Decoupling the Execution Engine From Python’s Pandas with Aditya Parameswaran of Ponder

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

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