Overview

University Analytics is undertaking a multi-year modernization initiative to transition institutional analytics and reporting workloads to Databricks. 

Our vision is to establish a single, trusted data platform that centralizes all institutional data, strengthens governance, and enables timely, informed decision-making.

This unified lakehouse environment will serve as the foundation for enterprise reporting and advanced analytics, with integrated AI capabilities to enable deeper insights and better decision-making.

What is Databricks?

Databricks is a modern data and analytics platform that unifies data engineering, reporting, analytics, and data science within a single, scalable environment.

Why Databricks?

Databricks is a strategic investment in the university’s data future. It enables us to:

  • Eliminate data silos and establish a single source of truth
  • Scale efficiently as institutional data and reporting needs grow
  • Improve trust in data through consistent governance and lineage
  • Increase speed to insight for leadership and operational teams
  • Support emerging capabilities in advanced analytics and AI

Databricks enables the university to reduce long-term costs by consolidating into a single, scalable platform with flexible, pay-as-you-use computing and no proprietary lock-in, eliminating redundant tools and inefficiencies.

This transition will enable our university to operate more efficiently while building the capabilities needed to succeed in the AI era.

What's happening?

All SAS and MARS reports will be consolidated and migrated to Databricks.

This page provides high-level status updates, planning information, and resources related to the migration of:

  • SAS reporting and data processes
  • MARS dashboards and reports

All information on this page is intended for planning and awareness purposes only and is subject to change as the project evolves.

Transition Timeline

Timeline is high-level and subject to change based on institutional priorities and implementation progress.

  • June 2025 – December 2025:
    • Vendor/implementation partner selection, contract execution, SAS data profiling
    • Databricks Working Group was established to enable early cross-functional participation across university units
  • January 2026 – December 2026:
    • Platform implementation, SAS workloads transition (Campus Solutions, state/federal reports, and HR)
  • January 2027 – September 2027:
    • SAS reports/dashboards transition
    • MARS Finance/Budget workloads
  • September 2027 – October 2028:
    • MARS Finance/Budget reports and dashboards
  • October 2029:
    • Formalize Data Steward Councils for various domains, along with Data Stewardship training and begin data documentation

What Clients & Stakeholders Can Expect

  • Early collaboration on report and dashboard priorities
  • Clearly defined roles and responsibilities
  • Phased rollouts to reduce operational impact
  • Regular, transparent communication on progress
  • Continued access to existing SAS and MARS dashboards during transition

How We Ensure Trusted, Consistent Data

Data in Databricks will be governed through a structured stewardship process. University Analytics will collaborate and work with domain experts and business owners to define, validate, and certify critical data as “gold” to ensure accuracy, consistency, and trust across the university.

 
Will all SAS and MARS reports be migrated?

The intent is to migrate most active and business-critical reports. Some reports may be retired, consolidated, or redesigned based on usage, redundancy, or data availability.

Will existing dashboards stop working immediately?

No. Existing SAS and MARS dashboards will remain available during the transition period, subject to standard system support policies.

Will reports look exactly the same in Databricks?

Not always. While functional parity is a goal, some reports may be modernized or redesigned to take advantage of Databricks capabilities and AI.

What if timelines change?

Timelines are estimates, not guarantees. Adjustments may occur due to technical findings, institutional priorities, or resource constraints.

How will users be notified of changes?

Users will receive updates through:

  • This page

  • Project communications

  • Stakeholder meetings

  • Department announcements

What does “single source of truth” mean? Why is it important?

A single source of truth means all users rely on the same, consistent, and validated data. It’s important because it eliminates conflicting reports, increases trust and ensures decisions are based on accurate information.

How does this implementation set our institution apart?

It modernizes our data infrastructure, enabling faster insights, advanced analytics, and scalable data access - putting us on par with leading institutions using cloud-based platforms.

Can I still receive support in SAS and MARS?

Yes, during the transition period. However, support will gradually shift toward Databricks as we move away from SAS and MARS.

What does this implementation mean for my department?

It depends on how your department currently uses data. If you rely on SAS or MARS reports and dashboards, you will be notified as those are transitioned to Databricks. If your department manages its own data systems or BI tools, you will have the option to move into Databricks and continue your analytics within the unified lakehouse environment.

What does “data lineage” mean and why is it important?

Data lineage tracks where data comes from, how it changes, and how it’s used. It’s important for transparency, trust, and troubleshooting data issues. For example, today if we need to know how many reports contain SSNs and who has access to them, we would have to manually review each report. With Databricks, we will be able to answer that question immediately through built-in lineage and governance capabilities.

What does “unified data environment” mean and why is it important?

A unified data environment brings data from across systems into one integrated platform, eliminating silos and making it easier to connect information.

This is important because it allows us to tell a complete, end-to-end data story. For example, we can understand a student’s journey - from where they come from, to their enrollment at UTA, their involvement in activities, library usage, and campus services like MAC membership - through graduation, and even as they become alumni or faculty. At that point, we can also connect their research, teaching, and grant activity as their role evolves.

This creates a true 360-degree view, enabling better insights, improved student success, institutional growth, and stronger ROI-driven decision-making. Today, this level of insight is difficult because data exists in silos and it is not easy to connect the dots.

What is a lakehouse and how is it different from SAS or MARS (Oracle data warehouse)?

A lakehouse combines the flexibility of a data lake with the structure of a data warehouse. Unlike SAS or MARS, it supports both structured and unstructured large-scale data, real-time processing, and advanced analytics in one platform.

What capabilities exist in Databricks?

Databricks supports data engineering, data science, machine learning, AI, and SQL analytics - all within a single platform.

Are there resources for training?

Yes. Databricks offers free training resources, including role-based learning paths, self-paced courses, and documentation to help users get started.

You can explore available training here: https://www.databricks.com/learn/training

Additional role-based learning is available through Databricks Academy. Throughout the transition, onboarding and data stewardship training will also be provided by University Analytics.

What type of governance will be established and why?

The governance framework will define roles and responsibilities, establish data quality standards, and set access policies and controls. It will also include Data Steward Councils for each domain. This structure ensures that data is properly documented, secure, accurate, and used appropriately across the organization.

Can Databricks feed data into other campus applications?

Yes. Databricks can integrate with and provide data to other systems and applications across campus.