Pipeline Design

Designing a data pipeline involves selecting batch or streaming processing patterns based on data volume, velocity, and freshness SLAs to reliably move and transform data from source systems to consumption layers.

Choose ETL vs ELT patterns based on your infrastructure and transformation needs, balancing where data processing occurs to optimize load strategies and ensure data integrity within your pipeline architecture.

Ensure idempotency and data quality in ETL pipelines by implementing re-runnable processes and integrating data quality checkpoints that validate data integrity during movement and transformation across pipeline runs.

Select the best orchestration tool by evaluating dbt, Airflow, Dagster, or Prefect against your existing infrastructure, team expertise, and specific requirements for data lineage tracking and pipeline observability.

Yes, you can track data lineage and observability across batch processing workflows by planning lineage tracking mechanisms and monitoring pipeline health to maintain visibility into data flow and transformations.

Meet data freshness SLAs for ingesting user activity logs by defining appropriate batch or streaming processing patterns, ensuring the pipeline architecture supports the required data velocity and delivery timelines.