Jingyong14/HPDP02
Jul 2025 – Jul 2025
1 Month active
Languages Used
CSSDockerfileHTMLJavaJavaScriptMarkdownPythonSQL
Technical Skills
Big DataContent ManagementData AnalysisData EngineeringDockerEconomic Analysis
NicoleLim
PROFILE
Nicolelim
Nicole Lim developed an end-to-end news processing pipeline in the Jingyong14/HPDP02 repository, focusing on real-time sentiment analysis for Free Malaysia Today articles. She architected a Dockerized environment integrating Zookeeper, Kafka, Spark, and Elasticsearch, enabling scalable ingestion and analysis of news data. Using Python, she built a Kafka producer to scrape and publish articles, while a Spark streaming job applied a pre-trained sentiment model, exporting results to both CSV and Elasticsearch for downstream analysis. Nicole also updated technical documentation to cover economic news, financial reporting, and market trends, demonstrating depth in big data engineering, content management, and technical writing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
191,559
Activity Months1
Work History
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025: Implemented an end-to-end News Processing Pipeline with Kafka-Spark sentiment analysis for Free Malaysia Today articles, dockerized Zookeeper/Kafka/Spark/Elasticsearch, and added a Python producer to scrape and publish articles. A Spark streaming job applies a pre-trained sentiment model with results exported to CSV and Elasticsearch, enabling real-time sentiment signals and scalable storage. Updated market coverage documentation to reflect economic news, financial reports, market analysis, currency movements, and investment trends. Major bugs fixed: None reported this month.
2 Commits • 2 Features
Jul 1, 2025July 2025: Implemented an end-to-end News Processing Pipeline with Kafka-Spark sentiment analysis for Free Malaysia Today articles, dockerized Zookeeper/Kafka/Spark/Elasticsearch, and added a Python producer to scrape and publish articles. A Spark streaming job applies a pre-trained sentiment model with results exported to CSV and Elasticsearch, enabling real-time sentiment signals and scalable storage. Updated market coverage documentation to reflect economic news, financial reports, market analysis, currency movements, and investment trends. Major bugs fixed: None reported this month.
July 2025
Activity
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Quality Metrics
Correctness75.0%
Maintainability70.0%
Architecture75.0%
Performance70.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
CSSDockerfileHTMLJavaJavaScriptMarkdownPythonSQLShellYAML
Technical Skills
Big DataContent ManagementData AnalysisData EngineeringDockerEconomic AnalysisElasticsearchFinancial ReportingKafkaMachine LearningMarket ResearchSentiment AnalysisSparkTechnical WritingWeb Scraping
Repositories Contributed To
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