What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)| Listen on Anghami

Jan 28

Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 297) [RB]

VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies. Today we explain how vortex shedding, the Str ... Show More

33m 15s

Jan 10

AGI: The Dream We Should Never Reach (Ep. 296)

Also on YouTube Two AI experts who actually love the technology explain why chasing AGI might be the worst thing for AI's future—and why the current hype cycle could kill the field we're trying to save. Want to dive deeper? Head to datascienceathome.com for detailed show notes, c ... Show More

45m 41s

Dec 22

When Data Stops Being Code and Starts Being Conversation (Ep. 297)

Mark Brocato built Mockaroo—the tool that taught millions of developers how to fake data. Now, as Head of Engineering at Tonic.ai, he's building the AI agent that's making his own creation obsolete. In this episode, we explore why static test data can't survive the AI era, what i ... Show More

33m 37s

Sep 2021

Massively Parallel Data Processing In Python Without The Effort Using Bodo

<div class="wp-block-jetpack-markdown"><h2>Summary</h2> <p>Python has beome the de facto language for working with data. That has brought with it a number of challenges having to do with the speed and scalability of working with large volumes of information.There have been many ... Show More

1h 4m

Mar 2024

#454: Data Pipelines with Dagster

See the full show notes for this episode on the website at <a href="https://talkpython.fm/454">talkpython.fm/454</a>

58m 25s

Nov 2022

Analyze Massive Data At Interactive Speeds With The Power Of Bitmaps Using FeatureBase

<div class="wp-block-jetpack-markdown"><h2>Summary</h2> <p>The most expensive part of working with massive data sets is the work of retrieving and processing the files that contain the raw information. FeatureBase (formerly Pilosa) avoids that overhead by converting the data int ... Show More

59m 25s

Dec 2019

Building The Materialize Engine For Interactive Streaming Analytics In SQL

<div class="wp-block-jetpack-markdown"><h2>Summary</h2> <p>Transactional databases used in applications are optimized for fast reads and writes with relatively simple queries on a small number of records. Data warehouses are optimized for batched writes and complex analytical qu ... Show More

48m 7s

Jul 2023

#422: How data scientists use Python

See the full show notes for this episode on the website at <a href="https://talkpython.fm/422">talkpython.fm/422</a>

1h 2m

Sep 2022

#382: Apache Superset: Modern Data Exploration Platform

See the full show notes for this episode on the website at <a href="https://talkpython.fm/382">talkpython.fm/382</a>

1h 8m

Nov 2021

Exploring Processing Patterns For Streaming Data Integration In Your Data Lake

<div class="wp-block-jetpack-markdown"><h2>Summary</h2> <p>One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. With the improvements in streaming engines it is now possible to perform all of your data integration in near r ... Show More

52m 53s

Aug 2023

#425: Memray: The endgame Python memory profiler

See the full show notes for this episode on the website at <a href="https://talkpython.fm/425">talkpython.fm/425</a>

1h 10m

Oct 2023

Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

<h2>Summary</h2> <p>Building streaming applications has gotten substantially easier over the past several years. Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. Decodable was built with a mission of eliminat ... Show More

1h 8m

What data transformation library should ...