Bridge the Gap in Your Data Stack: Leverage Databricks BI/AI to Enhance Traditional BI

Introduction

Many organizations place complete trust in their BI tool to exploit their information, but this overreliance has created disorder: oversized and costly licensing, models trying to process data volumes they weren't designed for, and ecosystem fragmentation that multiplies silos and obstacles.

The result? High recurring costs, poor performance, excessively long response times for certain dashboards, and data governance struggling to maintain a unified vision.

It's fair to acknowledge that tools like Power BI, Qlik, or Tableau have been key to democratizing data, allowing business teams to gain insights without waiting for IT. However, that success has generated attachment beyond their optimal purpose.

This blog doesn't aim to replace them, but offers practical criteria to identify when to complement their capabilities with Databricks' native Lakehouse and apply best practices.

By the end of this reading, you'll see concrete examples: how to reduce costs, for example, by decreasing underutilized licenses, and improve performance, for example, by moving complex aggregations to pipelines in Databricks. With these guidelines, you can design a hybrid architecture that leverages the best of both worlds.

Analysis Scenario

For educational purposes, we'll use the example of PowerBI connected to Databricks SQL Endpoint versus using native BI/AI capabilities in Databricks SQL. That is, we start from a scenario that, when well utilized, should be considered optimal, but it's highly likely we can identify some practices that are incorrect or could be improved for a more cost-efficient experience (which is the blog's objective).

Power BI + Databricks SQL Endpoint

From a technical standpoint, Microsoft Power BI connects to Databricks SQL Endpoints via JDBC/ODBC: each visual triggers an SQL query to the endpoint, executes in the cluster, and returns the result for rendering. Key points to watch:

Network latency (50–200 ms): Each query incurs a round-trip via JDBC/ODBC and serialization/deserialization. While it rarely breaks the user experience, it's important to keep in mind (perhaps in near-real-time dashboards, it could have a minimal effect).

Source: Medium - Author: Databricks SQL SME

Licensing cost: In practice, many companies pay for more licenses than they actually use because they don't know who consumes each data product and for what purpose. In numerous cases, you could forgo additional licenses by using:

• Databricks Apps for simple embedded applications.

• Genie, a conversational agent for ad-hoc queries.

• Light dashboards in Databricks BI when you don't need visual complexity.

Not everyone needs licenses if they don't really need them. Advanced users who model data can benefit from having direct access to the Databricks console and working in a more performant and cost-efficient environment.

Modeling in the correct layer: Avoid pushing heavy transformations to DAX or prep tools (Tableau Prep, Power Query). These engines don't scale well with large volumes, create bottlenecks, and often can't process the request even with all the time in the world.

Instead:

• Pre-aggregate and validate data in Databricks (Delta Live Tables, materialized views).

• Leave only light filters and visualization in Power BI.

Advice: A dashboard should not perform heavy aggregations in each visualization. If you need to explore data in its entirety, you may need another data product or a different platform for consumption. Forcing the dashboard to process all the data is the main cause of poor performance.

Databricks BI/AI

Databricks executes and renders queries directly on the platform: visualizations are fed from the same SQL Endpoint and the result cache, without leaving Databricks. Thus, the query, caching, and drawing of the dashboard occur in a single optimized Spark environment, resulting in minimal latencies and a single cost for compute.

Some aspects to consider are:

• Visual maturity: The native gallery covers the essentials (line charts, bar charts, tables), but doesn't offer advanced custom visuals. Advanced users requiring additional capabilities can continue using their favorite BI tool (and they justify their license).

Source: Databricks

Perhaps this section of the blog won’t age well as Databricks has a strong commitment in this area, and I believe over time it will be able to cover all use cases.

• Refresh configuration: It's not a negative point, but it is important to adjust auto-refresh to balance freshness and compute usage, avoiding unnecessary charges. Although Databricks BI allows refresh every 60 seconds, it's rarely necessary and can generate unnecessary charges; it's advisable to define a broader interval according to the use case.

• Security and permissions: All access is managed from Unity Catalog; review roles in the SQL Endpoint to isolate dev/prod environments. Again, this is not a negative point; in fact, it facilitates governed data democratization and avoids silos such as OLAP cubes.

It's important to note that this analysis describes an "ideal" scenario on paper. If we compare with a BI tool (Example: Qlikview) hosted on a monolithic server or static cluster (VMs with fixed cores), with manual provisioning, storage in a relational DW, and legacy OLAP cubes, the conclusions and recommendations would change drastically.

Source: Microsoft

Conclusions and Recommendations

As we've seen, Databricks BI/AI doesn't come to replace Power BI, but to complement it: it's an economical and strategic option to drastically reduce licensing costs while reviewing consumption patterns and detecting optimization opportunities at the enterprise level.