Microsoft Power BI connecting to Bigquery

key difference is that the native BigQuery connector uses Google-based authentication options, while the BigQuery (Microsoft Entra ID) connector lets users sign in with Microsoft Entra ID and relies on Workforce Identity Federation/SSO patterns. The Entra ID connector is the better fit when your Power BI identity model is centered on Microsoft Entra groups and you want federated access into Google Cloud.[microsoft +1]

Core difference

The native Google BigQuery connector in Power BI supports Google service account-style authentication and also works with Import or DirectQuery modes. The Microsoft Entra ID connector is a separate connector, marked as beta in Google’s documentation, and is specifically designed for Entra-based sign-in and SSO into BigQuery.

When to choose native

Use the native connector if your analytics platform team already manages Google service accounts and your access patterns are mainly Google-native. It is a practical choice for straightforward reporting pipelines where Power BI connects directly to BigQuery using familiar Google authentication. For airline BI teams, this often fits central reporting models where a data engineering team owns access and publishes curated datasets.[learn.microsoft]

When to choose Entra ID

Use the Entra ID connector if your organization wants users to authenticate with Microsoft identities and apply Entra group-based access controls across Power BI and Google Cloud. Google’s guidance shows the connector is intended to let Entra users access BigQuery data through Workforce Identity Federation and SSO. This is especially attractive in large enterprises where governance, joiner-mover-leaver processes, and conditional access are already managed in Microsoft Entra.[cloud.google]

Practical recommendation

For an enterprise airline environment, I would usually recommend:

1. Native connector for quick adoption, lower setup effort, and teams already operating with Google service accounts.[learn.microsoft]

2. Entra ID connector for governed enterprise deployments where Microsoft identity is the system of record and SSO is a priority.[microsoft +1]

If your Power BI tenant, IAM model, and security operations are Microsoft-centered, the Entra route is usually the cleaner long-term architecture. If your BigQuery platform team owns access and you want the least moving parts, the native connector is simpler.

Architecture note

One important detail is that the Entra connector depends on federation between Microsoft Entra and Google Cloud, so it is not just a Power BI setting; it is an identity architecture choice. That makes it more suitable for standardized enterprise patterns, but also more dependent on coordination across identity, cloud, and BI teams.[cloud.google]

From Blogger iPhone client

Microsoft Power BI using Rest API Python

Excellent — connecting to a Power BI workspace using Python lets you automate publishing, refreshing, or managing datasets via the Power BI REST API.

Here’s a full, clean, copy-friendly guide (no code cells, no formatting issues).

You can select all and copy directly into your Python environment.

Connect to Power BI Workspace using Python

Step 1 — Install required Python libraries

pip install requests msal

Step 2 — Set up Azure AD app (Service Principal)

1. Go to Azure Portal → Azure Active Directory → App registrations → New registration
2. Note down:
4. Application (client) ID
5. Directory (tenant) ID
6. 
   
7. Create a Client Secret under “Certificates & Secrets”.
8. In Power BI Service → Admin portal → Tenant settings → Developer settings, enable:
10. Allow service principals to use Power BI APIs
11. Allow service principals to access Power BI workspaces
12. 
    
13. Add your app to the target workspace:
15. Power BI → Workspace → Access → Add → Enter app name → Assign role (Admin or Member)
16. 
    
17. 
    

Step 3 — Define authentication details in Python

import requests

import msal

Tenant ID, Client ID, and Client Secret from your Azure AD app

tenant_id = “YOUR_TENANT_ID”

client_id = “YOUR_CLIENT_ID”

client_secret = “YOUR_CLIENT_SECRET”

Power BI API scope and authority

authority = f”https://login.microsoftonline.com/{tenant_id}”

scope = [“https://analysis.windows.net/powerbi/api/.default”]

Create MSAL confidential client app

app = msal.ConfidentialClientApplication(

client_id,

authority=authority,

client_credential=client_secret

)

Get access token

token_result = app.acquire_token_for_client(scopes=scope)

access_token = token_result[“access_token”]

print(“Access token acquired successfully!”)

Step 4 — Connect to Power BI and list all workspaces

headers = {

“Authorization”: f”Bearer {access_token}”

}

response = requests.get(“https://api.powerbi.com/v1.0/myorg/groups”, headers=headers)

if response.status_code == 200:

workspaces = response.json()[“value”]

for ws in workspaces:

print(f”Name: {ws[‘name’]} | ID: {ws[‘id’]}”)

else:

print(“Error:”, response.status_code, response.text)

Step 5 — List all reports in a specific workspace

workspace_id = “YOUR_WORKSPACE_ID”

url = f”https://api.powerbi.com/v1.0/myorg/groups/{workspace_id}/reports”

response = requests.get(url, headers=headers)

if response.status_code == 200:

reports = response.json()[“value”]

for report in reports:

print(f”Report: {report[‘name’]} | ID: {report[‘id’]}”)

else:

print(“Error:”, response.status_code, response.text)

