November 15, 2023

Embracing the Future of Data Management with Microsoft Fabric: A Setup Guide

As data becomes larger and more complex, organizations are turning to medallion architecture for efficient data management. This method combines data processing, storage, reporting, and machine learning into one system, using a single data lake to integrate various data sources. Commonly, this is done using Azure Data Factory, Azure Data Lake Storage Gen2, Databricks, Azure Synapse, and Power BI.

Microsoft Fabric offers a new, comprehensive solution for businesses. It's a SaaS service that separates computing and storage, improving the medallion architecture with Data Fabric and Data Mesh patterns.

Advantages of Fabric

Direct store delivery (DSD) is a distribution method that allows manufacturers to deliver directly to stores, bypassing wholesalers entirely. While it offers better control over inventory and pricing, it also introduces challenges such as managing routes, drivers, vehicles, and orders in real time. To address these DSD challenges, the retailer needed a solution that could:

1. Streamlined operations and management: As a unified platform, Microsoft Fabric simplifies the handling of compute resources and storage.

2. Consolidated data source: Organizations often scatter data across multiple locations. Microsoft Fabric, with OneLake as the central storage and the 'shortcut' feature (allowing references to delta tables in external storages like ADLS Gen2/S3), ensures a unified data reference point.

3. Seamless system integration: It easily integrates with existing data storage systems, whether they're in Azure Data Lake Storage, Amazon S3, or SQL Server.

4. Simplified security: OneSecurity offers a simplified security and governance model.  

Adoption strategy

Microsoft Fabric, with its benefits, seems like the natural evolution of the medallion architecture. However, as Microsoft is just making Microsoft Fabric generally available, there are still features that need to be implemented to achieve feature parity with the existing solutions. Shifting to it immediately is a large task. We recommend a three-phase adoption:

1. Use Fabric in the Platinum layer for reporting.
2. Decouple the Gold and Platinum layers.
3. Transform your data analytics landscape.

Phase 1: Use Fabric in the Platinum layer for reporting

Power BI Premium users can easily upgrade to Microsoft Fabric, as it's included in the Premium SKU. The cost of Fabric SKU (F 64) may seem higher compared to its equivalent Power BI Premium P1 (at $4,995 per capacity/month). This is because Microsoft Fabric is available as a pay-as-you-go model, offering flexibility to adjust capacity based on demand. Reserved instance pricing for Fabric, which is expected to be a lower-cost option, has not been announced.

Figure 1: Microsoft Fabric pricing

In terms of usage, the reporting experience in Fabric is similar to Power BI, meaning existing features will work seamlessly. The first step is to set up a Lakehouse in Fabric, using shortcuts to data in the ADLS layer for Power BI reports.

Changes in Fabric compared to your existing Power BI setup

1. Tracking at CU level: Billing and tracking in Fabric are based on Computing Units (CUs). You can see all this information in the updated Capacity Metrics App, which displays all associated workspaces.

2. Improved workload management: Fabric offers elevated workload management. It allows your tasks to perform optimally by temporarily using more capacity when needed and smoothing out the workload distribution. This approach helps in planning based on average workload needs.

3. Capacity usage and autoscaling: You can use up to 10 minutes of future capacity without affecting operations. Additionally, there's an autoscale feature that automatically increases capacity during high-demand periods. The table below outlines this in more detail.

Future smoothed consumption

Platform policy Impact
 Usage ≤ 10 minutes  Overage protection    Jobs can consume 10 minutes of future capacity use without throttling.    
 10 minutes < Usage ≤ 60 minutes      Interactive delay      User-requested interactive-type jobs will be throttled.
 60 minutes < Usage ≤ 24 hours  Interactive rejection    User-requested interactive-type jobs will be rejected.
 Usage > 24 hours  Background rejection    User-scheduled background jobs will be rejected from execution.

Phase 2: Decouple the Gold and Platinum layer

The next step is to separate the Platinum and Gold layers. This means adding team members with development skills to the reporting team. This will give them full control over the Platinum/semantic layer and reduce dependency on the engineering team for data formatting.

In this setup, Fabric is primarily used for reporting and the semantic layer.

