Freelancers using KafkaFreelancers using Kafka
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Drivyesh Modi's avatar
Drivyesh Modi
India
Growth is the only constant to achieve Perfection
Growth is the only constant to achieve Perfection
Cover image for Technical Mock interviews and coaching for big firms
Technical Mock interviews and coaching for big firms
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21
Cover image for Java Project and Bug Fixing
Java Project and Bug Fixing
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20
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Java Application Project
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23
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Kafka(3)
Aishwary Dhare's avatar
Aishwary Dhare
proBengaluru, India
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
5.0
Rating
7
Followers
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
Cover image for Global FinTech Development for MishiPay
Global FinTech Development for MishiPay
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9
Cover image for High-Performance Cryptocurrency Trading Engine Development
High-Performance Cryptocurrency Trading Engine Development
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11
Cover image for Migration of ETL Pipelines to Apache Airflow
Migration of ETL Pipelines to Apache Airflow
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16
Cover image for Development of PayLater & PayLater Business Apps
Development of PayLater & PayLater Business Apps
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18
Kafka(2)
Sreeram Melarkode's avatar
Sreeram Melarkode
Thane West, India
Wordsmith for hire: Writing, research, consulting
5.0
Rating
4
Followers
Wordsmith for hire: Writing, research, consulting
Cover image for Near Real Time Data WareHouse
Near Real Time Data WareHouse
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18
Cover image for Solution Consulting
Solution Consulting
1
16
Cover image for Mobile Device Policy
Mobile Device Policy
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24
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Kafka(1)
Muhammad Arsalan's avatar
Muhammad Arsalan
Karachi, Pakistan
Data Engineer | Web Scraping | SQL | BI Dashboards
Data Engineer | Web Scraping | SQL | BI Dashboards
Cover image for Problem: Batch data processing was not
Problem: Batch data processing was not suitable for real-time analytics and scalable cloud-based data ingestion. Solution: Created a real-time streaming pipeline using Kafka on AWS EC2, stored processed data in S3, cataloged it with AWS Glue, and queried it with Amazon Athena. Tools: Python, Apache Kafka, AWS EC2, Amazon S3, AWS Glue, Amazon Athena, Pandas Result: Built an end-to-end cloud data streaming workflow that supports real-time ingestion, storage, cataloging, and SQL-based analytics.
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88
Cover image for Problem: Financial data from stocks and
Problem: Financial data from stocks and crypto APIs was scattered, refreshed manually, and not ready for analytics or ML use. Solution: Built an Apache Airflow pipeline to collect, transform, validate, and load real-time financial data from multiple APIs into PostgreSQL, MongoDB, AWS RDS, and Qdrant. Tools: Apache Airflow, Python, PostgreSQL, MongoDB, AWS RDS, Qdrant, APIs Result: Automated sub-hourly data refresh, processed thousands of records daily, and delivered clean data for dashboards, analytics, and vector search.
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95
Cover image for Problem: Finance data was difficult to
Problem: Finance data was difficult to analyze across sales, profit, orders, discounts, countries, and customer segments. Solution: Created an interactive Power BI dashboard with KPI cards, sales trends, profit analysis, country performance, and segment-level insights. Tools: Power BI, Power Query, DAX, Excel, Data Modeling Result: Delivered a clear executive dashboard for tracking financial performance, identifying trends, and making faster business decisions.
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Cover image for Problem: Users needed a faster way
Problem: Users needed a faster way to find the most relevant counselor based on specialization and available data. Solution: Built a recommendation workflow using PySpark for data processing and Redis for fast lookup and recommendation serving. Tools: Python, PySpark, Redis, Docker, Docker Compose Result: Created a containerized recommendation system that processes counselor data and returns relevant matches efficiently.
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73
Kafka(1)
Jongeum Kim's avatar
Jongeum Kim
Toronto, Canada
Software Engineer | Backend & AI Systems
New to Contra
Software Engineer | Backend & AI Systems
Cover image for Smartport IoT Integrated Platform Management •
Smartport IoT Integrated Platform Management • Took ownership of a Smartport IoT integration platform originally developed through the IPLT national R&D project, maintaining and extending it for continued operational and research use. • Operated the platform as a real-time data hub, providing port equipment data to external companies and research partners through MQTT, Kafka, and HTTP-based interfaces. • Managed equipment integration • Operated the platform as a real-time data hub, providing port equipment data to external companies and research partners through MQTT, Kafka, and HTTP-based interfaces.and data availability by investigating terminal devices, communication methods, data formats, and operational status across connected port equipment. • Maintained and improved data pipelines, interface configurations, and platform components to support stable data delivery across multiple downstream systems. • Contributed to a commissioned operational analysis project by integrating Smartport equipment data with Terminal Operating System (TOS) data to analyze terminal workflows and equipment activities. • Supported ongoing collaboration with external vendors, terminal stakeholders, and R&D participants by validating data flows, resolving integration issues, and coordinating interface requirements.• Operated the platform as a real-time data hub, providing port equipment data to external companies and research partners through MQTT, Kafka, and HTTP-based interfaces.
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38
Cover image for AI-Powered Video-to-Recipe Backend Development
AI-Powered Video-to-Recipe Backend Development
0
0
Cover image for Development and Launch of VIV Platform
Development and Launch of VIV Platform
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0
Cover image for AI Vehicle Path Prediction Service for Smart Ports
AI Vehicle Path Prediction Service for Smart Ports
0
0
Kafka(1)
ANIMESH SINGH's avatar
ANIMESH SINGH
Delhi, India
Data & AI Engineer
Data & AI Engineer
Cover image for Problem: Many organizations still process invoices
Problem: Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals. Solution: This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.
3
3
154
Cover image for Problem Statement Urban traffic management systems
Problem Statement Urban traffic management systems lack real-time, integrated data combining traffic conditions with weather patterns. This results in poor routing decisions, delayed emergency responses, and inefficient traffic flow management. Solution Developed a comprehensive real-time ETL pipeline that integrates traffic APIs and weather data sources, processes millions of data points, and delivers actionable insights through interactive dashboards for traffic management and route optimization.
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49
Cover image for A fully local RAG pipeline
A fully local RAG pipeline that transforms your PDFs into a queryable knowledge base using FAISS vector search and Ollama LLMs. No cloud, no API keys - just private, grounded document intelligence running entirely on your machine.
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54
Cover image for A fully Dockerized real-time IoT
A fully Dockerized real-time IoT ETL pipeline that simulates device telemetry, processes events through MQTT and Kafka, orchestrates workflows with Airflow, and delivers real-time alerts and insights to CRM systems with monitoring via Grafana and Loki.
1
90
Kafka(2)
Zain Ul abideen's avatar
Zain Ul abideen
Lahore, Pakistan
I as a Fullstack engineer Help solve your technical problems
11
Followers
I as a Fullstack engineer Help solve your technical problems
Cover image for Kafka Server Deployment on Ubuntu WSL
Kafka Server Deployment on Ubuntu WSL
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32
Cover image for Foundations of Red Hat Cloud-native Development
Foundations of Red Hat Cloud-native Development
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40
Cover image for Databases development, practice proj
Databases development, practice proj
1
38
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Kafka(1)