Part 7/12:
This layered approach balances data freshness with manageability, enabling analysts and data scientists easy access to relevant information.
Tailored Frameworks for Analysts and Data Science Teams
Recognizing that not all team members are proficient in Spark or complex SQL, Zepto developed a low-code/no-code ETL framework built on top of Spark and Airflow. This internal solution allows analysts to simply write SQL queries, which are then scheduled and managed automatically, simplifying data transformations and reducing engineering overhead.
Additionally, strict governance measures, including runtime constraints (e.g., limiting data retention to a few months), help control costs and optimize performance.