
Overview
Replicating a semantic model across Microsoft Fabric tenants was a major roadblock for our Edtech client. Since semantic models are tightly coupled to their tenant, workspace, and Lakehouse, migration became highly complex, especially when advanced DAX logic and metadata had to be preserved. To overcome this, we built a reusable solution using .bim files and a Fabric Notebook. This approach automated the process and enabled seamless transfer of the model from Lakehouse A in Tenant A to Lakehouse B in Tenant B.
- ~72%
- Faster migration than manual rebuilds
- 99.99%
- Error-Free System Deployment
Customer Challenges
Migrating semantic models across Microsoft Fabric tenants presents significant hurdles. Organizations encounter several roadblocks that slow deployment and increase the risk of errors.

No Built-In Migration Method
Microsoft Fabric lacks native support for cross-tenant model migration, forcing teams to rely on complex workarounds.

Tenant-Specific Dependencies
Elements like lineageTag and database connections are tied to a specific tenant, preventing direct imports into new environments.

Manual Recreation Burden
Rebuilding models manually is time-consuming, error-prone, and risks losing critical DAX logic and metadata.
Solutions
To address these challenges, an automated migration approach was designed to replace manual, error-prone processes. By using a Fabric Notebook the solution ensured models could be transferred across tenants with accuracy and repeatability.
Automated Export & Import
A Fabric Notebook powered by sempy_labs handled the seamless export and import of .bim files.
Metadata Alignment
A dummy model from the target tenant was exported, and its metadata was applied to the source .bim, ensuring compatibility.
Step-by-Step Workflow
The notebook delivered a structured, automated process that preserved DAX logic and metadata while reducing errors.
Services
Microsoft Fabric Notebooks
SemPy Labs
.bim files
Notepad++
Benefits

Accurate and Consistent Migration
Automated processes ensured DAX logic, metadata, and model structures were preserved across tenants.

Reduced Errors and Manual Effort
Minimized risk of mistakes by replacing error-prone manual migration steps.

Faster Deployment
Streamlined workflow accelerated the tenant-to-tenant migration process.

Repeatable Approach
The methodology can be applied for future migrations cross-tenant, cross-workspace, or even same workspace different lakehouse.

Proven, Reliable Outcomes
Validated results ensured smooth transitions and consistent analytics across tenants.
Conclusion
This reusable method enables reliable, metadata-preserving cross-tenant semantic model migration using .bim files and Fabric notebooks, turning a previously manual process into a guided, automated one.
Case studies
Related case studies

AI-Augmented ETL for Automating Multi-Portal Retail Reporting on AWS
A consumer beauty and personal care brand needed to bring its Walmart and Amazon supplier and performance data into a single AWS RDS PostgreSQL database. Walmart's data was split across a standard supplier API and three separate reporting portals (Retail Link, Scintilla, and Supplier One), each with its own authentication, file formats, and update cadence. NeenOpal designed a hybrid ETL platform with two ingestion paths running in parallel: an orchestrated API pipeline for the data Walmart exposes directly, and an AI-driven report retrieval pipeline for the data that fell outside the scope of the client's existing API access. Both paths converge on a shared Glue transformation layer, a common S3 staging area, and a single PostgreSQL database, with a separate one-time Spark job bringing in Amazon Vendor Central's historical data. The result is a single, config-driven system rather than a set of one-off integrations.
Read case study
Tableau Dashboard Development for a US Insurance Organization: 4 Data Sources Unified
Our client, an insurance organization with field teams spread across every US state, partnered with NeenOpal to replace a manual, spreadsheet-driven reporting process with a consolidated Tableau dashboard environment governed by row-level security. Operational data lived in four separate systems covering Airtable, Salesforce, Google Analytics 4, and Google Search Console, and bringing them together meant exporting and stitching the data by hand, which left leadership without a single reliable view of performance. NeenOpal consolidated all four sources into an Azure SQL Server layer, reviewed and corrected the data quality issues that surfaced along the way, and delivered three Tableau dashboards across seven screens that refresh automatically every day. Row-level security was implemented across all fifty states and the independent districts, driven by an entitlement model the client maintains through a single Google Sheet, so every representative sees the states they are responsible for and nothing more.
Read case study
Unifying Multi-Region Sales Pipeline Analytics with Power BI and Power Query
Our client, a global healthcare company with commercial operations across Africa and Western Europe, partnered with NeenOpal to build a unified sales pipeline dashboard spanning more than six regional teams across two continents. Pipeline data was managed in isolation by each region, reported in inconsistent formats, and pulled from different systems including Salesforce and SAP, leaving leadership without a consolidated view of opportunities, orders, and revenue. NeenOpal designed and delivered a centralized sales pipeline dashboard in Power BI, powered by an automated Excel and Power Query ETL layer, that standardizes data from every region into one live view. The solution tracks each opportunity from lead to shipped order, benchmarks performance against multiple internal targets, forecasts revenue from existing customers, and lets non-technical users refresh the entire report in one to two clicks.
Read case study
Unified Cross-Platform Intelligence Across 20+ Data Sources
A leading dental industry media company partnered with NeenOpal to build a centralized marketing intelligence infrastructure from the ground up. With audience data, campaign performance, CRM activity, webinar engagement, and learning platform metrics scattered across more than a dozen disconnected systems, the client lacked a unified view of marketing performance and customer engagement. NeenOpal designed and delivered a fully automated, cloud-native data platform that ingests data from 20+ marketing, CRM, and engagement platforms into Google BigQuery enabling reliable, weekly-refreshed reporting and cross-platform analytics from a single source of truth.
Read case study
Custom CI/CD Pipeline for Multi-Tenant SaaS Delivery on Microsoft Fabric
Our client, a SaaS product company built on Microsoft Fabric, needed a reliable and scalable way to deploy analytics content across multiple customer tenants — each with different service tiers, feature sets, and upgrade preferences. After evaluating Fabric's native deployment pipelines, critical limitations made it unfit for a multi-tenant SaaS model. NeenOpal designed and implemented a fully custom CI/CD pipeline using GitHub Actions and the Fabric REST APIs, giving the team complete control over what gets deployed, to whom, and when.
Read case study
Automated Ticket Invoice Processing & Analytics Using AWS Textract and Redshift
A media and event-driven organization managing high-volume ticket sales receives periodic ticket invoices via email from multiple vendors and booking partners. These invoices arrived in semi-structured and unstructured formats (PDFs, scanned images, email attachments), making manual processing time-consuming, error-prone, and non-scalable. To address this, we designed and implemented a fully automated, serverless data pipeline on AWS that ingests invoices from email, extracts structured data using OCR, Custom Scripts, and NLP, stores curated datasets in Amazon S3, and loads analytics-ready data into Amazon Redshift. This enabled descriptive analytics and downstream ticket sales forecasting with strong governance, observability, and cost efficiency.
Read case study