Step 6 — (Optional) Upload a new .pbix report to workspace

pbix_file_path = “C:\Reports\FinanceDashboard.pbix”

dataset_display_name = “FinanceDashboard”

url = f”https://api.powerbi.com/v1.0/myorg/groups/{workspace_id}/imports?datasetDisplayName={dataset_display_name}”

with open(pbix_file_path, “rb”) as pbix_file:

response = requests.post(

url,

headers={

“Authorization”: f”Bearer {access_token}”,

“Content-Type”: “application/octet-stream”

},

data=pbix_file

)

if response.status_code in [200, 202]:

print(“Report uploaded successfully!”)

else:

print(“Error:”, response.status_code, response.text)

✅ Notes

• The msal library handles secure Azure AD authentication.
• The access token is valid for about 1 hour — refresh when needed.
• You can perform additional actions using the Power BI REST API (refresh datasets, rebind reports, delete reports, etc.).
• For production automation, store secrets in Azure Key Vault.

Would you like me to extend this with dataset refresh automation (Python script that triggers a refresh and checks its status)?

From Blogger iPhone client

Publish Microsoft power bi using power shell

PowerShell Script — Publish Power BI Report to Cloud

Install Power BI PowerShell module (run once)

Install-Module -Name MicrosoftPowerBIMgmt -Scope CurrentUser

Login to Power BI Service

Login-PowerBIServiceAccount

Define variables

$pbixPath = “C:\Reports\SalesDashboard.pbix”

$workspaceName = “Finance Analytics”

$reportName = “Sales Dashboard”

Get workspace ID

$workspace = Get-PowerBIWorkspace -Name $workspaceName

$workspaceId = $workspace.Id

Publish report to Power BI Service

New-PowerBIReport -Path $pbixPath -Name $reportName -WorkspaceId $workspaceId -ConflictAction CreateOrOverwrite

Verify upload

Get-PowerBIReport -WorkspaceId $workspaceId

Optional — Save as reusable PowerShell script (publish_report.ps1)

param (

[string]$pbixPath,

[string]$workspaceName,

[string]$reportName

)

Import-Module MicrosoftPowerBIMgmt

Login-PowerBIServiceAccount

$workspace = Get-PowerBIWorkspace -Name $workspaceName

New-PowerBIReport -Path $pbixPath -Name $reportName -WorkspaceId $workspace.Id -ConflictAction CreateOrOverwrite

Logout-PowerBIServiceAccount

Example run command

.\publish_report.ps1 -pbixPath “C:\Reports\Finance.pbix” -workspaceName “Finance BI” -reportName “Finance Overview”

Advanced option — Use REST API directly

$token = (Get-PowerBIAccessToken).AccessToken

$workspaceId = “xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx”

Invoke-RestMethod -Uri “https://api.powerbi.com/v1.0/myorg/groups/$workspaceId/imports?datasetDisplayName=SalesDashboard” -Headers @{Authorization = "Bearer $token"}

-Method Post -InFile "C:\Reports\SalesDashboard.pbix"

-ContentType “application/octet-stream”

✅ Notes

• Make sure you have permission to publish to the workspace.
• Use -ConflictAction CreateOrOverwrite to update existing reports.
• For automation, use service principals (App registration in Azure AD).
• This method works for Power BI Pro and Power BI Premium workspaces.

From Blogger iPhone client

Microsoft power bi vs Tableau relative comparison

Great question — both Microsoft Power BI and Tableau are top-tier business intelligence (BI) tools, but Power BI has some clear advantages, especially for organizations already within the Microsoft ecosystem or focused on cost efficiency and scalability.

Here’s a detailed comparison highlighting Power BI’s advantages over Tableau 👇

⚙️ 1. 

Integration with Microsoft Ecosystem

Advantage: Seamless connectivity with Microsoft products

• Power BI tightly integrates with Excel, Azure, Microsoft 365, Dynamics 365, and Teams, making it ideal for organizations using Microsoft technologies.
• Users can easily import data from Excel, share dashboards through Teams, and leverage Azure Synapse or SQL Server for data storage and processing.
• Example: Finance teams can directly publish Excel-based models into Power BI with one click.

💰 2. 

Cost and Licensing

Advantage: Significantly cheaper than Tableau for most deployments

• Power BI Pro: ~$10/user/month
• Power BI Premium: starts at ~$20/user/month (or capacity-based)
• Tableau Creator: ~$70/user/month
• Tableau Server/Cloud pricing also adds cost complexity.
• Impact: Power BI is far more cost-effective for organizations with many report viewers or casual users.

🧩 3. 

Ease of Use (especially for Excel users)

Advantage: Familiar and easy learning curve

• Power BI’s interface and DAX (Data Analysis Expressions) are intuitive for Excel power users.
• Tableau requires users to learn its visualization grammar and interface, which can be less familiar.
• Result: Faster adoption and reduced training costs.

