S1 | Apache Airflow 2025 Features, Core components- Webserver, Scheduler, Meta DB, Executor, Worker

Описание: Learn Apache Airflow core components with in this complete overview for data engineering and big data. From understanding the purpose of Apache Airflow and its features, orchestration, core components like Scheduler, Webserver, Executors, Workers, and the Meta Database, this video answers the most common questions learners and professionals ask. #apacheairflow #dataengineering #bigdatatutorial

Video in regards to Airflow will now be updated weekly for series continuation.
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0:00 – What is the purpose of Apache Airflow in data engineering?
0:50 – How to perform ETL and data storage using Apache Airflow?
2:25 – How does Apache Airflow orchestrate data pipelines?
3:53 – What Apache Airflow is not designed for in data workflows?
8:56 – What are the key features and use cases of Apache Airflow (organization, visibility, flexibility, scaling)?
14:26 – What are Apache Airflow core components (Webserver, Scheduler, Meta Database, Executor, Worker) and how do they interact?
17:22 – How does the Apache Airflow Scheduler component work step by step?
21:00 – What is the function of the Meta Database in Apache Airflow?
24:34 – How does the Apache Airflow Executor work in managing tasks?
28:48 – What are the different types of Executors in Apache Airflow and their configurations?
30:10 – How do Apache Airflow Worker processes communicate and interact (Celery, Kubernetes)?
35:17 – What is the summary of Apache Airflow core components for beginners?

What does the Data Engineering ecosystem consists of..? -- Airflow, Azure, DataBricks, AWS EMR, Glue, Lambda, Kinesis, Spark-Streaming, Python Programming, Apache Hadoop, Apache Hive, PySpark, SparkSQL, Kafka, Github, etc..

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