Education data expertise. Delivered on Databricks.

Student data requires precise controls over what people can access, how sensitive information is handled, and how every use of that data can be traced. We design Unity Catalog structures, access policies, PII classification and masking, row- and column-level security, lineage, and audit trails. The governance model is mapped to FERPA, GDPR, and applicable state privacy requirements.

Education data pipelines have to cope with changing rosters, vendor schemas, identity records, assessment formats, and reporting cycles. We manage ingestion from SIS, LMS, assessment, and identity systems using Lakeflow pipelines. Ed-Fi, OneRoster, and IMS CASE alignment is built into the transformations, reducing the manual work needed to make data usable across systems.

Natural-language analytics is useful only when every answer is grounded in approved definitions and consistent business logic. We create governed metric definitions, configure AI/BI Genie spaces, and migrate dashboards so answers can be reproduced, reviewed, and traced back to the same underlying logic.

AI use cases are only as reliable as the data beneath them. We build early-warning and intervention models, enrollment and retention forecasting, content efficacy analysis, and product telemetry insights with Mosaic AI on the governed lakehouse. Teams can move from disconnected experiments to models based on controlled, current data.

A lakehouse still needs daily care after the initial implementation is complete. We monitor pipelines, manage schema changes, administer workspaces, and tune DBU costs so the platform continues to perform without requiring you to hire an entire platform team. We can also work alongside your internal team and transfer ownership when it is ready.

Ingestion, transformation, business intelligence, and machine learning operate on the same governed data.This reduces the copies that can drift between a warehouse, a data lake, dashboards, and separate machine-learning environments. Reporting and AI teams work from a shared foundation instead of reconciling competing versions of the truth.

Unity Catalog provides fine-grained access control, row- and column-level security, lineage, and audit trails across tables, notebooks, and dashboards. We configure those controls around PII, education privacy obligations, and the way different institutional roles need to use the data.

Databricks is deployed in your AWS, Azure, or Google Cloud account. Delta Lake keeps data in open formats within your own cloud storage. Your organization retains ownership of the infrastructure and data. If operations move in-house, we can transfer the required knowledge, processes, and platform responsibilities to your team.

We can assess Databricks against your current stack using your actual workloads, data volumes, and operating requirements. Where appropriate, the assessment includes a working proof of concept. Pilots can begin with one urgent problem and expand only after the value has been demonstrated in your environment.

Organizations that want the outcome without assembling every component themselves can use EdDataHub, Magic EdTech’s managed education data foundation built on Databricks. EdDataHub brings together pre-built connectors, education data models, standards alignment, governance, and ongoing operations. It is deployed in your cloud and run by a team that understands education data.