Figure 2: Architecture flow diagram

Direct Lake reports

Direct Lake is a new dataset type in Microsoft Fabric for Power BI reports, adding to the existing DirectQuery and Import models. It combines the best of both: the efficiency of Import mode and the ability to handle large data like DirectQuery. Direct Lake datasets access data in OneLake directly, eliminating the need for converting to other query languages and bypassing the need for manual dataset refreshes. This makes it perfect for querying large datasets that are frequently updated.

For optimal performance, it's recommended to write data to OneLake with VORDER enabled. This method, different from ZORDER, streamlines querying in OneLake by removing the need for language translation. Since there is no need to explicitly refresh the dataset, Direct Lake mode is ideal for handling large, regularly updated datasets. 

spark.conf.set("spark.sql.parquet.vorder.enabled", "true")


Figure 3: Saving delta tables with VORDER enabled

When using Direct Lake datasets in Microsoft Fabric, keep these points in mind:
1. Be aware of the limitations of Direct Lake.
2. Sometimes, Direct Lake datasets might switch to DirectQuery mode, resulting in slower performance.

Granting engineering teams access to Fabric resources in a development setting will help them become familiar with its data integration, engineering, and real-time capabilities.

Phase 3: Transforming your data analytics landscape

The next step is to move your current processing and ingestion layers to Microsoft Fabric. However, we suggest waiting for this step until Fabric items like APIs, security, governance, CI/CD / git integration reach maturity. 

When migrating, create separate workspaces for the bronze, silver, and gold layers. If needed, divide the silver layer further into workspaces for different areas like Sales, HR, Finance, etc. Use OneSecurity for table and column level security to ensure access restrictions are maintained across all resources using the data.

Figure 4: Architecture diagram

Fabric setup

Start by forming a gold layer project team to create a strategy that fits your organization. Be aware of some limitations in Microsoft Fabric, like invoking pipelines across workspaces or defining identity columns, which are common in existing systems. While a one-size-fits-all guideline is challenging, the following recommendations can help structure your Fabric workloads with the current features. We will be revisiting these guidelines as new features become available.

1. Create 3 capacities: Allocate separate capacities for development + testing, pre-production, and production. This helps in testing production-level workloads in a controlled environment.

2. Organize workspaces for data categories: Establish common bronze and silver workspaces for each data type. For large enterprises, consider separating bronze and silver from gold workloads to prevent resource throttling issues in the gold layer.

3. Manage access:

a. Assign Pro licenses to developers and add them to the Contributor role in Azure Active Directory (AAD) groups.

b. Grant access to bronze and silver workspaces for the core team, and gold workspace access to gold project teams.

c. Add workspace admin groups to the admin role.

d. Create AAD groups per subject area, assigning gold development teams accordingly for access to relevant bronze and silver objects. Implement object/row/column level security using SQL server endpoints.

4. Implement version control: Set up an Azure repository for versioning Fabric solutions.

5. Establish deployment pipelines: Use these for promoting solutions to higher environments.

6. Use capacity metrics app: Install this app for admins and share it with development groups for visibility into capacity usage and workload impact.

7. Create domains for data mesh: Group project workspaces into domains like sales, marketing, etc., and manage access within these domains. Assign domain admins and contributors and allocate workspaces to each domain.

Figure 5: Creating a new domain 

8. Access for gold layer users: Each project team should handle user access through a workspace, using an Azure Active Directory (AAD) group with read-only permissions. This access covers both SQL and Power BI reports. For SQL endpoints, manage permissions with SQL GRANT and DENY commands.

9. Integrate Purview for protection: Set up Microsoft Purview integration in Fabric for information protection and data loss prevention. Set protection labels in the Purview portal and choose between mandatory or programmatic labeling for Power BI reports. Data loss prevention policies, currently applicable only to datasets, should be defined in the Microsoft Purview Compliance portal.

10. Use Purview Hub Report: This report, available to capacity admins, provides an overview of all items across workspaces, including resource promotions/certifications and sensitivity labels. Share it with the data stewards group to ensure adherence to organizational processes.

In summary

Microsoft Fabric excelled beyond addressing the retailer's core challenges by delivering transformative benefits to their Direct Store Delivery operations. By facilitating real-time data ingestion and processing, Fabric provided a unified, instantaneous view of all DSD activities, allowing for quick and strategic decision-making. 