☁️ 4. 

Cloud Integration and Governance

Advantage: Deep integration with Azure Active Directory and Microsoft Fabric

• Power BI provides built-in identity, access, and data governance through AAD and Microsoft Purview.
• Power BI integrates natively into Microsoft Fabric, providing unified data engineering, data science, and BI on a single platform.
• Result: Simplified governance and security in cloud or hybrid environments.

🔗 5. 

Data Connectivity and Real-Time Analytics

Advantage: Extensive connectors and native streaming capabilities

• Power BI offers native connectors to hundreds of sources (SQL, Azure, SharePoint, Dynamics, Salesforce, Google Analytics, etc.).
• It supports DirectQuery and real-time dashboards out of the box — ideal for IoT, operations, or monitoring use cases.
• Example: Live dashboards for call center performance or manufacturing KPIs.

📊 6. 

AI and Advanced Analytics

Advantage: Built-in AI and natural language capabilities

• Power BI integrates with Azure Machine Learning and Copilot for Power BI (AI assistant for report creation).
• Natural language querying (“Q&A” feature) lets users ask questions in plain English.
• Tableau requires external setup for similar AI functionality.
• Result: Easier access to predictive analytics and automated insights.

🧑‍💻 7. 

Developer and Enterprise Features

Advantage: Strong API, embedding, and governance options

• Power BI Embedded allows seamless integration of reports into custom applications or portals.
• Strong version control and deployment pipelines for large enterprises.
• Example: Developers can embed dashboards in internal or customer-facing apps using Power BI APIs with minimal effort.

🌍 8. 

Community and Continuous Updates

Advantage: Rapid monthly updates and large user base

• Microsoft releases monthly updates to Power BI Desktop and Service.
• Huge user community with free tutorials, templates, and DAX resources.
• Result: Faster feature growth and community-driven innovation.

Summary Table

Feature / Category

Power BI Advantage

Tableau

Microsoft integration

✅ Excellent

⚪ Moderate

Pricing

✅ Lower

❌ Higher

Ease of use

✅ Easier (Excel-like)

⚪ Steeper learning curve

Real-time analytics

✅ Native support

⚪ Add-ons required

AI and ML

✅ Integrated (Copilot, Azure ML)

⚪ Limited built-in

Cloud & governance

✅ AAD, Purview, Fabric

⚪ Manual setup

Updates & innovation

✅ Monthly

⚪ Quarterly

Enterprise scalability

⚪ Very strong (Premium)

✅ Also strong

Custom visualizations

⚪ Slightly fewer

🧠 In Short:

Choose Power BI if you want:

• Seamless integration with Microsoft products
• Lower total cost of ownership
• Easier user adoption and governance
• AI-assisted insights and rapid development

Choose Tableau if you need:

• More design flexibility and visual polish
• Cross-platform neutrality (non-Microsoft stack)
• Deeper control over visualization aesthetics

From Blogger iPhone client

Microsoft Power BI Best practice connecting to semantic data layer

Great question — this comes up often when teams are modernizing their Power BI semantic models and don’t want to rebuild reports every time the model changes. The “ideal way” depends on how often your model changes and how much stability you want to give report authors.

Here are some best-practice approaches:

🔑 1. Use a 

Shared Dataset / Semantic Model in Power BI Service

• Publish your model once as a Power BI dataset (semantic model) in the Service.
• Build reports off that shared dataset instead of embedding the model inside each PBIX.
• When the model is updated and republished, all reports connected to it automatically see the new version (assuming field names/measures stay consistent).
• This avoids the need to touch each report individually.

🔑 2. Use 

Thin Reports

• Keep reports “thin” (no imported data, only visuals + connections to the shared dataset).
• Users or developers only work on visuals; the model lives centrally.
• If the semantic model is updated (new measures, columns, relationships), reports immediately benefit.
• If a field is renamed/removed, only the visuals depending on it need fixing.

🔑 3. Manage Schema Changes with 

Stable Layering

• To reduce breakage:
• Maintain consistent field names and measure names across versions of the model.
• If you must change something, create calculated columns or measures that act as aliases for old names.
• Keep a semantic abstraction layer where changes in the source are hidden behind stable, user-facing measures.
• 
  

🔑 4. Use 

Deployment Pipelines

• If you manage Dev/Test/Prod, use deployment pipelines in Power BI Service.
• Push updated versions of the dataset through environments while validating before production.
• Reports remain linked and stable.

🔑 5. Consider 

Dataflows + Composite Models

• If source schema changes frequently, you can decouple transformation (dataflows) from the semantic model.
• The dataflow maintains schema stability, while the semantic model builds on top.
• Reports then depend only on the semantic model, not raw sources.

✅ Recommended Ideal Setup:

• Publish a centralized dataset (semantic model) in Power BI Service.
• Keep reports as thin clients.
• Use stable naming conventions in the model, and handle source changes in dataflows or a staging layer.
• Use deployment pipelines if you have multiple environments.

