正在加载视频...

视频加载失败

返回首页

DeFi data just became model-ready. Introducing Query Builder for DefiLlama Sheets. Instead of writing complex formulas or stitching together multiple queries, you can now pull exactly the data you want into Sheets or Excel in a few clicks. > Build spreadsheet formulas visually > Query multiple projects in a...

DefiLlama's profile picture

DefiLlama.com

368,450 subscribers

21,665 次观看 • 5 个月前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Quick demo of Zero’s new background queries. Zero’s sync is query-based. Rather than specifying what data you want using rules or some other separate system, you just use queries. Right inside the client app, you do a query using a full sql-style language. You get filters, subqueries , limits, etc. Zero syncs the data backing these queries to the client. It’s important to realize Zero isn’t really a cache. It’s a replica. It’s eagerly replicating a precise snapshot of a slice of your database - the slice covered by the queries you have open. So there is never stale data in Zero. When you close a query we delete the rows uniquely returned by that query because we can no longer keep them up to date. Of course that kind of sucks for the common case of doing a query, navigating, then pressing “back”. Ideally we want that back nav to be fast. To address this Zero 0.17 adds background queries. You can add a ttl to a query and it will keep running and syncing in the background. This is much different than normal caching because this data stays up to date. If you make the same query again, the results will be *instantly* available *and already up to date with server*. If you make a different query the data from the background query is used client-side to answer the new query instantly if possible. This all happens completely automatically. Just by adding the ttl flag.
1:01

Quick demo of Zero’s new background queries. Zero’s sync is query-based. Rather than specifying what data you want using rules or some other separate system, you just use queries. Right inside the client app, you do a query using a full sql-style language. You get filters, subqueries , limits, etc. Zero syncs the data backing these queries to the client. It’s important to realize Zero isn’t really a cache. It’s a replica. It’s eagerly replicating a precise snapshot of a slice of your database - the slice covered by the queries you have open. So there is never stale data in Zero. When you close a query we delete the rows uniquely returned by that query because we can no longer keep them up to date. Of course that kind of sucks for the common case of doing a query, navigating, then pressing “back”. Ideally we want that back nav to be fast. To address this Zero 0.17 adds background queries. You can add a ttl to a query and it will keep running and syncing in the background. This is much different than normal caching because this data stays up to date. If you make the same query again, the results will be *instantly* available *and already up to date with server*. If you make a different query the data from the background query is used client-side to answer the new query instantly if possible. This all happens completely automatically. Just by adding the ttl flag.

Aaron Boodman

18,808 次观看 • 1 年前

🚀 Stop Merging Data Manually in Excel! 📊 😫 Still copying and pasting data from multiple tables? There’s a smarter way. ⚡ Excel’s Consolidate feature lets you combine data from multiple ranges into a single summary in just a few clicks—saving time and reducing errors. ✅ Merge data faster ✅ Eliminate repetitive manual work ✅ Improve accuracy and productivity ✅ Perfect for reports, budgeting, and data analysis 💡 Small Excel tricks like this can save you hours over the course of a year. 📌 Save this post so you don’t forget it when working with spreadsheets! ❤️ Like • 🔄 Repost • 💬 Share with someone who still copies and pastes data manually.
0:36

🚀 Stop Merging Data Manually in Excel! 📊 😫 Still copying and pasting data from multiple tables? There’s a smarter way. ⚡ Excel’s Consolidate feature lets you combine data from multiple ranges into a single summary in just a few clicks—saving time and reducing errors. ✅ Merge data faster ✅ Eliminate repetitive manual work ✅ Improve accuracy and productivity ✅ Perfect for reports, budgeting, and data analysis 💡 Small Excel tricks like this can save you hours over the course of a year. 📌 Save this post so you don’t forget it when working with spreadsheets! ❤️ Like • 🔄 Repost • 💬 Share with someone who still copies and pastes data manually.

Tips Excel

557,801 次观看 • 1 个月前

It's launch week . 5 days 5 launches Day 1: SQL Server Data Connector Now you can connect your SQL Server to Julius in just a few clicks and instantly explore your data. Track adoption, analyze performance, and access KPIs all without writing a single line of SQL
1:32

It's launch week . 5 days 5 launches Day 1: SQL Server Data Connector Now you can connect your SQL Server to Julius in just a few clicks and instantly explore your data. Track adoption, analyze performance, and access KPIs all without writing a single line of SQL

rahul

48,216 次观看 • 9 个月前

Introducing: enrich-excel Hermes Agent or OpenClaw🦞 can now enrich any Excel sheet with live web data. > uses our free Search and Fetch to find missing data > writes results directly into the running Excel via AppleScript All in just a few seconds and one Skill.
0:23

Introducing: enrich-excel Hermes Agent or OpenClaw🦞 can now enrich any Excel sheet with live web data. > uses our free Search and Fetch to find missing data > writes results directly into the running Excel via AppleScript All in just a few seconds and one Skill.