The implementation also enabled real-time analytics and reporting capabilities, with Power BI integration ensuring that insights were both accessible and actionable. Fabric's scalable infrastructure laid the groundwork for predictive analytic applications, equipping the retailer with the tools to anticipate market trends and optimize their supply chain. On top of all this, costs were also optimized.

The result was a great shift in how the retailer operated, manifesting in increased efficiency, reduced costs, and a strengthened connection with customers.

Want to learn more?

When it comes to implementing Microsoft Fabric, you need a partner that you can trust to deliver the results you need. As a Fabric Featured Partner, our certified team has the deep expertise and experience you need to design, deploy, and manage a successful Microsoft Fabric environment. We offer a comprehensive suite of services, from planning and design to deployment and support, to help you get the most out of your investment in Microsoft Fabric. 

Contact to learn more about how MAQ Software can help you achieve your business goals with Microsoft Fabric. Explore our Fabric services and Marketplace offerings today.

November 14, 2023

Microsoft Fabric: Powering Real-Time Analytics for Retailers

In the fast-paced retail industry, being able to quickly adapt to evolving supply chain scenarios is essential to success. This requires accurate and up-to-date tracking of inventory, order, and delivery data from varied sources. A leading consumer packaged goods (CPG) retailer recognized this and embarked on their digital transformation journey with a goal: enabling real-time operational insights.

The challenges

Direct store delivery (DSD) is a distribution method that allows manufacturers to deliver directly to stores, bypassing wholesalers entirely. While it offers better control over inventory and pricing, it also introduces challenges such as managing routes, drivers, vehicles, and orders in real time. To address these DSD challenges, the retailer needed a solution that could:

1. Ingest and process high volumes of data in real time. 
2. Enable real-time analytics and reporting. 
3. Provide a unified view of all DSD operations.
4. Support data-driven, prescriptive analytics.
5. Optimize platform costs.

Figure 1: Overview of the DSD process for CPGs

Enabling real-time insights with Fabric

While the retailer’s existing infrastructure and setup laid the foundation, integrating Microsoft Fabric into the solution was the pivotal element in advancing their supply chain analytics. As an all-in-one analytics solution and easy-to-use SaaS product, designed to streamline your analytics processes and eliminating the need to combine services from various vendors.

Figure 2: Solution architecture diagram

Diving deeper into the solution

With Fabric, a solution was developed and implemented into their infrastructure to revolutionize their data analytics through a streamlined process:

Data ingestion & processing: 
1. Uses event streaming and data pipelines for ingesting real-time and micro-batch data into the KQL database. This ensures instantaneous data access for quick decision-making. 
2. Employs direct connections to Azure Event Hubs and Kafka for optimized real-time data ingestion with minimal delay.
3. Implements micro-batch processing for scheduled retrieval of incremental data when real-time streaming is not feasible.

Data storage & reliability:
1. Mirrors data to OneLake, ensuring secure and reliable data storage.
2. Integrates with Azure Datalake Gen2 Storage, enriching the data mart with new and existing datasets.
3. Facilitates advanced analytics through data transformations.
4. Supports large-scale batch processing within the unified data repository.

Advanced analytics & visualization: 
1. Offers real-time data visualization through Power BI reports with features like auto page refresh and admin controls.
2. Uses Fabric's analytical platform for robust data analysis.
3. Introduces Direct Lake mode in Power BI for faster data handling and improved query speed.
4. Demonstrates increased efficiency over traditional import and direct query reporting methods.

Operational efficiency & insights:
1. Combines Fabric's eventstreams with Lakehouse analytics for a solid base in metric monitoring and advanced data analysis.
2. Improves the ability to explore market patterns and consumer behavior.
3. Empower retailers to optimize operations and marketing strategies through data-driven insights.

Figure 3: The Power BI dashboard, using real-time data

Fabric benefits and beyond

Fabric presents a transformative approach to achieving real-time insights and comprehensive analytics, all within one product. The retailer's new Fabric-based solution delivered several key benefits, including:  

1. Real-time insights: The ability to access and analyze high-volume data in real time enabled the retailer to make faster and more informed decisions about their DSD operations. Analyzing supply chain data now enables the retailer to identify bottlenecks and optimize operations. This analysis allows them to identify and resolve potential issues before they impact customers.  