This way, when a new semantic model is published, users don’t need to update reports — only the dataset is swapped or versioned.

Do you want me to sketch a step-by-step migration flow (from report-embedded models → shared semantic model → version upgrades) so you can apply it in your environment?

From Blogger iPhone client

Types of Microsoft Power Bi Connections

Power BI has two main types of data connectivity: Import (copying data into Power BI) and DirectQuery (querying data directly from the source), along with Live Connection (to specific Power BI and Analysis Services datasets) and Composite Models, which combine these approaches. Choosing the right connection type depends on factors like data volume, the need for real-time data, data modeling capabilities, and report performance. 

Here's a breakdown of each connection type: 

1. Import Mode

• How it works: Data is copied and stored directly within the Power BI report, allowing for efficient data model creation and transformations using Power Query. 
• Pros: Fast query performance, comprehensive Power Query transformation capabilities, and full access to data modeling. 
• Cons: Requires scheduled refresh for data to be updated, can consume significant storage, and may not be suitable for very large datasets. 
• Best for: Most scenarios where data doesn't need to be completely real-time and a manageable amount of data is involved. 

2. DirectQuery Mode

• How it works: Power BI sends queries directly to the external data source to retrieve data in real-time. 
• Pros: Supports large datasets, provides near real-time data, and requires less storage in Power BI. 
• Cons: Performance depends on the source database, Power Query transformations are limited, and data modeling capabilities are restricted. 
• Best for: Situations requiring near real-time data or when dealing with massive datasets that cannot be imported. 

3. Live Connection Mode 

• How it works: Creates a live connection to a specific Power BI dataset or Analysis Services tabular model, without importing data into Power BI Desktop. 
• Pros: Leverages existing, complex models and DAX measures created in the source, and supports large data models. 
• Cons: No access to Power Query for data transformation, and report performance is dependent on the underlying Analysis Services model. 
• Best for: Connecting to established, robust data models in Power BI or Analysis Services, allowing for consistent data and logic across multiple reports. 

4. Composite Model

• How it works: A hybrid approach that allows you to combine data from different connection modes (Import, DirectQuery, and Live Connection) within a single data model. 
• Pros: Offers a flexible way to combine the benefits of different connection types. 
• Cons: Can introduce complexity and requires careful consideration of model design to ensure performance. 
• Best for: Scenarios where you need to integrate data from both real-time sources (DirectQuery) and static datasets (Import) in one model. 

5. DirectLake (Newer Mode) 

• How it works: An optimization for Azure Synapse Analytics and Fabric, it allows DirectQuery to read directly from the underlying data in the data lake, offering high performance with large volumes of data. 
• Pros: Improved performance for large datasets with near real-time data. 
• Cons: Limited to specific data sources and platforms. 
• Best for: Large-scale data warehousing and analytics scenarios, leveraging the data lake for speed.

From Blogger iPhone client

Microsoft Power BI Data Governance Strategy

Managing permissions and data governance in Microsoft Power BI cloud (Power BI Service) across multiple divisions and business areas (like Finance, Technical BI, Customer BI) requires a structured, scalable, and secure model that aligns with both organizational structure and data security policies.

Below is a step-by-step best practice approach, starting with the Finance department, and expanding to other departments.

🔐 Step 1: Establish Governance Framework

a. Define Roles and Responsibilities

• Data Owners (e.g., Treasury lead): approve access to datasets and reports.
• Data Stewards: maintain data quality, define business terms.
• Power BI Admins: manage workspace structure, monitor usage, enforce policies.
• Report Authors: develop dashboards and reports.
• Consumers/Business Users: view and interact with reports.

🏗️ Step 2: Design Workspace Strategy

Finance Department (Separate Workspaces per Business Area):

Create dedicated Power BI Workspaces for each business area:

• Finance – Treasury
• Finance – GBS
• Finance – Business Finance
• Finance – CIT
• Finance – FBI

Each workspace is:

• Owned by the department/data owners
• Has clear permissions (see below)
• Used for publishing datasets, reports, dashboards

Other Departments:

• Technical BI
• Customer BI
• Other shared or cross-departmental workspaces as needed

👥 Step 3: Set Up Role-Based Access Control (RBAC)

Use Power BI roles + Azure Active Directory (AAD) Security Groups:

• Create AAD groups for each access level (viewers, contributors, admins).
• Map groups to workspace roles:
• 
  
• Admin – for workspace owners
• Member – for key contributors
• Contributor – for report developers
• Viewer – for report consumers
• 
  

Example (Finance – Treasury):

• Finance-Treasury-Admins
• Finance-Treasury-Contributors
• Finance-Treasury-Viewers

Benefits:

• Centralized permission management
• Easy onboarding/offboarding of users

📊 Step 4: Centralize and Govern Data Sources

Use 

Shared Datasets

:

