data-engineering

You can orchestrate data pipelines with quality checks by applying standardized guidance for Airflow, Dagster, and Prefect, enabling reliable data delivery through unified scheduling, data quality gates, and lineage tracking.

The best way to add data quality gates to streaming pipelines is by integrating validation tools like Great Expectations, dbt, and Soda Core, ensuring results are validated before landing in a warehouse.

Yes, this approach supports both batch and streaming workloads, providing framework-agnostic orchestration guidance to build and maintain reliable pipelines across modern tooling.

You design a batch job with validation by creating an initial pipeline skeleton, selecting an orchestration framework, and wiring in the batch job with data quality checks to enforce validation.

Yes, this guidance includes best practices for testing and observability in data engineering, ensuring your pipelines are maintainable, observable, and reliable across the orchestrated workflows.

You track data lineage in pipelines built with Airflow or Dagster by applying standardized orchestration guidance that supports lineage tracking alongside data quality enforcement and observability.