2. Improved efficiency: The retailer was able to improve the efficiency of their DSD operations by using Fabric to optimize routes and reduce delivery times.  

3. Comprehensive analytics: Fabric's unified data lake and advanced analytics capabilities provided the retailer with a deeper understanding of their DSD customers and products.  Now, analyzing customer purchase patterns allows the retailer to identify trends and develop targeted marketing campaigns. 

4. Reduced costs: Costs could be reduced by using Fabric to identify and eliminate waste in their DSD operations.  

Beyond the initial goal, Fabric also enabled the retailer to achieve even more:

1. Effortless data integration: Fabric's shortcuts facilitate seamless onboarding of data points from Azure Datalake Gen 2 storage. This integration simplifies the inclusion of existing data lakes, thereby eliminating redundant data duplication.

2. Adaptive load handling: Fabric's bursting and smoothing capabilities ensure the system remains resilient during intense activity bursts. By only paying for the average load, instead of the peak, cost is optimized.

3. Simplified management: Instead of juggling multiple resources like Azure Data Factory, KQL database, and Power BI capacity, users can solely focus on managing Fabric. This streamlined approach cuts down operational complexities and overheads.

4. A launchpad for AI: Looking into the future, Fabric's robust data science tools combined with data in OneLake positions retailers to enable prescriptive analytic models. These models aim to revolutionize product recommendations, substantially aiding field agents during store visits.

In summary

Microsoft Fabric excelled beyond addressing the retailer's core challenges by delivering transformative benefits to their Direct Store Delivery operations. By facilitating real-time data ingestion and processing, Fabric provided a unified, instantaneous view of all DSD activities, allowing for quick and strategic decision-making. 

The implementation also enabled real-time analytics and reporting capabilities, with Power BI integration ensuring that insights were both accessible and actionable. Fabric's scalable infrastructure laid the groundwork for predictive analytic applications, equipping the retailer with the tools to anticipate market trends and optimize their supply chain. On top of all this, costs were also optimized.

The result was a great shift in how the retailer operated, manifesting in increased efficiency, reduced costs, and a strengthened connection with customers.

Want to learn more?

When it comes to implementing Microsoft Fabric, you need a partner that you can trust to deliver the results you need. As a Fabric Featured Partner, our certified team has the deep expertise and experience you need to design, deploy, and manage a successful Microsoft Fabric environment. We offer a comprehensive suite of services, from planning and design to deployment and support, to help you get the most out of your investment in Microsoft Fabric. 

Contact to learn more about how MAQ Software can help you achieve your business goals with Microsoft Fabric. Explore our Fabric services and Marketplace offerings today.

September 22, 2023

Harnessing real-time data insights with a versatile bot application


The need for rapid, accurate, and comprehensive insights

Our client, an influential American manufacturing giant, faced the challenge of effectively using their massive volume of data from multiple sources. To achieve consistent decision-making and streamlined operations, the client needed a versatile bot application.

The solution required would seamlessly link to a range of data sources, enable quick and pinpoint insights, and provide references from their rich knowledge base. Providing real-time insights was another key feature the solution had to provide.

The ask

•  Effortless integration: Connect seamlessly to various data sources.
Rapid insight extraction: Provide users with real-time, accurate data insights.
Knowledge base citations: Validate insights with trusted knowledge-base references.

Addressing the challenges

In the pursuit of an efficient system, we created a comprehensive Knowledge Bot to meet the client's requirements. The highlights of our approach included:

      •   User authentication
Seamlessly verifies user identities through Managed Identity or Custom Login, ensuring secure access control based on roles. This feature ensures that only authorized individuals interact with the system, improving data security.
      •   Azure-powered backend
Using the Azure platform with Flask framework, we developed a bot   that easily integrates with diverse data sources. This integration means that the Bot has a cohesive system capable of handling data from multiple origins efficiently.
      •   Interactive chat experience
With a focus on user engagement, our solution provides an interactive chat interface. This feature simplifies the process of extracting real-time insights from complex data sources and increases user interaction. This integration means that the Bot has a cohesive system capable of handling data from multiple origins efficiently.
      •   Customized responses
Using Azure OpenAI, the Bot offers personalized prompts. This customization ensures that the Bot can respond to queries effectively for more precise insights and better decision-making.
      •   Citations for validation
For every insight provided, our Bot integrates citations referencing sources from their knowledge base. This feature adds a layer of credibility and aligns with the client's need for validated insights.

