discover-data

ETL automation orchestrates data workflows by scheduling, monitoring, and managing batch and stream processing tasks. This skill provides guidance on designing end-to-end pipelines, choosing between batch and stream processing, implementing data validation, and using orchestration frameworks like Airflow to handle timely dataflow and ensure reliable execution.

Data validation in pipelines catches errors before downstream processing by checking schema, completeness, and quality at ingestion and transformation stages. This skill covers validation techniques integrated into ETL workflows to ensure data integrity across batch and stream processing systems.

Airflow orchestrates both batch and stream processing by scheduling DAGs that trigger jobs, manage dependencies, and coordinate workflows. This skill explains how to design hybrid pipelines that combine batch processing for historical data and stream processing for real-time analytics within a unified orchestration framework.

Real-time analytics pipelines use stream processing to ingest, validate, and transform data continuously, then route results to analytics systems. This skill guides you through architecture decisions, stream processing patterns, data validation at scale, and orchestration strategies for timely insights.

Batch processing handles large data volumes on a schedule with high latency; stream processing handles continuous data with low latency. This skill explains when each approach fits your use case, how to implement both in a single pipeline, and how orchestration tools coordinate them.