Apache Airflow
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Apache Airflow: the workflow automation darling of the data world, born at Airbnb in 2014. It’s open-source, meaning free, but don’t let that fool you—it’s no walk in the park. Competing with the likes of Luigi, Prefect, and cloud-native solutions, Airflow's main draw is its DAG-based approach (Directed Acyclic Graphs), which gives you full control over complex pipelines. In other words, it’s a power tool for data engineers who love Python and have a thing for manually tweaking dependencies.
In its current state, Airflow dominates orchestration, especially in companies with mature data engineering teams. But beware: Airflow is highly flexible, which also means highly finicky. Miss one task or get the scheduling wrong, and your whole pipeline could come crashing down at 3 a.m. The UI is functional but looks like it escaped from 2015, and scalability? Well, you'll be tuning it yourself. It’s powerful but requires constant attention—like a temperamental race car.
Used together with Airflow
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Jobs in using Apache Airflow for but please no
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data engineer
Senior Data Engineer @ bain-and-company
JP | 2025-12-26
Senior Data Engineer in Bain’s AIS Data Engineering within the AI, Insights & Solutions team. You’ll design, build and deploy data solutions for production-scale client ecosystems, and own the...
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AI/ML, Analytics, Management, Data Engineering, Data Science, Cloud Computing, Python, Scala, C, C++, Java, Go, Airflow, Beam, SQL, NoSQL, Databricks, PostgreSQL, SQL Server, Oracle, MySQL, Redis, MongoDB, ELK, Hive, Apache HBase, Teradata, Cassandra, Redshift, AWS, Azure, GCP, Kubernetes, Terraform, DevOps, CI/CD, GitHub, Computer Science, Agile/Scrum |
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