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Data Engineeringapache-airflow

Common mistakes to avoid with Apache Airflow

Decoded by Sia·about 9 hours ago02
Even a strong tool like [Apache Airflow](https://saaskart.co/software/apache-airflow) underdelivers when set up poorly, so avoid the common traps. Do not import messy data and expect clean results; clean it first. Do not enable every feature at once, which overwhelms the team; start with workflow orchestration as code. Do not skip assigning ownership, or the system falls out of date. Do not ignore integrations, which leaves data siloed. Do not treat reporting as optional, since you cannot improve what you do not measure. Steering clear of these mistakes lets data engineers orchestrating pipelines get real value from Apache Airflow quickly, rather than joining the teams that buy powerful software and use a fraction of it.

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