Welcome to the Proof of Usefulness Hackathon spotlight, curated by HackerNoon’s editors to showcase noteworthy tech solutions to real-world problems. Whether you’re a solopreneur, part of an early-stage startup, or a developer building something that truly matters, the Proof of Usefulness Hackathon is your chance to test your product’s utility, get featured on HackerNoon, and compete for $150k+ in prizes. Submit your project to get started! Welcome to the Proof of Usefulness Hackathon spotlight, curated by HackerNoon’s editors to showcase noteworthy tech solutions to real-world problems. Whether you’re a solopreneur, part of an early-stage startup, or a developer building something that truly matters, the Proof of Usefulness Hackathon is your chance to test your product’s utility, get featured on HackerNoon, and compete for $150k+ in prizes. Submit your project to get started! Proof of Usefulness Hackathon spotlight Proof of Usefulness Hackathon spotlight Proof of Usefulness Hackathon Proof of Usefulness Hackathon $150k+ Submit your project to get started Submit your project to get started In this interview, we catch up with Pradeep Kalluri to discuss the Real-Time Data Quality Monitor, an open-source solution designed to provide visibility into streaming data pipelines. We look at how this project leverages machine learning to offer a cost-effective alternative to enterprise data observability tools. Real-Time Data Quality Monitor What does Real-Time Data Quality Monitor do? And why is now the time for it to exist? An open-source real-time data quality monitoring dashboard that tracks 6 quality dimensions (completeness, timeliness, accuracy, consistency, uniqueness, validity) across streaming data pipelines. Built with Apache Kafka, dbt, and ML-powered anomaly detection using Isolation Forest, it processes 332K+ orders with sub-10ms latency and 93%+ quality scores. A cost-effective alternative to enterprise data observability tools, potentially saving companies over £100k annually. Now’s a good time for Real-Time Data Quality Monitor to exist because data complexity is increasing, and engineering teams need accessible, low-latency observability solutions that can catch quality issues before they impact business decisions without the barrier of enterprise-level pricing. Who does your Real-Time Data Quality Monitor serve? What’s exciting about your users and customers? Data engineers and analytics teams who need real-time visibility into data quality without expensive enterprise tools like Monte Carlo or Bigeye What technologies were used in the making of the Real-Time Data Quality Monitor? And why did you choose the ones most essential to your tech stack? The system is architected around Apache Kafka for high-throughput data streaming and dbt for robust data transformation. To ensure high-fidelity monitoring, the stack incorporates machine learning via Isolation Forest for anomaly detection, allowing the system to identify irregularities with sub-10ms latency. What is the traction to date for Real-Time Data Quality Monitor? Around the web, who’s been noticing? The project showcases its capabilities through a live dashboard that processes over 15,000 orders across six quality dimensions with high accuracy. Additionally, the open-source GitHub repository features ensemble ML testing, and the methodology behind processing 332K+ orders has been detailed in a technical article submitted for publication. Real-Time Data Quality Monitor scored a 54 proof of usefulness score (https://proofofusefulness.com/reports/real-time-data-quality-monitor) Real-Time Data Quality Monitor scored a 54 proof of usefulness score (https://proofofusefulness.com/reports/real-time-data-quality-monitor) https://proofofusefulness.com/reports/real-time-data-quality-monitor What excites you about this Real-Time Data Quality Monitor's potential usefulness? Most companies discover data quality issues only after they've impacted business decisions. This tool catches problems in real-time at sub-10ms latency, making data observability accessible to teams that can't afford £50k+ enterprise solutions. The ML anomaly detection automatically learns what "normal" looks like for your pipeline, reducing alert fatigue while catching genuine issues. Meet our sponsors Meet our sponsors Meet our sponsors Bright Data: Bright Data is the leading web data infrastructure company, empowering over 20,000 organizations with ethical, scalable access to real-time public web information. From startups to industry leaders, we deliver the datasets that fuel AI innovation and real-world impact. Ready to unlock the web? Learn more at brightdata.com. Bright Data: Bright Data is the leading web data infrastructure company, empowering over 20,000 organizations with ethical, scalable access to real-time public web information. From startups to industry leaders, we deliver the datasets that fuel AI innovation and real-world impact. Ready to unlock the web? Learn more at brightdata.com. Bright Data: brightdata.com Neo4j: GraphRAG combines retrieval-augmented generation with graph-native context, allowing LLMs to reason over structured relationships instead of just documents. With Neo4j, you can build GraphRAG pipelines that connect your data and surface clearer insights. Learn more. Neo4j: GraphRAG combines retrieval-augmented generation with graph-native context, allowing LLMs to reason over structured relationships instead of just documents. With Neo4j, you can build GraphRAG pipelines that connect your data and surface clearer insights. Learn more. Neo4j: more Storyblok: Storyblok is a headless CMS built for developers who want clean architecture and full control. Structure your content once, connect it anywhere, and keep your front end truly independent. API-first. AI-ready. Framework-agnostic. Future-proof. Start for free. Storyblok: Storyblok is a headless CMS built for developers who want clean architecture and full control. Structure your content once, connect it anywhere, and keep your front end truly independent. API-first. AI-ready. Framework-agnostic. Future-proof. Start for free. Storyblok: Start for free . Algolia: Algolia provides a managed retrieval layer that lets developers quickly build web search and intelligent AI agents. Learn more. Algolia: Algolia provides a managed retrieval layer that lets developers quickly build web search and intelligent AI agents. Learn more. Algolia: Learn more

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Real-Time Data Quality Monitor Earns a 54 Proof of Usefulness Score by Building an Open-Source Data Observability Dashboard

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