DeFtunes Data Pipeline
DeFtunes Data Pipeline
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A comprehensive data pipeline for a fictional music streaming service, built with AWS services, Apache Airflow, and modern data engineering practices.
Technology Stacks
Technology Stacks
☁️ Cloud Infrastructure
☁️ Cloud Infrastructure
AWS S3
Object storage for the data lakeAWS Glue
Serverless ETL serviceAWS Lambda
Serverless compute for data ingestionAWS Redshift
Data warehouse for analytics
⚙️ Data Processing
⚙️ Data Processing
Apache Spark
Distributed data processingApache Iceberg
Table format for data lakesAWS Glue Data Quality
Data validation servicedbt
Data transformation tool
♾️ Orchestration
♾️ Orchestration
Apache Airflow
Workflow orchestration platformAWS Step Functions
Serverless workflow serviceAWS Glue Workflow
Managed ETL workflows
🌐 Infrastructure as Code
🌐 Infrastructure as Code
Terraform
Infrastructure provisioningAWS CloudFormation
AWS resource templatingDocker
Container platform for services
💬 Languages & Frameworks
💬 Languages & Frameworks
Python
Primary programming languageSQL
Data query languagePySpark
Python API for Apache Spark
💻 Version Control & CI/CD
💻 Version Control & CI/CD
Git
Version control systemGitHub Actions
CI/CD automation
📊 Monitoring & Visualization
📊 Monitoring & Visualization
Apache Superset
Data visualization platformAWS CloudWatch
Monitoring and observabilityGrafana
Metrics visualization
Pipeline Architecture
Pipeline Architecture
Extract Layer
AWS Glue jobs extract data from API endpoints and RDS databases, storing raw data in S3 landing zone with date-based partitioning.
Transform Layer
Glue ETL jobs transform data, adding metadata and proper formatting, storing the result in Apache Iceberg tables.
Data Quality
AWS Glue Data Quality validates data against rule sets that check for completeness, uniqueness, and data type compliance.
Serving Layer
Amazon Redshift hosts the dimensional model (star schema) created with dbt, serving as a data warehouse for analytics.
Pipeline Workflow
Pipeline Workflow
Apache Airflow orchestrates the pipeline through DAGs that define the entire data flow from extraction to analytics.
Songs Pipeline DAG ⬇️
API Pipeline DAG ⬇️
Data Models
Data Models
The project uses dbt to implement a star schema model and analytical views for business intelligence.
Star Schema Model
The dimensional model consists of fact tables (sessions, purchases) and dimension tables (users, artists, songs, time) to support analytical queries.
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