Flask-Based ETL Microservice for Scalable Data ProcessingAnna Isoyan
I provide a Flask-based microservice solution for handling complex ETL (Extract, Transform, Load) processes. With expertise in Python, I design scalable data pipelines to automate data extraction from various sources, perform transformations, and load the processed data into databases or cloud storage.
What's included
Flask Microservice
A fully implemented Flask-based ETL microservice exposing RESTful endpoints for initiating, monitoring, and completing ETL tasks.
Docker containerization of the microservice for easy deployment.
ETL Pipeline Scripts
Python scripts for data extraction, transformation, and loading operations.
Configurable pipelines for different data sources and destinations.
Deployment & Integration
Flask application deployed to cloud or containerized environments (e.g., AWS, Google Cloud, Docker, Kubernetes).
Configurations for database connections, API integration, and cloud storage setup.
Documentation
Clear and detailed API documentation for integrating with the ETL microservice, including authentication, data processing steps, and response formats
Monitoring and Logging Setup
Integration with logging tools (e.g., Loggly, Datadog) to track job statuses, errors, and system health.
Setup for real-time monitoring of ETL jobs can be discussed.
Contact for pricing
Tags
Flask
Kafka
pandas
PySpark
Data Analyst
Data Modelling Analyst
Service provided by
Anna Isoyan Yerevan, Armenia
Flask-Based ETL Microservice for Scalable Data ProcessingAnna Isoyan
Contact for pricing
Tags
Flask
Kafka
pandas
PySpark
Data Analyst
Data Modelling Analyst
I provide a Flask-based microservice solution for handling complex ETL (Extract, Transform, Load) processes. With expertise in Python, I design scalable data pipelines to automate data extraction from various sources, perform transformations, and load the processed data into databases or cloud storage.
What's included
Flask Microservice
A fully implemented Flask-based ETL microservice exposing RESTful endpoints for initiating, monitoring, and completing ETL tasks.
Docker containerization of the microservice for easy deployment.
ETL Pipeline Scripts
Python scripts for data extraction, transformation, and loading operations.
Configurable pipelines for different data sources and destinations.
Deployment & Integration
Flask application deployed to cloud or containerized environments (e.g., AWS, Google Cloud, Docker, Kubernetes).
Configurations for database connections, API integration, and cloud storage setup.
Documentation
Clear and detailed API documentation for integrating with the ETL microservice, including authentication, data processing steps, and response formats
Monitoring and Logging Setup
Integration with logging tools (e.g., Loggly, Datadog) to track job statuses, errors, and system health.
Setup for real-time monitoring of ETL jobs can be discussed.
Contact for pricing