The Unizin Data Platform (UDP) is a data platform product that integrates, aggregates, cleans, models, and stores all teaching and learning data into a data lake.
It generates a unified portrait of learners in the context of their learning environments and provides a layer of data services to drive analytics, data science, and research, enabling institutions to build effective data-driven practices at scale.
The Unizin Data Platform (UDP) is a cloud-native, single-tenant architecture solution that integrates and warehouses data from the Student Information System (SIS), Learning Management System (LMS), and LMS-integrated tools.
Understand the fundamentals of how the UDP aggregates and normalizes learning data to serve a learning analytics ecosystem. The section describes the key features of the UDP and the ideas that inform its technical design. If you are new to the UDP, we strongly recommend that you begin with the Key concepts section.
The UDP supports two data standards: (1) IMS Global Caliper, (2) Unizin Common Data Model (UCDM). The two data standards are complementary and enable the UDP to consolidate all teaching and learning data together.
Any individual responsible for configuring or creating SIS, LMS, or Learning tool integrations to the UDP will find this section essential.
The Unizin Data Platform (UDP) is primarily composed of two data pipelines. Each data pipeline creates and maintains the data lakes and data marts that undergird the UDP's data services. There exists one data pipeline for each learning data category integrated by the Unizin Data Platform (context data and behavior data).