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Your Postgres is 100x slower than traditional OLAP engines. A deceptively simple OSS extension fixes this. Here's an interview where we dive into the deep engineering around how this is achieved. Joining me (and leading the conversation) is Marco Slot: an engineer with an EXTENSIVE and impressive career history...

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Stanislav Kozlovski

18,444 subscribers

16,910 просмотров • 7 месяцев назад •via X (Twitter)

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Комментарии: 3

Фото профиля Stanislav Kozlovski
Stanislav Kozlovski7 месяцев назад

also on other platforms: - YouTube: - Spotify: - Apple Podcasts: - Your favorite podcast player (via RSS):

Фото профиля wanziforever
wanziforever7 месяцев назад

is there a youtube link for this

Фото профиля PsudoMike 🇨🇦
PsudoMike 🇨🇦5 месяцев назад

The 100x gap isn't the real story. Postgres was built for OLTP and its execution model reflects that. Row-by-row is fine at transactional scale, terrible for scans across millions of rows. For serious analytics, a dedicated columnar store isn't premature optimization.

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27,789 просмотров • 4 месяцев назад

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110,500 просмотров • 6 месяцев назад

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9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 просмотров • 1 год назад