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Here’s how to spend the next 90 days learning Data Engineering: DataOps Track - 30 days → DataOps Basics • Version control, branching • CI/CD for data • Pipeline testing (unit + integration) • Docker fundamentals - 30 days → Monitoring & Reliability • Logging, alerts,…
> Why Apache Airflow Is Still the Orchestration Standard (Even in a World of Managed Pipelines) - Everyone's building on Prefect, Dagster, and cloud-native schedulers, but Airflow quietly orchestrates them all. - In 2025, Airflow isn't a legacy infrastructure - it's the control…
> Why Apache Airflow Is Still the Orchestration Standard (Even in a World of Managed Pipelines) - Everyone's building on Prefect, Dagster, and cloud-native schedulers, but Airflow quietly orchestrates them all. - In 2025, Airflow isn't a legacy infrastructure - it's the control…
All you need to become a data engineer: → learn python (pandas, pyspark, etc.) → learn sql (queries, joins, window functions, optimization) → learn data modeling & schema design (star / snowflake) → learn etl / elt concepts → learn data pipelines (airflow / prefect /…
> Why Apache Spark Is Still the Backbone of Modern Data Engineering (Even in the Age of Lakehouses) - Everyone’s talking about Databricks, Snowflake, and AI, but Spark quietly powers them all. - In 2025, Spark isn’t old tech, it’s the invisible engine behind most large-scale…
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