elevate effective teamwork

Having a safe space is critical for creatives to think effectively and work. This is particularly true when brainstorming new ideas or taking a novel approach to traditional work systems.

Likewise, a safe space is vital for the creative process of engineers and designers. But unfortunately, many organizations take the approach of micromanaging, disrupting the creative process of employees.

Wes Gelpi found a balance between ensuring the team is on-task and allowing creative freedom.

Preferring to check in at the end of the day with each team member allows employees to capitalize on the time they have available throughout the workday, driven by creativity and initiative. In addition, this will enable leaders to avoid instilling fear or non-essential checkpoints in the workplace.

Attributing team success to visualization and performance, Wel Gelpi argues that having a team or project vision is one thing while implementing the idea into something tangible is another thing entirely.

Listen to Part 1


Part 2 - The Data Management Triangle: Lake, Warehouse, Virtualization

In part 2 of our conversation with Wes Gelpi, Director of Research & Development at SAS, we dive into the importance and differences between data lakes, warehouses, and virtualization.

Gelpi has a rich history of taking challenging situations and running with them. From working in compensation to overhauling HR systems, they have a knack for getting to the heart of an issue and working with a team to find the best solution.

Hear about Wes’ work at SAS and learn about:

  • Data management through lakes, warehouses, and virtualization
  • How data lakes and warehouses can work together
  • Why data virtualization brings together lakes and warehouses for the end user


Navigating the triangle of data management

The process of managing data is complex. However, you can break it into three central systems: lakes, warehouses, and virtualization.

Delving specifically into the architecture industry, Wes Gelpi explains that there are three main factors to consider:

  1. The mixture of data that you’re working with
  2. The speed at which you need that data
  3. Who needs the data

To store data, you should extract, load, and transfer (ELT) with a focus on data retention policies. It’s also crucial to strategically keep data in the correct area.

The value proposition for the lake is the amount of storage which differs from the warehouse. The data warehouse is built for structure instead of space.

Watch: Modern Analytics: Data Lakes, Data Warehouses and Clouds


A collaboration between data lakes and warehouses

The value proposition for the lake is the amount of storage that can be housed. This is different than the warehouse, which is made for structure instead of space. By pairing the data lake and warehouse, organizations are offered a choice and can tailor data views depending on the situation or project.

“All of your data is stored in a lake or warehouse for a specific reason, and now you can create almost infinite views of that data in the virtualization space for your users to build custom views without having to change or replicate your physical data.” –Wes Gelpi

They work together by creating a space for data depending on whether a more structured or quantitative approach is required for the specific context.

Moreover, the ability of modern virtualization technologies allows for a customizable data view for various users and purposes. This technology allows data storage to be simplified and customizable and meets each user where they stand without significant technical involvement or manipulation.

More information about Wes and today’s topics:


Listen to Part 2

Subscribe to the Data Legends Podcast


Additional Resources

Read the Blog: Data Lake vs Data Warehouse

Watch the Video: Advanced Analytics - Data Architecture Best Practices for Advanced Analytics

Check out the Report: 2022 Cloud Data & Analytics Survey Report

About the Author, Courtney Pallotta

Courtney Pallotta
Courtney is passionate about building dynamic teams and developing marketing strategies that help customers understand and benefit from exciting technology. Outside of work, she and her husband enjoy cheering on their three children from many sidelines and discovering all things outdoors in the Rocky Mountains. More posts by Courtney Pallotta