Spotify Cloud Project

Spotify Stream Analytics 🎥

Built a synthetic data pipeline for real-time music insights, stunning dashboards, and actionable decisions.

🌟 Project Overview:

Addresses limited Spotify stream data access with a synthetic pipeline. Realistic events stream to Kafka, processed by Spark, stored in Deltalake. Airflow ensures a seamless pipeline, and dbt transforms data into captivating dashboards.

📌 Key Features:

Streamlined Infrastructure: Scripts for rapid deployment, minimizing complexity.
Real-Time Data Simulation: Synthetic events flow through Kafka, mimicking user behavior.
High-Performance Processing: Spark Streaming efficiently handles real-time event processing.
Secure Cloud Data Warehouse: Snowflake acts as a secure data warehouse.
Change Data Capture Integration: Databricks captures updates from Delta Lake into Snowflake.
Incremental Data Transformation: dbt uses Snowflake Streams for automatic updates.
Orchestrated Pipelines: Airflow orchestrates tasks on an hourly basis.
Interactive Insights: Metabase connects to Snowflake for visually compelling dashboards.

🌟 Challenges & Solutions:

1. Challenge: Snowflake stream data disappears after consumption for CDC.
Solution: Transiently consumed data, using an intermediary table for incremental loading.
2. Challenge: Slow Airflow performance with default Docker Compose file.
Solution: Optimized performance with a custom Docker Compose with a local executor.
3. Challenge: Orchestration of dbt without overloading Airflow DAGs directory.
Solution: Dockerized the dbt project, orchestrating runs using Airflow DockerOperator.
4. Challenge: ADLS-to-Snowflake staging and real-time freshness on minimal resources.
Solution: Enabled data freshness using Databricks through Airflow.

🎯 Goals Achieved:

Instant Music Insights: Real-time trends and user preferences analysis through Spark Streaming and Delta Lake processing.
Streamlined Deployment: Simplified scripts for rapid resource deployment.
Robust Data Security: Data integrity ensured with Snowflake’s secure warehouse.
Automated Workflows: Orchestrated tasks with Airflow, Databricks, and dbt for reliable hourly pipelines.

👉 Conclusion:

In summary, this project provides real-time music insights, addressing data accessibility challenges with a streamlined and secure solution.

📚 Explore the complete project at [https://lnkd.in/dWHxZB3d].