risingwavelabs/awesome-stream-processing
Dec 2025 – Dec 2025
1 Month active
Languages Used
MarkdownPythonSQL
Technical Skills
KafkaNLPPythonRisingWavedata engineeringdata streaming
Jr_TS
PROFILE
Jr_ts
Contributed to the risingwavelabs/awesome-stream-processing repository by developing two end-to-end real-time data streaming features. Built a Live Crypto-Sentiment Analyzer demo that streams random news headlines via Kafka and applies sentiment scoring using a Python UDF with VADER, showcasing real-time NLP integration. Also implemented a Real-Time E-Commerce Conversion Funnel, tracking user journeys from page views to purchases using dbt models and Kafka streams within a RisingWave pipeline. Enhanced documentation for data generation, improving onboarding and clarity. The work demonstrated practical integration of Kafka, Python, and dbt for robust, real-time analytics pipelines, focusing on usability and reproducible streaming demonstrations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
869
Activity Months1
Your Network
10 peopleShared Repositories
10
Work History
December 2025
5 Commits • 2 Features
Dec 1, 2025December 2025 performance summary for risingwavelabs/awesome-stream-processing: Delivered two major features enabling real-time streaming demonstrations: Live Crypto-Sentiment Analyzer Tutorial and Demo; Real-Time E-Commerce Conversion Funnel using dbt and Kafka; Improved documentation readability for data generation; Across both features, demonstrated end-to-end real-time capabilities, with end-to-end pipeline using RisingWave, Kafka, VADER, and dbt; Key business value: faster onboarding, robust demo pipelines, clearer setup instructions; Technologies: Kafka, UDF, VADER, dbt, RisingWave streaming, Python/readme tooling.
5 Commits • 2 Features
Dec 1, 2025December 2025 performance summary for risingwavelabs/awesome-stream-processing: Delivered two major features enabling real-time streaming demonstrations: Live Crypto-Sentiment Analyzer Tutorial and Demo; Real-Time E-Commerce Conversion Funnel using dbt and Kafka; Improved documentation readability for data generation; Across both features, demonstrated end-to-end real-time capabilities, with end-to-end pipeline using RisingWave, Kafka, VADER, and dbt; Key business value: faster onboarding, robust demo pipelines, clearer setup instructions; Technologies: Kafka, UDF, VADER, dbt, RisingWave streaming, Python/readme tooling.
December 2025
Activity
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Quality Metrics
Correctness96.0%
Maintainability92.0%
Architecture96.0%
Performance92.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
MarkdownPythonSQL
Technical Skills
KafkaNLPPythonRisingWavedata engineeringdata streamingdbtreal-time data processingsentiment analysisstream processing
Repositories Contributed To
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