a16z Podcast

Data Alone Is Not Enough: The Evolution of Data Architectures

Oct 23, 2020

Ali Ghodsi, CEO and Founder of Databricks, dives into the evolution of data architectures, highlighting their journey from traditional warehousing to modern solutions. He discusses the crucial integration of tools to extract value from diverse data types. Insights on the differences between traditional analytics and machine learning, the complexities of data lakes versus warehouses, and innovative solutions like data frames offer a fresh perspective. Ghodsi also emphasizes real-time data management's growing importance and the best practices for a robust data infrastructure.

21:53

Creator website

Episode guests

Ali Ghodsi

AI Chapters

Episode notes

Intro

4min

Contrasting Analytics and Machine Learning in Data Architecture

2min

Bridging Traditional Data Models with Machine Learning Innovations

2min

Navigating the Complexities of Data Lakes and Warehouses

2min

Navigating Modern Data Architectures

12min

Data, data, data – it’s long been a buzzword in the industry, whether big data, streaming data, data analytics, data science, even AI & machine learning — but data alone is not enough: it takes an entire system of tools and technology to extract value from data.

A multibillion dollar industry has emerged around data tools and technologies. And with so much excitement and innovation in the space: how exactly do all these tools fit together?

This podcast – a hallway style conversation between Ali Ghodsi, CEO and Founder of Databricks, and a16z general partner Martin Casado – explores the evolution of data architectures, including some quick history, where they’re going, and a surprising use case for streaming data, as well as Ali’s take on how he’d architect the picks and shovels that handle data end-to-end today.

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