TinyFish

58,574 次观看 • 2 个月前

📊 Behind every great business is… a spreadsheet. Budgets, forecasts, inventory, project plans. It is where the real work happens. Now, Manus builds them for you. From a single prompt, you can generate full spreadsheets with structure, formulas, charts, and logic baked in. No syntax needed. No manual formatting. Whether you’re cleaning raw data, modeling growth, or tracking operations—Manus helps you go from idea to working system in seconds. Spreadsheets just got a serious upgrade. Now, anyone can be a spreadsheet master.
0:51

📊 Behind every great business is… a spreadsheet. Budgets, forecasts, inventory, project plans. It is where the real work happens. Now, Manus builds them for you. From a single prompt, you can generate full spreadsheets with structure, formulas, charts, and logic baked in. No syntax needed. No manual formatting. Whether you’re cleaning raw data, modeling growth, or tracking operations—Manus helps you go from idea to working system in seconds. Spreadsheets just got a serious upgrade. Now, anyone can be a spreadsheet master.

Manus

64,038 次观看 • 11 个月前

Ever heard of Connected Lenses on Spectacles ? Because together is more fun and this is a little glimpse into the future of AR. Connected Lenses let multiple people run the same Lens and share + save data across devices, whether you’re side by side or miles apart. That means you can build AR experiences your friends can jump into at the same time, then return to later, with the world still remembering what you all did.
29:29

Ever heard of Connected Lenses on Spectacles ? Because together is more fun and this is a little glimpse into the future of AR. Connected Lenses let multiple people run the same Lens and share + save data across devices, whether you’re side by side or miles apart. That means you can build AR experiences your friends can jump into at the same time, then return to later, with the world still remembering what you all did.

Alessio

79,134 次观看 • 6 个月前

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:
2:47

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

Andrew Ng

218,284 次观看 • 2 年前

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:
3:08

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,315 次观看 • 9 个月前

New startup company will put ‘Mini Data Centers’ on the side of residential homes all over America Companies Nvidia and PulteGroup are patterning with SPAN, “A major U.S home builder is now making small fractional data centers, or they call them nodes that can be put on the side of residential homes.” “These could negate the need to build as many hyperscalers and AI cloud providers Just tap into the node network like a regular data center now SPAN, collaborated with NVIDIA using its technology in the system span claims it can install 8,000 of these units about six times faster and at five times lower cost than the construction of a typical centralized 100 megawatt data center of the same size.” In return for letting this company put a mini data center on the side of your home, you’ll get a $150 credit on your electricity bill In some locations you could get free electricity and internet
1:35

New startup company will put ‘Mini Data Centers’ on the side of residential homes all over America Companies Nvidia and PulteGroup are patterning with SPAN, “A major U.S home builder is now making small fractional data centers, or they call them nodes that can be put on the side of residential homes.” “These could negate the need to build as many hyperscalers and AI cloud providers Just tap into the node network like a regular data center now SPAN, collaborated with NVIDIA using its technology in the system span claims it can install 8,000 of these units about six times faster and at five times lower cost than the construction of a typical centralized 100 megawatt data center of the same size.” In return for letting this company put a mini data center on the side of your home, you’ll get a $150 credit on your electricity bill In some locations you could get free electricity and internet

Wall Street Apes

258,222 次观看 • 2 个月前

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!
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 次观看 • 8 个月前

🥁 Sync any data source. Whatever context you need can now live in Notion. Our new database sync (powered by Workers) runs on our infrastructure and lets you sync any data source with an API into a Notion database. Sync customer data from Salesforce to build detailed reports. Or connect Strava and Spotify data to curate the perfect running playlist.
1:41

🥁 Sync any data source. Whatever context you need can now live in Notion. Our new database sync (powered by Workers) runs on our infrastructure and lets you sync any data source with an API into a Notion database. Sync customer data from Salesforce to build detailed reports. Or connect Strava and Spotify data to curate the perfect running playlist.

