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Let Postgres own the Iceberg catalog and delegate analytics to DuckDB. The result => transactional lakehouse updates with fast analytical queries. This isn’t a concept. It’s exactly what pg_lake delivers today. pg_lake combines a set of extensions and components that let you query and modify Iceberg tables (and other... lakehouse formats) directly from Postgres. DuckDB is used to accelerate analytical queries and runs in a sidecar process called pgduck_server, which communicates with Postgres during query execution. How it works (diagram below): 1. An application sends a query to Postgres to calculate unrealized PnL (Profit and Loss) for the Disney ticker. 2. Postgres parses the query and identifies the part that computes the average price from historical lakehouse data. 3. That part is forwarded to pgduck_server for accelerated execution. 4. pgduck_server delegates execution to DuckDB, which queries the lakehouse (reusing cached data if available). 5. DuckDB computes the average price and returns it to Postgres. 6. Postgres joins the result with local portfolio data and computes the unrealized PnL. 7. The final result is returned to the application.show more

Denis Magda

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28,473 次观看 • 4 个月前 •via X (Twitter)

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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 around PostgreSQL: 👉 Created pg_cron in 2017 (3.7k stars) - a tool to run cron-jobs in Postgres 👉 Built pg_incremental - fast, reliable, incremental batch processing inside PostgreSQL itself 👉 co-created pg_lake (after working on Crunchy Data's Warehouse, and getting acquired into Snowflake) 👉 Helped get pg_documentdb (MongoDB-on-Postgres) off the ground Marco Slot is a world-class expert in Postgres extensions. He seriously impressed me with his knowledge over the course of a private LinkedIn conversation, and now that I type out his resume - I understand where it came from. He should be on everyone's radar. So I brought him on the pod. In our full 2-hour deep-dive, we went over: • 🔥 how pg_lake makes analytics 100x faster (literally) • 🔥 perf internals like vectorized execution & CPU branching • 🤔 practical differences between OLTP and OLAP database development (and the age-old mission in uniting both) • 🤔 how (and why) pg_lake intercepts query plans and delegates parts of the query tree to DuckDB • 💡 why Postgres is architecturally terrible at analytical queries (and how vectorized execution fixes this) • 💡 Marco's hard-won experience through a decade+ career in Postgres • 🏆 Iceberg's role as the TCP/IP for tables • 🏆 what the real moat of PostgreSQL is Developments like pg_lake are a real reason why "Just Use Postgres" is much more than a meme, and it'll continue to dominate discourse. I promise you will learn a lot from this episode. Timestamps: (0:02) What is pg_lake? (2:23) Postgres' 100x slower problem and columnar storage experiments they had to make Postgres fast for analytics (6:00) practical examples and internals (16:20) perf internals - vectorized execution & CPU optimization (23:00) pg_lake architecture (why DuckDB isn't embedded) and the connection-per-process issue (29:16) how pg_lake intercepts the query plan tree and delegates parts to DuckDB (41:09) Iceberg catalogs (48:24) postgres to iceberg ingestion patterns (and pg_incremental) (53:40) Marco's (long) career: early AWS, Citus, Microsoft, Crunchy Data & Snowflake (1:04:20) Marco's observations around the merging between OLTP and OLAP (and the subtle dev differences there) (1:15:30) reverse ETL (1:33:08) Iceberg as the TCP/IP for tables (1:35:00) Marco's thoughts on the "Just Use Postgres" fever

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 around PostgreSQL: 👉 Created pg_cron in 2017 (3.7k stars) - a tool to run cron-jobs in Postgres 👉 Built pg_incremental - fast, reliable, incremental batch processing inside PostgreSQL itself 👉 co-created pg_lake (after working on Crunchy Data's Warehouse, and getting acquired into Snowflake) 👉 Helped get pg_documentdb (MongoDB-on-Postgres) off the ground Marco Slot is a world-class expert in Postgres extensions. He seriously impressed me with his knowledge over the course of a private LinkedIn conversation, and now that I type out his resume - I understand where it came from. He should be on everyone's radar. So I brought him on the pod. In our full 2-hour deep-dive, we went over: • 🔥 how pg_lake makes analytics 100x faster (literally) • 🔥 perf internals like vectorized execution & CPU branching • 🤔 practical differences between OLTP and OLAP database development (and the age-old mission in uniting both) • 🤔 how (and why) pg_lake intercepts query plans and delegates parts of the query tree to DuckDB • 💡 why Postgres is architecturally terrible at analytical queries (and how vectorized execution fixes this) • 💡 Marco's hard-won experience through a decade+ career in Postgres • 🏆 Iceberg's role as the TCP/IP for tables • 🏆 what the real moat of PostgreSQL is Developments like pg_lake are a real reason why "Just Use Postgres" is much more than a meme, and it'll continue to dominate discourse. I promise you will learn a lot from this episode. Timestamps: (0:02) What is pg_lake? (2:23) Postgres' 100x slower problem and columnar storage experiments they had to make Postgres fast for analytics (6:00) practical examples and internals (16:20) perf internals - vectorized execution & CPU optimization (23:00) pg_lake architecture (why DuckDB isn't embedded) and the connection-per-process issue (29:16) how pg_lake intercepts the query plan tree and delegates parts to DuckDB (41:09) Iceberg catalogs (48:24) postgres to iceberg ingestion patterns (and pg_incremental) (53:40) Marco's (long) career: early AWS, Citus, Microsoft, Crunchy Data & Snowflake (1:04:20) Marco's observations around the merging between OLTP and OLAP (and the subtle dev differences there) (1:15:30) reverse ETL (1:33:08) Iceberg as the TCP/IP for tables (1:35:00) Marco's thoughts on the "Just Use Postgres" fever

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We're excited to announce duckdb-skills, a DuckDB plugin for Claude Code! We think the embedded nature of DuckDB makes it a perfect companion for Claude in your local workflows. The skills supported include: + read-file and query – uses DuckDB's CLI to query data locally, unlocking easy access to any file that DuckDB can read. + read-memories – a clever idea to store your Claude memories in DuckDB and query them at blazing speed. These are powered by two additional skills: + attach-db – gives Claude a mechanism to manage DuckDB state through a .sql file linked to your project. + duckdb-docs – uses a remote DuckDB full-text search database to query the DuckDB docs and answer all of your (and Claude's own) questions.

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Aaron Boodman

18,808 次观看 • 1 年前

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Tyler Shukert

24,973 次观看 • 7 个月前

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Sam Willis

55,403 次观看 • 1 年前

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Gwen (Chen) Shapira

27,599 次观看 • 1 年前

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!

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 次观看 • 7 个月前

Introducing pg_strict for Postgres. Our new extension adds a safety net to Postgres, catching dangerous queries before they run.

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PlanetScale

114,704 次观看 • 5 个月前

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murat 🍥

50,537 次观看 • 4 个月前

Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa Links below ↓

Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa Links below ↓

dale

14,888 次观看 • 28 天前

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

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Charly Wargnier

20,181 次观看 • 4 个月前

We re-architected Neon Auth to integrate it directly into the Neon platform. Auth now lives in your Postgres database and branches with your data. Built on Better Auth tech and available today 🧵

We re-architected Neon Auth to integrate it directly into the Neon platform. Auth now lives in your Postgres database and branches with your data. Built on Better Auth tech and available today 🧵

Neon Postgres

34,908 次观看 • 6 个月前