Project Title: ETL Pipeline Development

Objective:

To design and implement a robust ETL (Extract, Transform, Load) pipeline that automates the process of collecting raw data, transforming it into a usable format, and loading it into a data store for analysis or modeling.

Key Components:

Extract:

Collect raw data from various sources such as:

Databases (SQL, NoSQL)

APIs

Files (CSV, Excel, JSON)

Web scraping or external services

Ensure connection handling, retries, and logging for fault tolerance.

Transform:

Clean and standardize data (handle missing values, data types, formatting).

Perform business logic transformations or feature engineering.

Use libraries like pandas, NumPy, or PySpark for scalable data processing.

Maintain data lineage and track changes for auditing.

Load:

Write processed data to target storage:

Data warehouses (BigQuery, Redshift, Snowflake)

Databases (PostgreSQL, MySQL)

Cloud storage (S3, Azure Blob)

Support batch or incremental loading strategies.

Automation & Orchestration:

Schedule pipeline runs using tools like Apache Airflow, Luigi, or Prefect.

Monitor pipeline health, performance, and logging.

Testing & Validation:

Include unit tests for transformation logic.

Validate schema and data quality before loading.

Log pipeline metrics and failure alerts.

Scalability & Reusability:

Modular design with reusable components.

Configurable settings for different environments or use cases.

Support version control and CI/CD integration.

Outcome:

A production-ready ETL pipeline that ensures efficient, reliable, and scalable data flow from raw sources to final storage, enabling downstream analytics and machine learning.