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real-time-recommendation-analytics

End-to-end retail analytics and recommendation platform using PySpark, Kafka, PostgreSQL, Power BI, PyTorch Two-Tower, Qdrant, FastAPI, Docker and Azure.

Details

External ID
1393371921
Source
GITHUB
Company
—
Product
real-time-recommendation-analytics
Website domain
github.com
Launched
Sept. 28, 2026
Cohort
—
Upvotes
96
Upvotes percentile
0.9211503971304125
Tags
—
Fetched at
Sept. 30, 2026, 5:01 p.m.
Updated at
Sept. 30, 2026, 5:01 p.m.

Enrichment

Theme
revenue optimization and billing analytics
Vertical
Retail & commerce
Function
Analytics & BI
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
recommendation and analytics platform for retail
Manually corrected
False

Could you build this?

Partial The individual components (FastAPI, Qdrant, Docker) can be set up via prompting, but establishing a real-time distributed stream pipeline (PySpark + Kafka + PyTorch Two-Tower training/inference) requires complex infrastructure engineering.

What it would actually take: Building a working prototype involves orchestrating Kafka for clickstream ingestion, PySpark Streaming for feature aggregation, a two-tower neural network trained in PyTorch, and Qdrant for approximate nearest neighbor vector search. The difficult aspects are distributed stream state management, low-latency online inference (<50ms), and feature store consistency between training and real-time serving.

Competitors

Other products that read as similar to this one — 190 launches clear the similarity bar, closest 8 shown.

Attention rank: #10 of 191 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 334 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a analytics & bi tool for Legal yet.