Episode description

Highlights from this week’s conversation include:

• Aditya’s background and journey in the data space (2:47)
• What does Ponder do? (5:18)
• 101 on Pandas and why people utilize it (6:42)
• The challenge of translating Pandas to a big data platform (16:11)
• Data Warehouses and ML workflows (21:27)
• The differences in the “zoo” of data languages (26:56)
• Why do ML and data engineering have to be so different in languages? (34:39)
• Builders should be adapting to the users and not the other way around (39:32)
• Will we see a singular data interface in the future? (46:19)
• Aditya’s most surprising discovery in his research (50:40)
• Final thoughts and takeaways (53:18)

Read more of Aditya's work: 

• Pandas vs. SQL – Part 1: The Food Court and the Michelin-Style Restaurant
• Pandas vs. SQL – Part 2: Pandas Is More Concise
• Pandas vs. SQL – Part 3: Pandas Is More Flexible
• Pandas vs. SQL – Part 4: Pandas Is More Convenient

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.

RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.