You will get Apache Airflow ETL Data Pipeline — Proof of Concept (POC)
Top Rated
Top Rated
Project details
The Problem:
Organizations today deal with data arriving from multiple heterogeneous sources — databases, APIs, flat files, and streaming systems. Without a reliable, automated pipeline, data engineers spend significant manual effort orchestrating data movement, handling failures, and ensuring data quality.
Key challenges include:
• No centralized pipeline orchestration — tasks run manually or via ad-hoc scripts.
• No automatic retry or failure handling when a step breaks mid-way.
• No data quality validation before loading into the warehouse.
• No visibility or monitoring of pipeline health and task status.
Teams are notified of failures only after downstream impact is already felt.
Our Solution:
We built and deployed an automated ETL (Extract, Transform, Load) pipeline using Apache Airflow, containerized with Docker. The pipeline orchestrates end-to-end data movement across 5 tasks in a clearly defined DAG (Directed Acyclic Graph):
The solution runs on a @daily schedule, supports automatic retries on failure, and provides full visibility via the Airflow web UI.
Data flows: Source DB → Extract → XCom → Transform → XCom → Validate → XCom → Load → Warehouse → Notify
Organizations today deal with data arriving from multiple heterogeneous sources — databases, APIs, flat files, and streaming systems. Without a reliable, automated pipeline, data engineers spend significant manual effort orchestrating data movement, handling failures, and ensuring data quality.
Key challenges include:
• No centralized pipeline orchestration — tasks run manually or via ad-hoc scripts.
• No automatic retry or failure handling when a step breaks mid-way.
• No data quality validation before loading into the warehouse.
• No visibility or monitoring of pipeline health and task status.
Teams are notified of failures only after downstream impact is already felt.
Our Solution:
We built and deployed an automated ETL (Extract, Transform, Load) pipeline using Apache Airflow, containerized with Docker. The pipeline orchestrates end-to-end data movement across 5 tasks in a clearly defined DAG (Directed Acyclic Graph):
The solution runs on a @daily schedule, supports automatic retries on failure, and provides full visibility via the Airflow web UI.
Data flows: Source DB → Extract → XCom → Transform → XCom → Validate → XCom → Load → Warehouse → Notify
Machine Learning Tools
Amazon SageMaker, Apache Spark, Azure Machine Learning, BERT, Chainer, Databricks MLflow, GitHub Copilot, Google Data Studio, Keras, KNIME, Kubeflow, Microsoft Power BI, MLflow, NumPy, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, RapidMiner, SPSS, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$100
|
Standard
$700
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 3 days | 10 days | 25 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 2 | 5 | 10 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
DF
Danisavage F.
Aug 14, 2025
Spam detection and email analytics dashboard
About Indumati
Python, Power BI, DAX, Make, n8n, Kibana, Grafana, Neo4j, ETL/ELK, SQL
100%
Job Success
Jamui, India - 1:23 am local time
𝗜 𝗯𝘂𝗶𝗹𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘁𝗲𝗮𝗺𝘀 𝗿𝗲𝗹𝘆 𝗼𝗻.
With 10+ years of experience across BI, data modeling, and data engineering, I help companies transform fragmented data into a clean, reliable source of truth — and turn it into executive-ready dashboards that drive real decisions.
I am a Data Scientist and Data Engineering professional specializing in Python, data analytics, visualization, ETL pipelines, and business intelligence. Experienced in transforming complex datasets into actionable insights using Power BI, D3.js, Kibana, and Grafana. Build scalable ETL/ELT workflows, interactive dashboards, log analytics solutions, and graph-based data models using Neo4j. Skilled in integrating structured and unstructured data, data cleansing, transformation, statistical analysis, and developing analytics solutions for operational and business decision-making.
Skills:
✔ Data Science & Data Analytics
✔ Python Development
✔ Machine Learning
✔ Data Mining & Statistical Analysis
✔ ETL / ELT Development
✔ Data Engineering & Data Pipelines
✔ Power BI Dashboard Development
✔ D3.js Data Visualization
✔ Kibana & Elasticsearch Analytics
✔ Grafana Dashboards & Monitoring
✔ Neo4j & Graph Database
✔ ELK Stack
✔ Data Cleaning & Transformation
✔ SQL & Database Management
✔ Business Intelligence
✔ KPI & Performance Analytics
✔ Data Integration & Automation
✔ Dashboard & Reporting Solutions
Tech Stack
Languages: Python, SQL, JavaScript
▸ BI & Visualization: Power BI, D3.js, Kibana, Grafana
▸ Data Engineering: ETL, ELT, Data Pipelines, Data Integration
▸ Search & Analytics: Elasticsearch, Logstash, Kibana (ELK Stack)
▸ Databases: Neo4j, PostgreSQL, MySQL, SQL Server
▸ Data Science: Pandas, NumPy, Scikit-learn, Statistical Analysis
▸ Development: REST APIs, JSON, Jupyter Notebook
▸ Monitoring: Grafana, Elasticsearch, Kibana
▸ Cloud/Data Platforms: Azure, AWS, BigQuery
━━━━━━━━━━━━━━━━━━━━━━━
💬 𝗟𝗘𝗧’𝗦 𝗕𝗨𝗜𝗟𝗗
━━━━━━━━━━━━━━━━━━━━━━━
👉 𝗦𝗲𝗻𝗱 𝗺𝗲 𝗮 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 — 𝗜’𝗹𝗹 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆.
Steps for completing your project
After purchasing the project, send requirements so Indumati can start the project.
Delivery time starts when Indumati receives requirements from you.
Indumati works on your project following the steps below.
Revisions may occur after the delivery date.
Solution Architecture
The solution is built on a microservices architecture using Docker Compose with- Airflow Webserver Airflow Scheduler PostgreSQL DAG ETL Helpers Data flows: SourceDB → Extract → XCom → Transform → XCom → Validate → XCom → Load → Warehouse → Notify