More solution benefits

Beyond the core features, the Bot had additional benefits and features:

      •   Integration with existing systems
Adding the power of our Bot plugin elevates the data processing and insight generation of existing technology investments.
      •   Lightweight and cross-environment support
Our system is designed to be lightweight and versatile, making it easy to deploy across various environments. This feature means that the bot can be seamlessly integrated into existing infrastructures without causing unnecessary disruptions.
      •   Customizable scopes
This ensures that data from different scopes remains separate, eliminating the risk of data contamination and ensuring data integrity.
      •   Instant response generation
The Bot's ability to quickly retrieve answers helps users obtain the information as they need it.
      •   Simple and easy to use
The simple and easy-to-use Bot enables users to quickly adapt to and benefit from the system. This ease reduces the learning curve and improves overall usability. 
      •   Multimedia support
The Bot can process various dynamic file types, including videos.

A look into the solution flow

Our solution harnesses the combined capabilities of Azure Blob Storage, Azure SQL Database, and Azure OpenAI. Not only does it fulfill the client's need for instantaneous, precise insights, but it also addresses issues concerning data sourcing, extraction, and validation.

      •   Data Source stage
Azure Blob Storage and Azure SQL Database serves as our primary data repositories.
      •   Data Processing stage
Documents and images undergo processing using Azure Form Recognizer and Azure Computer Vision within a Function App. This data, once converted into JSON format, is stored in Azure Blob Storage.
      •   User interaction
Users can direct questions or keywords to the Bot. This then taps into Azure Cognitive Search which is indexed to retrieve data from Azure Blob Storage.
      •   Answer generation
External users receive responses via an Azure App Service running a Flask application. This app, communicating with Azure OpenAI, ensures the delivery of precise and contextually relevant answers.
      •   Security
Security remains paramount, with access to the OpenAI model governed by the Azure Key Vault, ensuring a high standard of data protection and governance.

Figure 3: Solution flow

September 18, 2023

Microsoft Fabric: Empowering all personas

In today's business landscape, effectively using business intelligence (BI) is critical. However, many enterprises struggle with deciphering KPIs, comprehending predictive analytics, and achieving data democratization.  

Microsoft Fabric emerged as a game-changer, revolutionizing data visualization, BI, and real-time analytics.  

What is Microsoft Fabric?

Microsoft Fabric is an end-to-end unified SaaS analytics solution. It offers auto-integration, auto-optimization, central governance, and a seamless experience across all workloads. Incorporating all of Microsoft Azure's tools into Fabric makes it a cohesive data platform.  

Fabric enables your organization to gather, process, analyze, and visualize data securely with ease. With capabilities that cover everything from data science to real-time analytics, Fabric streamlines the journey from raw data to actionable insights. 

Key benefits of Microsoft Fabric

Analytics Excellence: Fabric integrates Power BI, Data Factory, and Synapse to create a cost-effective and easy-to-manage analytics platform. 

Cost-Efficiency: Fabric eliminates data silos, data duplication, and vendor lock-in. It also shares unused capacities, resulting in further cost savings. 

Unified Experience: Fabric offers integrated data modeling, dashboarding, and centralized governance, ensuring a consistent approach across all aspects. 

Lake-Centric Approach: OneLake—Fabric’s unified data lake—stores all organizational data regardless of the source or format. This approach eliminates data duplication and movement, minimizing the effort and time spent on data management. 

AI-Powered Insights: Fabric improves analytics with advanced AI tools, including AOAI, Cognitive Services, and ONNX—all seamlessly integrated. The insights generated also support the development of custom OpenAI solutions. 

Data Governance: Fabric provides a unified approach to data warehousing, offering centralized data modeling, security, and lineage management. These benefits simplify data stewardship. 

