The Data Engineering Part 2: Building Your First Production Data Pipeline

1. 1Build a data pipeline using Kafka for data ingestion
2. 2Configure Spark for data processing and transformation
3. 3Apply dbt for data modeling and transformation
4. 4Schedule workflows using Airflow for automated pipeline execution
5. 5Test and monitor the pipeline for real-time data processing and dashboarding

Data engineers and data scientists can benefit from this walkthrough to design and implement scalable data pipelines for their organizations

💡 A modern data pipeline architecture should include tools for data ingestion, processing, transformation, and workflow management

🚀 Build your first production data pipeline with Kafka, Spark, dbt, and Airflow! 📊