data-engineering-data-pipeline

Designing a scalable batch and streaming data pipeline involves selecting an architecture pattern like ETL/ELT, Lambda, Kappa, or Lakehouse. You configure ingestion, orchestration with Airflow, transformation with dbt, and Delta Lake storage to handle workloads reliably.

Yes, you can use Airflow and dbt together for ETL workflow orchestration and transformation. Airflow manages the scheduling and workflow dependencies, while dbt handles the SQL-based data transformations, ensuring robust pipeline execution and data modeling.

The best way to manage data quality in a streaming data pipeline is implementing automated testing frameworks like Great Expectations or dbt tests. These validate data integrity during ingestion and transformation, ensuring errors are caught early.

Yes, Delta Lake supports ACID transactions for both batch and streaming workloads. It provides reliable storage management on cloud data platforms, enabling concurrent reads and writes while maintaining data consistency across your pipeline architecture.

Monitor data pipeline performance using tools like CloudWatch, Prometheus, or Grafana. Optimize compute costs by implementing data partitioning, applying storage lifecycle policies, and fine-tuning compute resources to match your batch and streaming workload requirements.