Notion

87,747 次观看 • 2 个月前

For a vault token to be traded or used as collateral, other protocols must trust your price feed. Stale data doesn't just limit growth, it prevents integration. Verified, real-time data is how you build a vault that the rest of DeFi can rely on. Full breakdown in the video: 👇
0:46

For a vault token to be traded or used as collateral, other protocols must trust your price feed. Stale data doesn't just limit growth, it prevents integration. Verified, real-time data is how you build a vault that the rest of DeFi can rely on. Full breakdown in the video: 👇

EO

12,982 次观看 • 6 个月前

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi
5:34

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 次观看 • 10 个月前

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.
14:14

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 次观看 • 1 年前

Introducing: Manus Data Visualization Say goodbye to spreadsheet chaos. Whether you’re analyzing competitors, prepping for a client meeting, or deep-diving into a market trend- Manus makes it effortless to: ✅ Turn messy data into clean, interactive charts ✅ Skip the hassle of pivot tables and clunky chart builders ✅ Create presentation-ready visuals tailored to your goals Just upload your raw dataset, describe what you need, and let Manus do the heavy lifting. Perfect for dashboards, reports, or your next big presentation. Less formatting. More intelligence. Data clarity is one prompt away.
0:41

Introducing: Manus Data Visualization Say goodbye to spreadsheet chaos. Whether you’re analyzing competitors, prepping for a client meeting, or deep-diving into a market trend- Manus makes it effortless to: ✅ Turn messy data into clean, interactive charts ✅ Skip the hassle of pivot tables and clunky chart builders ✅ Create presentation-ready visuals tailored to your goals Just upload your raw dataset, describe what you need, and let Manus do the heavy lifting. Perfect for dashboards, reports, or your next big presentation. Less formatting. More intelligence. Data clarity is one prompt away.

Manus

223,200 次观看 • 1 年前

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?
0:51

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Zach Wilson

20,368 次观看 • 1 年前

If your financial model doesn’t tell you how sensitive your outcomes are to key assumptions, you’re only seeing half the picture. That’s where Sensitivity Analysis steps in. Whether you're modeling EBITDA, company valuation, or cash flow, testing the impact of changes in assumptions—like growth rate, margin, or discount rate—is crucial for informed decision-making. 📊 3 Powerful Tools in Excel for Sensitivity Analysis: 1️⃣ Data Tables Easily test the impact of one or two input variables on a key output. Perfect for: Valuation scenarios, breakeven analysis, IRR stress tests. 2️⃣ Scenario Manager Create and store multiple input sets and compare outputs. Perfect for: Best case / base case / worst case planning. 3️⃣ Manual What-Ifs with Named Ranges + Excel Formulas Use dynamic formulas, dropdowns, and structured inputs to test anything. Perfect for: Custom, presentation-ready models with clear audit trails. 💡 Pro Tip: Combine sensitivity analysis with clear data visualization (charts, conditional formatting, slicers) to drive your story home. Finance isn't just about building the model—it's about telling the right story with the right levers. 👇 How do you use sensitivity analysis in your day-to-day modeling? HERE IS A VIDEO
3:04

If your financial model doesn’t tell you how sensitive your outcomes are to key assumptions, you’re only seeing half the picture. That’s where Sensitivity Analysis steps in. Whether you're modeling EBITDA, company valuation, or cash flow, testing the impact of changes in assumptions—like growth rate, margin, or discount rate—is crucial for informed decision-making. 📊 3 Powerful Tools in Excel for Sensitivity Analysis: 1️⃣ Data Tables Easily test the impact of one or two input variables on a key output. Perfect for: Valuation scenarios, breakeven analysis, IRR stress tests. 2️⃣ Scenario Manager Create and store multiple input sets and compare outputs. Perfect for: Best case / base case / worst case planning. 3️⃣ Manual What-Ifs with Named Ranges + Excel Formulas Use dynamic formulas, dropdowns, and structured inputs to test anything. Perfect for: Custom, presentation-ready models with clear audit trails. 💡 Pro Tip: Combine sensitivity analysis with clear data visualization (charts, conditional formatting, slicers) to drive your story home. Finance isn't just about building the model—it's about telling the right story with the right levers. 👇 How do you use sensitivity analysis in your day-to-day modeling? HERE IS A VIDEO

Bojan Radojicic

14,714 次观看 • 5 个月前

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.
0:24

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.

Denis Magda

28,503 次观看 • 5 个月前

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.
0:34

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Santiago

20,756 次观看 • 1 个月前