Building a Smarter Azure Cost Analysis Pipeline with Power BI and Databricks Genie
At Cloudaeon, we help enterprises take control of their Azure cloud spend by building smart, scalable cost analytics solutions. In this blog, we showcase a modern approach that uses Databricks, Power BI, and Databricks Genie to transform raw Azure cost exports into actionable, domain ready insights.
Our solution enables:
Executive dashboards in Power BI
Conversational, real-time insights with Databricks Genie
A single, curated dataset ensuring consistency across tools
As organisations adopt Lakehouse architectures, billing visibility must shift from centralised finance teams to individual domains like engineering, product, or operations. Cloudaeon’s solution is designed for this evolution empowering each domain with self-service cost intelligence without compromising on governance or data trust.
Managing cloud costs effectively is essential for organisations using Azure. But what if you could not only visualise these costs but also ask intelligent, natural language questions like "Which department overspent last month?" or "What were the top five projects by cost trend last quarter?"
With Azure Databricks and Power BI, and now Databricks Genie, you can.
In this guide, we'll show how to build an end to end solution that takes Azure cost exports, transforms them intelligently using Databricks (including advanced tag parsing), visualises the results in Power BI, and makes the same dataset available to Databricks Genie for conversational AI insights.
Author
Pravin
Ghavare
I'm a Data & AI Lead with 9 years of experience delivering scalable Azure based solutions. I lead cross-functional teams to build high-performance, cost optimised data platforms with a strong focus on observability, FinOps, and performance tuning. I collaborate closely with stakeholders to align technical delivery with strategic business goals.
Connect with
Pravin
Ghavare
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Why Smart Azure Cost Analysis Matters?
Tagging Azure resources is a foundational cloud governance strategy but transforming those tags into actionable cost insights at scale? That’s where many organisations struggle.
With the combined power of Azure Databricks, Power BI, and Databricks Genie, you can turn complex, unstructured billing data into an intelligent, user friendly analytics ecosystem:
Executive ready dashboards in Power BI for strategic visibility.
Conversational intelligence with Databricks Genie for real-time, ad hoc decision making.
A unified semantic layer, one curated dataset powering both tools, ensuring consistency and trust in your insights.
This isn't just about reporting costs, it's about empowering finance, engineering, and leadership teams to proactively manage cloud spend with the speed and scale of modern data platforms.
Architecture Diagram:
Step by Step Implementation Azure Cost Analysis
Step 1: Automate Azure Cost Data Export
In the Azure Portal, go to Subscriptions, select your target subscription, and navigate to: Cost Management → Cost Analysis → Configure Subscription → Exports
Click + Add to define a new export with the following settings:
Scope: Select either a Subscription or Management Group
Export Type: Choose a frequency, daily, weekly, or monthly
Format: Select a format that suits your processing pipeline (e.g., CSV or Parquet)
Destination: Specify the Azure Storage Account where export files will be delivered
Step 2: Ingest Cost Data into Azure Data Lake
Once the daily exports are being delivered to your designated Azure Storage Account:
Organise the data into a “Raw” zone in your Data Lake (e.g., /raw/azure_cost_exports/).
Ensure overwrite mode is enabled if you're using the same filename daily (as per your architecture).
Optionally, configure lifecycle policies for file retention or archival if needed.
Step 3: Transform & Enrich Data with Databricks
Launch a Databricks notebook or job to begin processing the raw cost export data.
Key transformation steps:
Read the raw CSV/Parquet into a Spark DataFrame
Parse the tags column into separate key value pairs (e.g., Department, Environment, Owner)
Normalise fields like resourceId, meterCategory, usageDate
Derive cost allocation metrics, such as:
Cost per project/ department
Unused resources
Resource type aggregations
Optional enhancements:
Add a data quality check to filter out nulls or malformed tag entries
Store intermediate outputs in a “Curated” Delta Table (e.g., cost_insights_curated)
Schedule the notebook as a job with a cluster to run daily after export lands
Step 4: Connect Power BI to Curated Dataset
Power BI can now consume your curated dataset for rich visualisations.
In Power BI Desktop:
Connect to the curated Delta Table using Azure Databricks connector
Build visualisations for:
Cost trends over time
Cost by department/project
Forecast vs actual
Tagged vs untagged resources
Publish to Power BI Service and create:
Scheduled refreshes (matching your export frequency)
Role based dashboards for finance, engineering, and leadership
Step 5: Enable Databricks Genie for Conversational Insights To empower users with natural language analytics:
Register the same curated Delta Table to Databricks Genie
Define table metadata and tag fields for enhanced semantic understanding (e.g., explain tag_department or cost_in_usd)
Users can now ask:
“Show me the top 5 cost centres by growth”
“What’s the average cost of untagged VMs last month?”
This makes your cost dataset not just accessible, but intelligent.
Alternative Approach: Native Dashboards in Databricks
For teams looking to streamline their stack, the same curated cost dataset can also power Databricks native dashboards, eliminating the need for external BI tools like Power BI.
With built in Databricks' visualisations, role-based access, and real-time Spark-backed performance, Databricks dashboards offer a unified environment for both data engineering and cost analytics, reducing complexity while keeping everything within a single platform.
Conclusion
Azure cost data doesn’t have to be complex. With the right architecture, Databricks for transformation, Power BI and Genie for insights, and a unified dataset underneath, you get clarity, speed, and real impact.
At Cloudaeon, we don’t just build dashboards, we build cost intelligence. Whether you’re moving to a Lakehouse model or empowering domains with self-serve analytics, we help you own your cloud spend with confidence.
Click here, and let’s turn your cost data into decisions.
