data-engineering-data-pipeline

Designing scalable data pipelines for batch and streaming workloads requires selecting appropriate architecture patterns like Lambda or Kappa, implementing robust ingestion mechanisms, and configuring workflow orchestration with tools like Airflow or Prefect to ensure reliable data flow.

Building data ingestion and orchestration for cloud data architecture involves defining batch and streaming ingestion patterns, utilizing orchestration frameworks like Airflow or Prefect, and transforming data using dbt and Spark to ensure scalable and reliable pipeline operations.

Managing ACID transactions in a data lakehouse storage architecture is achieved by implementing storage formats like Delta Lake or Iceberg, which provide reliable data management and support transactional integrity alongside your standard pipeline workflows.

Yes, you can use dbt tests and Great Expectations to implement comprehensive data quality frameworks within your ETL pipeline, ensuring that data accuracy and integrity are maintained throughout the transformation and ingestion processes.

Optimizing data pipeline costs across cloud environments involves applying data partitioning strategies, configuring storage lifecycle policies, and implementing compute optimization techniques to reduce overall operational expenses while maintaining processing efficiency.

Choosing a Kappa architecture over a Lambda architecture for streaming data processing depends on your need for a unified streaming model, whereas Lambda separates batch and streaming layers, requiring you to manage two distinct processing code paths for the same data.