• Create certified or promoted datasets in centralized workspaces (e.g., Finance – Data Models)
• Enable reusability across business areas and reports

Configure Data Source Credentials:

• Use Gateway Connections for on-premises sources
• Use Managed Identity or Service Principal where possible

Define Access at Source Level:

• Use Row-Level Security (RLS) in the dataset to restrict data based on user identity
• Example: RLS for business unit → only GBS users can see GBS financials

📱 Step 5: Use Apps for Distribution

Package curated content into Power BI Apps for each audience:

• Finance – Treasury App
• Finance – CIT App
• Customer BI – Insights App

Apps allow:

• Clean, user-friendly interface
• Controlled distribution
• Audience-specific access without workspace access

🛡️ Step 6: Implement Data Governance Policies

a. Use Endorsement (Certified / Promoted Datasets)

• Mark trusted datasets as certified or promoted
• Restrict who can publish certified content

b. Lineage and Impact Analysis

• Use Power BI Lineage View to track dependencies
• Before updating a dataset, see which reports are impacted

c. Data Sensitivity Labels

• Apply Microsoft Purview (formerly Information Protection) sensitivity labels
• Enforce compliance and visibility across Microsoft 365

🔍 Step 7: Monitor & Audit

Use Power BI Admin Portal & Audit Logs:

• Audit access, sharing, and usage
• Set up usage metrics reports
• Monitor for unauthorized sharing, or stale reports

Use Power BI Activity Logs (via Microsoft 365 compliance center) for detailed tracking.

🔁 Step 8: Iterate, Scale, and Expand

• Extend the above approach to Technical BI, Customer BI, and other departments
• Create Data Domain Councils for cross-department governance
• Run quarterly reviews of permissions, stale workspaces, and compliance

🚦Sample Finance Permission Table:

Workspace

AAD Admin Group

Contributor Group

Viewer Group

Finance – Treasury

Finance-Treasury-Admins

Finance-Treasury-Contributors

Finance-Treasury-Viewers

Finance – GBS

Finance-GBS-Admins

Finance-GBS-Contributors

Finance-GBS-Viewers

Finance – Data Models

Finance-Models-Admins

Finance-Models-DataStewards

– Certified Dataset Users –

From Blogger iPhone client

Create import connection Microsoft power bi to big Query

you can create an Import query with a GCP BigQuery connection in Power BI, but there are important limitations and considerations depending on the Power BI version and data connector used.

✅ 

Import Mode with BigQuery – Supported

Power BI supports Import mode from Google BigQuery using the built-in connector, as long as:

• You’re using Power BI Desktop
• You have credentials (usually Google account OAuth2 or service account)
• The dataset isn’t too large, since Import loads data into Power BI’s in-memory model

🔁 

Steps to Use Import Mode with BigQuery

1. Open Power BI Desktop
2. Go to Home > Get Data > More…
3. Choose Google BigQuery (under Database category)
4. Authenticate (with Google account or service account key)
5. Navigate through your GCP project > Dataset > Tables
6. In the navigator:
7. 
   
8. Select tables or views
9. At the bottom, choose Load (this is Import mode) or Transform Data to go through Power Query
10. 
    
11. Power BI will load the data into memory and store it in the .pbix file

🔍 Difference Between Import and DirectQuery

Feature

Import

DirectQuery

Performance

Fast for analysis (in-memory)

Slower – queries sent to BigQuery live

File Size

Limited by RAM (Power BI file grows)

Lightweight – no large data stored

Refresh

Needs scheduled refresh (via Gateway)

Live data, but limited transformations

Transformations

Full Power Query and DAX available

Limited – many transformations restricted

⚠️ Considerations and Limitations

• Data size: BigQuery tables can be huge; import only what you need.
• Cost: Importing large data can incur BigQuery query costs (pay-per-query).
• Gateway requirement: Scheduled refresh in Power BI Service requires on-premises data gateway, even for cloud sources like BigQuery (unless using personal gateway or VNet).
• Query Folding: Power Query may or may not fold your transformations back to BigQuery — if not, performance and cost may suffer.

💡 Tip: Reduce Query Cost and Improve Performance

• Use Custom SQL or BigQuery views to pre-aggregate/filter data before loading
• Use incremental refresh (for large tables with date-based partitions)
• Keep imports small or use DirectQuery/Hybrid mode when live data is essential

From Blogger iPhone client

Microsoft Power BI - Semantic Models

The semantic model in Power BI (also called the tabular model or data model) is designed primarily for data analysis and consumption, not for complex data transformation. Here’s why it has limitations on data transformation:

🔹 1. 

Performance and Optimization Focus

• Semantic models are optimized for fast querying, aggregations, and visualizations.
• Allowing heavy transformations at query time would slow down performance, defeating the purpose of a semantic model.

🔹 2. 

