Mage.ai is a technology company that specializes in artificial intelligence solutions for e-commerce businesses. Their AI-powered platform helps businesses optimize their online stores, improve customer experience, and increase sales.

Mage

Mage.ai

🧙‍♂️ Mage is an open-source tool for building and running
data pipelines that transform your data.
Github: https://github.com/mage-ai/mage-ai
Slack: https://mage.ai/chat

Designer Fund

Gradient Ventures

James Beshara

John Riccitiello

Lenny Rachitsky

Scott Belsky

Alumni Ventures

Trajectory Ventures

Mana Ventures

Gaingels

🚨 Find out all the buzz in The Beekeeper release 0.9.62! 🐝
🧵 [Mage IO] Weaviate Integration
🔍 [Mage IO] Alogia Integration
💥 Dynamic SQL Blocks
🌊 [Kafka Streaming Sources] Offset & Partitions
⛴️ Configure Kubernetes Affinity & Tolerations
🐛 Bug fixes & more!

Details: https://lnkd.in/eRKEfkJE

#dataintegration #SQL #streaming #dataengineering #changelog #mage #mageai

🎉 A massive shoutout to our outstanding community member, Edmond Winston! 🙌 

From bug fixes to elevating docs and updating frontend tests, Edmond has become a true trailblazer in the data engineering community! 🌐🏆

Your unwavering dedication is truly commendable—keep up the stellar work, Edmond!

#community #opensource #contribution #thankyou #dataengineering

It's time for another Mage Tip in our Feature Callout Series! This week: parameterized execution 🚀

Have you ever wanted your data engineering pipelines to be more flexible and dynamic? With parameterized execution in Mage, you can do just that! 🎯

Parameterized execution allows you to define variables that change based on the date, time, or any other input you provide at the beginning of the run. This is especially useful when making API calls, as you can ensure you're always getting fresh data. 📊

In our latest demo video, I'll walk you through an example using a weather API. We show you how to fetch data from a source and use pipeline variables and parameterized runs to update the pipeline by day, fetching the latest data available. ☁️

With Mage, it's easy to access pipeline-level variables like the execution date and define start and end dates based on these variables. You can even supply latitude and longitude as variables, giving you the flexibility to fetch data from different locations. 🌍

By using parameterized execution in Mage, you can effortlessly update API calls, fetch the most recent data, and avoid any additional work. It's like magic! ✨

Read more in our docs here: https://lnkd.in/gAWfDXvA 

If you've got feedback or magical puns (always appreciated), slide into our Slack channel! We’ll see you next time.

#sql #analytics #data #dataengineering #datascience

We had a good day talking about Mage.ai and what we can all expect in the Generative Artificial Intelligence space over the next year in Hobart yesterday. We have completed a major data migration to Snowflake using Mage after I decided to take the leap from Airflow. We haven't looked back, and for data pipelines and orchestration Mage gives the power of coding and the ease of a great interface while moving Directed Acyclic Graph (DAG) paradigm forward. We are now starting to Orchestrate ML and LLM jobs in Snowflake from Mage - which is where the tool really started before going back to make ELT better. Added to that, the founder, Tommy Dang, is a Golden Bear. So if you are looking at the tools for data migration, or data transformations, you should really give Mage a look. Transparency over what is going on 'under the hood' is what you really want, so chuck that GUI and start doing some magic. https://www.mage.ai . #ShralpOn

What's going on here! Big thanks to Mage team for mentioning me on their blog bringing to me more folks from our great community. https://lnkd.in/eP5g7ZbR. Keep up the good work wizards!

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