data-engineering-data-pipeline

A scalable data pipeline requires choosing the right architecture pattern, such as Lambda, Kappa, or Lakehouse, to handle both batch and streaming ingestion while ensuring reliable transformation and storage across modern data platforms.

Building ETL and ELT architectures involves using Spark for large-scale data processing and dbt for data transformations, orchestrated through frameworks like Airflow to automate workflows and ensure reliable data loading into your storage layer.

Streaming data ingestion works by continuously capturing real-time event data and processing it through pipeline architectures like Kappa, ensuring low-latency ingestion, transformation, and storage while maintaining data quality and monitoring.

Yes, you can use Airflow to orchestrate both batch and streaming data pipelines by defining directed acyclic graphs that schedule and monitor ingestion, transformation with dbt or Spark, and storage tasks across your data architecture.

Optimizing data pipeline costs involves architecting efficient ingestion and storage layers, while ensuring data quality and governance requires implementing continuous monitoring and validation checks throughout the ETL and ELT transformation stages.

You should choose a Lakehouse architecture when you need to unify batch and streaming data processing on a single platform, combining the reliable storage of a data lake with the structured querying and governance of a data warehouse.