data-engineering-data-pipeline

To design a streaming data pipeline for real-time clickstream sessionization, you implement a Kappa or Lambda architecture that ingests events, processes session windows using Spark, and stores the output in a Lakehouse using Delta Lake or Iceberg for immediate downstream analytics.

The difference between ETL and ELT architecture patterns is the transformation stage. ETL transforms data before loading it into the target storage, while ELT loads raw data directly into a Lakehouse or warehouse first, then uses tools like dbt to transform it within the storage layer using native compute power.

To orchestrate a batch processing pipeline with Airflow and dbt, you configure Airflow to schedule and trigger dbt transformation jobs, managing the workflow dependencies from initial data ingestion through to the final creation of analytics-ready tables in your Lakehouse environment.

You need a Lakehouse architecture instead of a traditional data pipeline when you require both the scalable, cost-effective storage of a data lake and the reliable transactional guarantees, schema enforcement, and time travel features of a data warehouse using table formats like Iceberg or Delta Lake.

Yes, you can use Prefect for orchestrating both batch and streaming data pipelines, allowing you to coordinate data ingestion, manage dbt or Spark transformation jobs, and monitor pipeline health and data quality frameworks across different processing modes.

The best way to ensure data quality in an automated data pipeline is to implement a dedicated data quality framework within your transformation layers, continuously checking for accuracy and completeness during ingestion and orchestration to prevent bad data from reaching your Lakehouse storage.