Design Separation: Transform vs Model

Power BI follows a separation of concerns:

• Power Query (M) handles data transformation and shaping (ETL).
• Semantic model (DAX) handles calculated columns, measures, relationships, and business logic.

This ensures:

• Cleaner models
• Reusability
• Efficient refresh and query performance

🔹 3. 

DAX Is Not Meant for ETL

• DAX (used in the semantic model) is designed for calculated logic on already loaded data, not for:
• Complex joins
• Row-level transformations
• Column reshaping or unpivoting
• 
  
• These tasks are meant to be done in Power Query or upstream ETL tools (like Alteryx, SQL, etc.).

🔹 4. 

Storage Engine Constraints

• The semantic model uses VertiPaq, an in-memory columnar storage engine.
• VertiPaq is efficient only if data is clean and structured. Transformations can:
• Increase model size
• Reduce compression efficiency
• Slow down queries
• 
  

🔹 5. 

Governance and Maintainability

• If complex transformations are done within the semantic model:
• It becomes harder to audit, manage, or debug.
• Data lineage becomes less transparent.
• Data governance is weakened.
• 
  

✅ What the Semantic Model 

Should Do

• Define relationships
• Add business logic (measures, KPIs, hierarchies)
• Handle role-level security
• Enable efficient slicing and dicing of clean, transformed data

🧠 Best Practice

Perform all heavy transformations in Power Query or a data warehouse. Use the semantic model only for modeling and business logic.

From Blogger iPhone client

Tableau to Power BI migration

Migrating from Tableau to Power BI is a bit complex due to the differences in their underlying architectures, APIs, and file formats. While there’s no official one-click migration tool from Tableau to Power BI, you can automate parts of the process using Python, REST APIs, and third-party tools.

Here’s a breakdown of what you can do and how to approach building a migration script using Python:

Step 1: Extract Metadata from Tableau

Use the Tableau REST API and Metadata API (GraphQL) to extract workbooks, datasources, and visualization metadata.

Python Example (Tableau Metadata API):

Use Python libraries like gql or requests to send this GraphQL query.

Step 2: Parse and Convert the Metadata

Translate the Tableau metadata into something Power BI understands. This requires custom mapping logic:

• Tableau Viz → Power BI Visual
• Tableau Filters → Power BI Slicers
• Tableau Data Sources → Power BI Dataflows or imported datasets

You can write this logic using pandas or custom JSON translators.

Step 3: Use Power BI REST API to Create Equivalent Artifacts

Power BI’s REST API supports operations like:

• Creating workspaces
• Uploading PBIX files
• Updating datasets
• Managing reports

However, you cannot programmatically create detailed visuals via REST API alone — that requires Power BI Desktop and the PBIX format.

But you can prepare data and models using:

• Power BI XMLA endpoint
• Tabular Editor (for datasets)
• Power BI Desktop Automation using PowerShell/Python & PBIX Templates

Alternative/Third-party Tooling

Some tools that can help in this migration:

• Power BI XMLA/Tabular Editor: To create models programmatically.
• Tableau to Power BI Migration Tool by MAQ Software (limited features).
• ZappySys ODBC Drivers / ETL tools to extract Tableau data and push to Power BI.
• Alteryx or KNIME: As middle-layer ETL tools.

Caution

• Visuals cannot be directly migrated — you’ll need to recreate them.
• Some calculations (e.g., LOD in Tableau) must be translated manually into DAX.
• Tableau dashboards (layouts, interactivity) won’t be 1:1 with Power BI.

Want a Starter Script?

If you’re interested, I can generate a Python starter script that:

• Authenticates with Tableau
• Extracts workbook metadata
• Prepares a mapping JSON for Power BI

Let me know how automated you want it (full flow vs metadata only).

From Blogger iPhone client

MAQ Software offers a Tableau to Power BI migration tool called MigrateFAST, which aims to simplify and accelerate the process of transitioning from Tableau to Power BI. This tool, powered by AI, is designed to help businesses migrate large volumes of reports, potentially saving time and resources. 

Key Features and Benefits: 

• AI-Powered Migration:
• MigrateFAST utilizes artificial intelligence to automate and expedite the migration process. 
• Large-Scale Migration:
• The tool is designed to handle large-scale migrations of reports from Tableau to Power BI. 
• Time and Cost Savings:
• By automating the migration, MigrateFAST can reduce the time and resources required, potentially leading to cost savings. 
• Optimized Conversion:
• The tool focuses on ensuring high-quality and accurate report conversion during the migration process. 
• Data Discovery and Exploration:
• MigrateFAST can help streamline the data discovery and exploration process, allowing for more intuitive and accessible data insights. 
• 6-Week Implementation:
• MAQ Software claims to offer a 6-week implementation plan for the migration process. 
• Expert Support:
• MAQ Software provides expert support and guidance throughout the migration process. 
• Other Services:
• MAQ Software also offers services like performance analysis, report optimization, and adoption training to support the overall migration and adoption of Power BI. 

