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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Cloud Composer (Airflow) on GCP Explained
Cloud Composer is Google Cloud's managed Apache Airflow, now being renamed Managed Service for Apache Airflow. This guide explains what an environment contains, how the three generations differ, shows a DAG built from Google operators, works out what an always-on environment costs and when a simpler scheduler will do.
Airflow Tutorial: Orchestrating Data Pipelines
A practical introduction to Apache Airflow: what orchestration adds to a pipeline, the scheduler, DAG processor and metadata database, a complete three-task DAG written with the TaskFlow API and run on real taxi data with dag.test, how XComs, retries and logical dates work, an idempotent rerun, and design habits.
Orchestration: Airflow vs Dagster vs Prefect
Airflow, Dagster and Prefect compared by writing the same real pipeline in each: Airflow's DAG of tasks, Prefect's flows that run like ordinary Python, and Dagster's assets that describe the data to be produced, with the Prefect and Dagster versions run on real taxi data, a comparison table and how to choose.
Workflow Orchestration with Apache Airflow DAGs
Once you can write an Airflow DAG, the next problems are about design: files that slow the scheduler, branches that silently skip the final step, and dozens of near-identical pipelines. This guide runs four DAG design patterns on Airflow 3.3.2 and shows what each one fixes, with real output.
Pipeline Orchestration: Airflow vs Prefect vs Dagster
Machine learning pipelines ask more of an orchestrator than nightly ETL: skipping expensive steps that have not changed, training many segment models with retries, and knowing which model came from which data. This guide runs those needs in Prefect and Dagster on real data, and maps them to Airflow.
Airflow for ML Pipelines: A Practical Guide
Airflow can schedule more than data loads: it can retrain a model, score each day, check the error and raise an alert. This guide builds a training DAG and a scoring DAG on real bike rental data, and shows them handling a normal day, a bad day, and the day Hurricane Sandy cut the data off.