GlassFlow provides a serverless and production-ready setup for building and transforming event-driven data pipelines in Python, without the need for Kafka or Flink.

GlassFlow

When it comes to data streaming, organizations are using complex tech systems to meet user expectations. The challenges of constructing and managing event-driven data pipelines often require substantial data engineering efforts, hindering the adaptability of applications. 

GlassFlow's mission is to democratize real-time data processing, helping organizations of all sizes and Python engineers from diverse backgrounds unlock the full potential of streaming data. The product includes an easy-to-use setup that scales automatically with the performance needs of the pipelines and a serverless transformation engine that allows users to define applications as functions and events.

GlassFlow.dev

LinkedIn live webinar: How to build a data streaming pipeline for real-time anomaly detection with AI and alerting

Monitoring log data, detecting anomalies, and alerting come with challenges such as the volume and velocity of log data, complexity of log formats, high false positive rates, and integration with existing logging systems like application or database logs. 

AI can simplify the cleaning of data, convert it into a structured form, and identify security threats, audit issues, application failures, or other unusual and suspicious activities.

Join top experts in the data streaming and AI field! We’ll guide you through these challenges, helping you build a powerful solution using GlassFlow, OpenAI, and Slack in just minutes.

We look forward to seeing you there!

#webinar #StreamDataProcessing #datastreamingminutes

Thanks Swapnil Gupta for sharing your positive experience with GlassFlow 🚀 ✨

Use Case: Learn how to enrich classified ads in real-time with OpenAI, LangChain and GlassFlow.dev

How it works:
➡ Collect classified ads from data sources using managed connectors
➡ GlassFlow processes the ads, enriching them with additional information and categorizing them using Langchain and OpenAI
➡ Result: Stores enriched ads in Redis for quick retrieval and advanced search, providing a better user experience

This allows you to
• Enhance ad visibility and accuracy with enriched, well-organized ads
• Improve user engagement and satisfaction by ensuring ads are detailed and easy to find
• Integrate machine learning models that detect and filter out spam ads in real time

📚Learn more about setting up classified real-time ads: 
https://lnkd.in/dSiGKxt6

#langchain #AI #Python #TechInnovation #DataStreaming

Connect Google Pub/Sub (Google Cloud) with Glassflow - ingest events for data streaming with less effort

Why to use Google Pub/Sub with GlassFlow?
🌟 Perfect for building scalable, event-driven architectures and real-time applications that react to every event instantly.
🌟 As your data grows to billions, the data transformation pipeline scales automatically.
🌟 No code integration with easy configuration using UI and running it as a fully managed service.

📚 See what else you can do with GlassFlow
https://lnkd.in/dxP47tg5

#RealTime #googlepubsub #DataAnalytics

We are happy to share this fantastic feedback from Anurag Roy 😊✨Thanks!

DEV:GLASSFLOW

GlassFlow