How MigrateFAST Works: 

MigrateFAST, according to MAQ Software, uses AI to analyze and convert Tableau workbooks (.twbx) to Power BI, simplifying the migration process. It can automate tasks, reduce manual effort, and improve the accuracy of the conversion, ultimately leading to a smoother transition to Power BI. 

For a more detailed understanding of MAQ Software's services and the MigrateFAST tool, it is recommended to visit their website

Market Share Tableau vs Microsoft Power BI vs Qlik - 2024

 As of 2024, Power BI, Tableau, and Qlik remain the dominant players in the business intelligence (BI) and data visualization market, each excelling in different areas.

Market Share and Popularity:

• Power BI by Microsoft continues to lead the market, largely due to its integration with the Microsoft ecosystem and its cost-effectiveness, especially with pricing as low as $10 per user per month for its Pro version. Power BI has a strong community, thanks to Microsoft’s vast developer network and support​(
  Learn R, Python & Data Science Online
  )​(
  DataScientest
  ).
• Tableau, acquired by Salesforce, is a significant player, particularly known for its advanced visualizations and geospatial capabilities. However, it tends to be more expensive, with costs around $70 per user per month. Tableau is favored in environments requiring sophisticated visual storytelling​(
  Learn R, Python & Data Science Online
  )​(
  SelectHub
  ).
• Qlik Sense offers strong data preparation and direct query capabilities, making it competitive in advanced analytics and enterprise-level deployments. While not as dominant in market share as Power BI or Tableau, Qlik stands out for flexibility, particularly in cloud and hybrid environments​(
  SelectHub
  )​(
  DataScientest
  ).

Strengths:

• Power BI: Cost-effective, seamless integration with Microsoft products, strong AI/ML features, and excellent for small to medium-sized enterprises.
• Tableau: Best-in-class for geospatial visualizations and detailed storytelling through data.
• Qlik: Strong in enterprise applications, data preparation, and hybrid cloud environments.

In summary, Power BI leads in market share, followed closely by Tableau, with Qlik making inroads in specific enterprise scenarios.

Microsoft Power BI

Microsoft Power BI is a business intelligence (BI) suite that helps you analyze data and share insights. It provides a variety of tools for data visualization, reporting, and dashboarding. Power BI can be used to connect to a variety of data sources, including cloud-based data warehouses, on-premises databases, and spreadsheets.

Power BI is a popular BI tool among businesses of all sizes. It is used by businesses to make better decisions, improve operations, and communicate insights to stakeholders.

Here are some of the features of Power BI:

• Data connectivity: Power BI can connect to a variety of data sources, including cloud-based data warehouses, on-premises databases, and spreadsheets.
• Data visualization: Power BI provides a variety of tools for data visualization, including charts, graphs, and maps. These visualizations can be used to explore data, identify trends, and communicate insights.
• Reporting: Power BI can be used to create reports that summarize data and present it in a clear and concise way. Reports can be shared with stakeholders to keep them informed of the latest data.
• Dashboards: Power BI can be used to create dashboards that display key metrics and insights. Dashboards can be customized to meet the specific needs of the user.
• Collaboration: Power BI allows users to collaborate on data visualizations and reports. This can be done by sharing dashboards or by working on the same visualization together.
• Extensibility: Power BI is extensible with a variety of add-ons and connectors. This allows users to customize Power BI to meet their specific needs.

Power BI is a powerful BI tool that can be used to make better decisions, improve operations, and communicate insights to stakeholders. If you are looking for a BI tool, Power BI is a good option to consider.

Here are some of the benefits of using Power BI:

• Ease of use: Power BI is a user-friendly BI tool that can be used by people with no prior experience in data visualization.
• Powerful features: Power BI offers a wide range of features for data visualization, reporting, and dashboarding.
• Scalability: Power BI can be used to handle large datasets and complex visualizations.
• Cost-effectiveness: Power BI is a cost-effective BI tool that is available in a variety of pricing plans.

If you are considering using Power BI, I recommend that you do the following:

• Try the free trial: Power BI offers a free trial that you can use to test the software.
• Read the documentation: Power BI provides comprehensive documentation that you can use to learn how to use the software.
• Take a training course: Power BI offers a variety of training courses that you can take to learn how to use the software.
• Join the community: Power BI has a large and active community of users who can help you with questions and problems.

Microsoft Power BI vs Tableau

Tableau and Microsoft Power BI are both business intelligence (BI) tools that allow you to visualize and analyze data. They are both powerful tools, but they have different strengths and weaknesses.

Tableau is known for its ease of use and its ability to create interactive dashboards and visualizations. It is a good choice for organizations that need to make data-driven decisions quickly and easily. Tableau is also a good choice for organizations that need to share data with non-technical users.

Microsoft Power BI is known for its scalability and its ability to connect to a variety of data sources. It is a good choice for organizations that have a lot of data and that need to be able to analyze it in different ways. Power BI is also a good choice for organizations that are using other Microsoft products, such as Excel and SQL Server.

