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תוכן מסופק על ידי Rudderstack. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Rudderstack או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
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259: AI is All About Working with Data with Kostas Pardalis of typedef

1:02:17
 
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Manage episode 502714253 series 3264623
תוכן מסופק על ידי Rudderstack. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Rudderstack או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

This week on The Data Stack Show, Eric and John welcome back Kostas Pardalis, long-time co-host of the Data Stack Show and now Co-Founder of typedef. The group discusses the rapid evolution of AI and data infrastructure. The conversation also explores how AI is accelerating industry change, the challenges of integrating large language models (LLMs) into data workflows, and the limitations of current semantic layers. Kostas shares insights on building next-generation query engines, the importance of using familiar engineering paradigms, and the need to make AI seamless and almost invisible in user experiences. Key takeaways include the necessity of practical, incremental innovation, the reality behind AI hype, strategies for making advanced data tools accessible and reliable for engineers and businesses alike, and so much more.
Highlights from this week’s conversation include:

  • Kostas’s Background and Career Timeline (1:10)
  • Transition from RudderStack to Starburst Data (4:25)
  • AI Acceleration and Industry Impact (9:37)
  • AI Hype, Investment, and Polarized Reactions (12:05)
  • Historical Parallels and Tech Adoption (13:54)
  • AI Disrupting Tech Workers and Internal Drama (18:56)
  • Experimentation Phase and Future AI Applications (24:01)
  • Invisible AI and User Experience (28:21)
  • AI in Data Infrastructure and LLMs (34:24)
  • SQL, LLMs, and Engineering Solutions (36:35)
  • Standardization, Semantic Layers, and Data Modeling (41:01)
  • Introduction to typedef (45:49)
  • Productionizing AI Workloads with typedef (51:36)
  • Familiarity, Reliability, and Engineering Best Practices (57:24)
  • Security, Enterprise Concerns, and Open Source Models (1:00:48)
  • Final Thoughts and Takeaways (1:01:47)

The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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.


Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  continue reading

490 פרקים

Artwork
iconשתפו
 
Manage episode 502714253 series 3264623
תוכן מסופק על ידי Rudderstack. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Rudderstack או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

This week on The Data Stack Show, Eric and John welcome back Kostas Pardalis, long-time co-host of the Data Stack Show and now Co-Founder of typedef. The group discusses the rapid evolution of AI and data infrastructure. The conversation also explores how AI is accelerating industry change, the challenges of integrating large language models (LLMs) into data workflows, and the limitations of current semantic layers. Kostas shares insights on building next-generation query engines, the importance of using familiar engineering paradigms, and the need to make AI seamless and almost invisible in user experiences. Key takeaways include the necessity of practical, incremental innovation, the reality behind AI hype, strategies for making advanced data tools accessible and reliable for engineers and businesses alike, and so much more.
Highlights from this week’s conversation include:

  • Kostas’s Background and Career Timeline (1:10)
  • Transition from RudderStack to Starburst Data (4:25)
  • AI Acceleration and Industry Impact (9:37)
  • AI Hype, Investment, and Polarized Reactions (12:05)
  • Historical Parallels and Tech Adoption (13:54)
  • AI Disrupting Tech Workers and Internal Drama (18:56)
  • Experimentation Phase and Future AI Applications (24:01)
  • Invisible AI and User Experience (28:21)
  • AI in Data Infrastructure and LLMs (34:24)
  • SQL, LLMs, and Engineering Solutions (36:35)
  • Standardization, Semantic Layers, and Data Modeling (41:01)
  • Introduction to typedef (45:49)
  • Productionizing AI Workloads with typedef (51:36)
  • Familiarity, Reliability, and Engineering Best Practices (57:24)
  • Security, Enterprise Concerns, and Open Source Models (1:00:48)
  • Final Thoughts and Takeaways (1:01:47)

The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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.


Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  continue reading

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