Paper Number
ICIS2025-2232
Paper Type
PDW
Abstract
As data tools evolve, Information Systems curricula must adapt. While SQL remains foundational, tools like Pandas, Tableau, and PowerBI are used to teach principles of analytics and visualization. This workshop introduces Malloy, a new open-source data language developed by engineers at Google and Meta. Malloy combines the readability of a semantic data model with the power of SQL, enabling reusable joins and calculations while simplifying tasks such as date functions, percent-of-total, and level-of-detail queries. Unlike Tableau or PowerBI, Malloy is code-based, keeping learners closer to the data and reducing reliance on complex interfaces. Compared to Pandas, it provides accessible, database-native analytics without requiring extensive programming expertise. Participants will explore Malloy hands-on using finance, higher education, and sports datasets. Activities include small-group coding, discussion, and Q&A. Attendees will leave with practical teaching materials, datasets, and links to open resources. Basic SQL familiarity is helpful; laptops with internet and GitHub access required.
Recommended Citation
Olsen, Timothy, "Teaching Data Analytics with Malloy: An Open-Source Alternative to Pandas, Tableau, and PowerBI" (2025). ICIS 2025 Proceedings. 5.
https://aisel.aisnet.org/icis2025/pdw/pdw/5
Teaching Data Analytics with Malloy: An Open-Source Alternative to Pandas, Tableau, and PowerBI
As data tools evolve, Information Systems curricula must adapt. While SQL remains foundational, tools like Pandas, Tableau, and PowerBI are used to teach principles of analytics and visualization. This workshop introduces Malloy, a new open-source data language developed by engineers at Google and Meta. Malloy combines the readability of a semantic data model with the power of SQL, enabling reusable joins and calculations while simplifying tasks such as date functions, percent-of-total, and level-of-detail queries. Unlike Tableau or PowerBI, Malloy is code-based, keeping learners closer to the data and reducing reliance on complex interfaces. Compared to Pandas, it provides accessible, database-native analytics without requiring extensive programming expertise. Participants will explore Malloy hands-on using finance, higher education, and sports datasets. Activities include small-group coding, discussion, and Q&A. Attendees will leave with practical teaching materials, datasets, and links to open resources. Basic SQL familiarity is helpful; laptops with internet and GitHub access required.
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Comments
27-PDW