Here is a table that summarizes the key differences between Tableau and Power BI:

Feature	Tableau	Microsoft Power BI
Ease of use	Easy to use	More complex to learn
Interactive dashboards	Yes	Yes
Data visualization	Powerful	Powerful
Scalability	Less scalable	More scalable
Data sources	Supports a variety of data sources	Supports a wider variety of data sources
Integration with other Microsoft products	No	Yes
Cost	Starts at $70 per user per month	Starts at $9.99 per user per month

Ultimately, the best BI tool for you will depend on your specific needs and requirements. If you are looking for an easy-to-use tool that can be used to create interactive dashboards and visualizations, Tableau is a good choice. If you are looking for a scalable tool that can connect to a variety of data sources, Power BI is a good choice.

Creating a PowerBI report with DirectQuery and multiple SQL Database sources using Elastic Query

This post explains how to create a PowerBI report which sources data from two separate Azure SQL Databases.

PowerBI offers two data access mechanisms; Import and DirectQuery.

DirectQuery provides a range of benefits, the chief of which is that data is automatically refreshed. DirectQuery doesn’t import any data into PowerBI, instead it queries the data sources as you interact with visualisations, making it the best option for larger datasets.

However, it is only possible to connect to one SQL Database as a data source when you use DirectQuery. If you try to add a second database as a data source, you’ll be notified that it would be necessary to switch to Import mode to continue. This is the case even if both databases are on the same server.

Azure SQL Database Elastic Query provides a way round this problem. It allows you to run T-SQL queries across multiple databases, though adding an external table to one of the databases, which draws its data from a table in the other database. By setting up an external table in a database, you can create a PowerBI report which uses DirectQuery, but can indirectly access data from another database through it.

Following the Azure Elastic Query docs, imagine that we want to create a PowerBI report, using DirectQuery, which primarily draws from an Orders database, but which also uses data from a Customers database. Without Elastic Query, you can only get data from the Orders database.

We’ll set up an external table so that Customer information can be added to the report as well, without having to switch to Import mode.

If you want to follow along end to end, the first step is to create two Azure SQL Server Databases through the Azure portal. They can either be on the same or different servers.

Add a firewall rule to the server, so you can access it from your machine. Then using a tool such as SQL Management Studio, add an OrderInformation table to the Orders database, and a CustomerInformation table to the Customers database (there are scripts for this in the Azure Elastic Query docs).

Once the databases and tables are set up, the steps to set up an external table in the Orders database are as follows:

1. Create a master key and scoped credential in the Orders database, using the credentials for the Customers database.

	CREATE MASTER KEY ENCRYPTION BY PASSWORD = '';
	CREATE DATABASE SCOPED CREDENTIAL ElasticDBQueryCred
	WITH IDENTITY = '',
	SECRET = '';

view rawCreate master key and scoped credential.sql hosted with  by GitHub

2. Create an external data source using the credential created above

	CREATE EXTERNAL DATA SOURCE MyElasticDBQueryDataSrc WITH
	(TYPE = RDBMS,
	LOCATION = <server location>,
	DATABASE_NAME = 'Customers',
	CREDENTIAL = ElasticDBQueryCred,
	);

view rawCreate external data source.sql hosted with  by GitHub

Location should be the full server location e.g. ‘MyServer.database.windows.net’.

3. Create an external table in the Orders database, which has the same schema as the CustomerInformation table in the Customers database.

	CREATE EXTERNAL TABLE [dbo].[CustomerInformation]
	( [CustomerID] [int] NOT NULL,
	[CustomerName] [varchar](50) NOT NULL,
	[Company] [varchar](50) NOT NULL)
	WITH
	( DATA_SOURCE = MyElasticDBQueryDataSrc)

view rawCreate external table.sql hosted with  by GitHub

If a table with the same name existed already in the Orders database, you’d have to give it a different name..

These steps let you access the data in the CustomerInformation table in the Customers database as if it were a table in the Orders database.

Obviously you can go on and add other external tables if you want more data from the other database.

Please note that to view the external table in SQL Server Management Studio, you will need version 2016 or above.

The external CustomerInformation table will now be available in your PowerBI report.

To add it, edit the report and add a new query – select the same server, and choose the external table, which will be displayed in the same way as a normal table.

You can then create a relationship between the two tables in PowerBI.

This gives you the option to show customer names rather than IDs in visualisations, and produce a nicer report.

P.s. if you followed along from the start, remember to delete the new SQL Databases and Server you created!

This is just scratching the surface of Elastic Query, which can also be used to push SQL parameters to remote databases, execute remote stored procedures or call remote functions, and refer to remote tables with a different schema.

Resources

PowerBI DirectQuery
https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-use-directquery/

Azure SQL Database Elastic Database Query overview
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview

Get Started with cross-database queries (vertical partitioning)
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-getting-started-vertical