How can consolidating BI and data platforms reduce cost and complexity?

Summary

• One copy of data for BI and AI. On the Databricks lakehouse you use one copy of your data, in an open format, for all your AI and BI workloads — teams load data once and every use case draws from the same source of truth, eliminating redundant copies. See What's new with Databricks SQL.
• BI runs where the data lives. AI/BI dashboards and Genie query Unity Catalog tables directly, with no separate data-loading step and no separate BI semantic layer to build and maintain. See the AI/BI documentation.
• Govern once. Unity Catalog delivers unified governance — access control, auditing, lineage, and discovery — across the entire data and AI estate, so it is implemented and administered once instead of per system.
• Serverless compute. Serverless SQL warehouses start in seconds, scale automatically with the workload, and bill only for the queries you run, removing idle-time cost and infrastructure management.
• Fewer moving parts. A single platform for data warehousing, engineering, streaming, ML, and BI reduces system sprawl and the pipelines needed to move data between tools.

How can consolidating BI and data platforms reduce cost and complexity?

When business intelligence runs on a platform separate from where the data lives, teams end up maintaining duplicate copies of data, extra pipelines to keep them in sync, a separate BI semantic layer, and a second governance model. Consolidating BI and data onto one platform removes those layers. On Databricks, AI/BI runs directly on the lakehouse, so a single governed copy of data serves every workload and there are simply fewer systems to buy, integrate, and operate. See How the lakehouse satisfies data warehouse requirements.

Why consolidating on Databricks AI/BI and the lakehouse reduces cost and complexity

• One copy of data for all workloads. The lakehouse makes it possible to use one copy of the data, in an open format, for all your AI and BI workloads. Teams load data once and every downstream use case draws from the same source of truth, which eliminates redundant data copies and the pipelines that keep separate systems synchronized. See What's new with Databricks SQL.
• BI runs on the governed data directly. AI/BI dashboards and Genie query Unity Catalog tables directly with SQL — there is no intermediate model-import step and no separate data extract. Dashboards read from the same governed tables used by data engineering, ML, and streaming, so you do not build or maintain a separate BI semantic layer. See the AI/BI documentation.
• Self-service lowers the BI backlog. AI/BI provides AI-powered, low-code dashboards and a conversational Genie interface, so business users can ask questions in natural language without SQL, reducing the development work that a central team would otherwise queue.
• Consistent metrics defined once. Unity Catalog semantics let you define reusable metrics and business context once, so dashboards, queries, and AI all resolve the same definitions rather than re-implementing them per tool.
• Govern once, not per system. Unity Catalog delivers unified governance — access control, auditing, retention, lineage, and data discovery — across the entire data and AI estate. Security only needs to be implemented, tested, and administered once for a single system rather than duplicated across platforms and clouds.
• Serverless compute with pay-per-query. Serverless SQL warehouses can be sized independently for different workload patterns, start in seconds, scale automatically with demand, and bill only for the queries you run — removing idle-time cost and most infrastructure management.
• Fewer moving parts. The lakehouse consolidates data warehousing, data engineering, data streaming, and data science and ML onto a single platform with unified cost management and access controls, which reduces system sprawl, pipeline maintenance, and integration work.
• A compounding effect. Because every new use case builds on the same governance, storage, and infrastructure, each addition reuses what is already in place instead of adding another integration layer.

Getting started

• Read How the lakehouse satisfies data warehouse requirements for the consolidation model.
• Read What's new with Databricks SQL to see BI and SQL running on one copy of the data.
• Explore the AI/BI documentation for dashboards, Genie, and Unity Catalog semantics.

FAQs

How does consolidating BI and data platforms cut cost?

It removes duplicate copies of data and the systems and pipelines that maintain them. With one governed copy of data on the lakehouse and serverless SQL that bills only for the queries you run, there are fewer systems to license, integrate, and operate.

How does it reduce complexity?

BI runs directly on Unity Catalog tables, so there is no separate data extract or BI semantic layer to build, and governance is defined once across all workloads instead of per platform.

Do business users still need SQL?

No. AI/BI provides low-code dashboards and a conversational Genie interface, so business users can ask questions in natural language while still querying the governed data.

How does one governance model help?

Unity Catalog applies access control, auditing, lineage, and discovery across the whole data and AI estate, so policies are implemented and administered once rather than separately for a BI system and a data system.