Freelancers using Apache Airflow in Glyfada
Freelancers using Apache Airflow in Glyfada
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El Mehdi El Wafi
pro
Morocco
Linux VPS SysAdmin AWS & Oracle Cloud Architect | Terraform
$10k+
Earned
1x
Hired
11
Followers
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Linux VPS SysAdmin AWS & Oracle Cloud Architect | Terraform
1
92% Cost OFF of EC2 Airflow/DBT Workers using Lambda
1
16
$10K+ earned
3
DevOps/Cloud & DataOps maintainer
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34
3
🛠️ DevOps Tips 🛠️ If your GitHub workflows or jobs are failing due to jobs concurrently accessing and/or modifying shared resources, You can control execution at the workflow or job levels using the concurrency. Jobs/Workflows in the same are prohibited from running at the same time, and you can cancel any jobs/workflow if you trigger another one. Groups let you define compartments in which you don't want concurrent workflows running. They can be organized however you like. I find that using them in separate environments is a great practice. Cancel-in-progress: specifies if the old running job/workflow needs to shut down. Read more in docs: https://docs.github.com/en/actions/how-tos/write-workflows/choose-when-workflows-run/control-workflow-concurrency
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3
581
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Enterprise VM Template for Proxmox VPS
0
10
Apache Airflow
(2)
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ADEWUNMI OLUWASEYI
Lagos, Nigeria
Expert in building an efficient and scalable data pipeline
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Expert in building an efficient and scalable data pipeline
0
Scalable E-commerce Big Data Platform for BuildItAll
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5
0
BeejanTech Analytics Platform Development
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4
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Offensive Card Booking Tracker for Football Club
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4
0
AWS Infrastructure Provisioning with Terraform
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4
Apache Airflow
(3)
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Abhishek Jha
United Arab Emirates
Analytics Engineer | Snowflake, Airflow, dbt
New to Contra
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Analytics Engineer | Snowflake, Airflow, dbt
0
Reverse ETL: Warehouse Data Back to Sales Tools
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6
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Data Quality Framework: 62% to 99% Accuracy
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4
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Real-Time Analytics: 48hr Latency to 90 Seconds
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9
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I Build Production-Grade Data Pipelines (ETL, Medallion, Snowflake, dbt) Messy data → clean, trusted analytics in ~2 weeks.
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61
Apache Airflow
(8)
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Aishwary Dhare
pro
Bengaluru, India
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
5.0
Rating
7
Followers
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Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
0
Migration of ETL Pipelines to Apache Airflow
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16
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Global FinTech Development for MishiPay
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8
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Development of PayLater & PayLater Business Apps
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18
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Development and Launch of MishiPay Self-checkout Kiosks
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10
Apache Airflow
(1)
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John Dzilvelis
pro
Colorado, USA
Database Architect & Engineer
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Database Architect & Engineer
0
Data Lake Development Project
0
7
0
MySQL Database Repair and Recovery
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6
0
Supabase Test Project Refresh
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58
0
ETL Pipeline - AWS Aurora to Snowflake
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75
Apache Airflow
(1)
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Muhammad Arsalan
Karachi, Pakistan
Data Engineer | Web Scraping | SQL | BI Dashboards
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Data Engineer | Web Scraping | SQL | BI Dashboards
0
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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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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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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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
Apache Airflow
(1)
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Umar Shaikh
Pune, India
Data Scientist, Backend Engineer, Data Analyst
10
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Data Scientist, Backend Engineer, Data Analyst
2
Proud to share that I’ve earned the Oracle Cloud Infrastructure (OCI) 2025 Certified Data Science Professional certification (https://tinyurl.com/dsmlaidl) — issued by Oracle. This certification validates my expertise in Machine Learning (ML) | Data Science | Artificial Intelligence (AI) | Cloud Computing, showcasing my ability to design, build, deploy, and manage end-to-end ML solutions using OCI Data Science and Oracle AI Services. 💡 Core Expertise: 🔹 Scalable ML pipelines on OCI Data Science 🔹 AI, MLOps, and Cloud best practices for model optimization 🔹 Leveraging OCI Data Flow, Data Catalog, and AI Services for automation 🔹 Skilled in Python | TensorFlow | Scikit-learn | OCI SDKs for production-grade workflows 💪 Passionate about applying ML | Data Science | AI | Cloud Innovation to drive real business transformation.
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main_palmer_penguin_EDA_2024-project
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16
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main_palmer_penguin_EDA_2024-project
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11
1
Fraud_Transaction_Detection-Fraud_Finder_ID12254…
1
17
Apache Airflow
(1)
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ANIMESH SINGH
Delhi, India
Data & AI Engineer
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Data & AI Engineer
3
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.
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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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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.
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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
Apache Airflow
(3)
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