Tableau to Databricks AI/BI Migration with Modern Data Stack
Databricks' own internal data team relied on Tableau for analytics and reporting, which introduced significant licensing costs and limited native integration with the Lakehouse platform they were building. Data ingestion from diverse sources — Google Sheets, Google Ads, various SaaS platforms — lacked a unified approach, creating data silos and inconsistent metrics. The team needed a modern data stack that dogfooded their own platform: native Databricks AI/BI dashboards powered by the Photon engine, a proper medallion architecture for data quality, and CI/CD-enabled dashboard deployment. Vrahad Analytics was engaged to execute this transformation, proving that Databricks' own tools could replace a mature BI platform while adding capabilities Tableau couldn't offer.
Client
Databricks (Internal)
Cloud Platform
Multi-Cloud
Duration
4 Months
The Challenge
Even at Databricks itself, the internal data team faced the common challenge of analytics tool sprawl and fragmented data pipelines. Migrating away from an established BI tool like Tableau required not just technical migration but also demonstrating feature parity and improved capabilities to stakeholders who were accustomed to Tableau's interface.
Tableau licensing costs were substantial and growing with the team size, representing a significant line item that could be eliminated by migrating to native Databricks AI/BI capabilities that were already part of the platform.
Data ingestion from Google Sheets, Google Ads, and various SaaS platforms was handled through a patchwork of custom scripts, manual uploads, and ad-hoc connectors with no centralized monitoring or error handling.
No medallion architecture existed — raw data was often consumed directly by dashboards without proper cleansing, validation, or transformation, leading to inconsistent metrics and frequent data quality complaints from stakeholders.
Dashboard deployment was manual — analysts would build dashboards in Tableau and publish them through the Tableau server, with no version control, no code review process, and no ability to roll back problematic changes.
Custom Google Ads API integration was needed to pull advertising performance data at the granularity required for marketing analytics, which Tableau's built-in connectors couldn't provide at the required detail level.
Stakeholder adoption risk — many team members were deeply familiar with Tableau's interface and were skeptical that Databricks AI/BI could deliver equivalent or better analytics experiences.
Our Solution
We executed a comprehensive migration from Tableau to Databricks AI/BI, building a modern data stack from the ground up. The solution encompassed automated multi-source ingestion via Fivetran, a proper medallion architecture for data quality, custom Google Ads API integration, and industry-first version-controlled dashboard deployment using Databricks Asset Bundles with full Git-integrated CI/CD.
Migrated all analytics dashboards from Tableau to Databricks AI/BI, leveraging the Photon engine for sub-second query performance on analytical workloads that previously required Tableau extracts and caching.
Deployed Fivetran connectors for automated ingestion from Google Sheets, SaaS platforms, and internal databases, replacing fragile custom scripts with a managed service that provides automatic schema detection and data freshness guarantees.
Built a custom Google Ads API integration pipeline that pulls campaign performance data at keyword-level granularity, processes it through the medallion architecture, and delivers marketing analytics that weren't possible with Tableau's native connectors.
Implemented a full medallion architecture (Bronze → Silver → Gold) with automated data quality checks at each tier, ensuring that business-facing dashboards always consume validated, consistent data from the Gold layer.
Pioneered version-controlled dashboard deployment using Databricks Asset Bundles, enabling dashboards to be defined as code in Git, go through pull request review processes, and deploy through CI/CD pipelines — just like software.
Conducted stakeholder training and created self-service documentation that enabled analysts to build and modify dashboards independently, with guardrails that ensure consistency with the organization's data and design standards.
Implementation Phases
Current State Assessment & Planning
2 WeeksInventoried all Tableau dashboards, data sources, and user workflows. Assessed Databricks AI/BI feature parity, identified gaps, and designed the target architecture including the medallion data model and CI/CD pipeline for dashboards.
Data Ingestion & Medallion Architecture
5 WeeksDeployed Fivetran connectors for all data sources, built the custom Google Ads API integration, and implemented the Bronze → Silver → Gold medallion architecture with automated quality checks, deduplication, and schema evolution handling.
Dashboard Migration & AI/BI Development
5 WeeksRecreated all Tableau dashboards in Databricks AI/BI, optimized SQL queries for the Photon engine, and added new capabilities (drill-down, AI-powered insights) that weren't available in Tableau. Validated dashboard accuracy with stakeholders.
Asset Bundles CI/CD & Deployment Automation
3 WeeksImplemented Databricks Asset Bundles for version-controlled dashboard definitions, built CI/CD pipelines in Git that automatically validate, test, and deploy dashboard changes through dev → staging → production environments.
Stakeholder Training & Tableau Decommission
2 WeeksConducted hands-on training sessions for all dashboard consumers and builders, created comprehensive documentation, executed a parallel-run period for validation, and decommissioned Tableau licenses upon successful migration.
Technologies Used
Key Results
Measurable outcomes and business impact delivered through this engagement.
Eliminated Tableau licensing costs entirely by migrating to native Databricks AI/BI, resulting in significant annual savings while gaining deeper integration with the Lakehouse platform.
Built automated multi-source ingestion from Google Sheets, Google Ads, and SaaS platforms via Fivetran and custom API integrations, reducing data ingestion maintenance from 15+ hours/week to under 2 hours/week.
Implemented a full medallion architecture (Bronze/Silver/Gold) that improved data quality scores from 72% to 98%, ensuring that all business dashboards consume validated, trustworthy data.
Deployed version-controlled dashboards using Databricks Asset Bundles — an industry-first approach that treats dashboards as code with full Git history, pull request reviews, and automated CI/CD deployment.
Established end-to-end CI/CD for both data pipelines and dashboards, enabling the team to deploy changes in minutes with confidence rather than the hours of manual work previously required.
Achieved stakeholder satisfaction scores above 90% post-migration, with many users reporting that AI-powered insights and faster query performance in Databricks AI/BI exceeded their Tableau experience.
Before vs. After Comparison
BI Tool Cost
Before
Tableau licenses
After
Native AI/BI ($0 extra)
100% eliminated
Data Quality Score
Before
72%
After
98%
26pt improvement
Ingestion Maintenance
Before
15+ hrs/week
After
< 2 hrs/week
87% reduction
Dashboard Deployment
Before
Manual (hours)
After
CI/CD (minutes)
Fully automated
Query Performance
Before
Tableau extracts
After
Photon live queries
Sub-second
User Satisfaction
Before
Mixed
After
90%+ approval
Strong adoption
Migration Scale
Data Source Distribution
Analytics Platform Capability — Tableau vs AI/BI
Data Quality Score Improvement Over Time (%)
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