Robust Security: Fabric's consistent security model, implemented across all engines, safeguards data and ensures compliance. 

Empowering different user roles

Data Engineers: Dive deep into data integration, using rapid Spark VM cluster setups. The data lake architecture seamlessly integrates with tools like Git and DevOps. These tools help ensure data quality and integrity throughout the pipeline. 

Data Scientists: Harness the power of statistical modeling and machine learning algorithms. With Fabric's integration capabilities, time series forecasting, clustering, and regression analysis become much more efficient. 

Data Analysts: Simplify ad-hoc querying and data exploration. Analysts can use multi-dimensional analysis for deeper business insights, transforming them into strategic narratives with visual tools and a Microsoft 365 integration. 

Data Citizens: Achieve self-service analytics with ease. Empower your workforce to make data-driven decisions with drilldowns, slice-and-dice analyses, and interactive dashboards—tailored to individual roles. 

Data Architect: Data architects can streamline design and implementation processes, enjoy increased agility, and benefit from automated tasks. The platform also manages your resources, catalogs, and report metadata. 

Data Governance Manager: Fabric helps you detect and protect sensitive data, apply information protection policies, and meet regulatory requirements across the platform. 

Operations Analyst: Fabric helps you analyze and optimize business processes and operations. Protect your data from unauthorized access and improve the speed of your data and analytics workloads. 

Microsoft Fabric stands out as an essential tool for enterprises seeking unparalleled data intelligence. Its comprehensive suite of features transforms the complex process of data management and analytics into an efficient, streamlined journey. Position your business for sustained success in an increasingly data-driven world with Microsoft Fabric. 


Interested in unlocking the future of analytics today? 

Our certified team possesses deep expertise in Microsoft Fabric's architecture and features. Drawing on extensive experience, we are experts in tackling diverse industry challenges using custom Fabric solutions.  

Contact to partner with us today and transform your data potential into tangible success.

August 25, 2023

Power BI Migration

Power BI Migration Strategy

Enterprises today are looking for more efficient BI platforms to drive decision-making. Many are opting for Power BI due to its:

1. Quick page and report loading.
2. Easy management of multiple data sources.
3. Centralized reporting.
4. Cost-effective maintenance.

Migrating to Power BI, especially with large data volumes, can be complicated. After leading over 100 Power BI migrations for large-enterprise companies, and implementing over 8,000 Power BI solutions, we’ve developed a simple six-step migration strategy. With this strategy, we ensure a streamlined migration to Power BI for your enterprise.

1. Requirement Gathering and Analysis

The first step is gaining a clear understanding of your current BI landscape. Our team evaluates key areas, such as existing reporting platforms, to identify the key functionalities required and the gaps we need to fill. Examining your reports, dashboard usage, UI/UX, audiences, data sources, and security enables us to create a report inventory and data estate.

All this information helps determine the optimal migration scope for your organization, ensuring the performance will align with your business needs.

2. Planning and Design

Next, we propose a solution based on all the requirements gathered in step one. Meetings with relevant stakeholders (architects, data admins, etc.) ensure that the migration plan is aligned with your organizational objectives. 

The planning and design process is divided into five sub-steps. We work to:

1. Identify areas for improvement and areas to address in the migration via a thorough gap analysis.
2. Propose a Power BI architecture, with a focus on data security, refresh latency, and report performance.
3. Design report templates and prepare mock-ups.
4. Define the scope for automated validation.
5. Set a transparent deployment strategy and implementation timeline for clear expectations.

3. Execution

With a well-defined roadmap, this execution phase is streamlined for your team. Our agile framework optimizes our workflow and minimizes disruptions through these steps:

1. Sprint planning: Define product backlogs, scope, and sprint durations.
2. Report migration: Implement using best practices, reusable templates, and themes for incremental report builds.
3. Optimization: Refine both architecture and report layouts to improve performance.
4. Testing: Use in-house performance analysis tools to monitor query performance, suggesting optimizations in layout and data validation.
5. Deployment: Conclude sprints with automated report deployments, preparing for user acceptance testing (UAT).

4. Deployment and Post-Production

The experience of your end users is at the core of our approach. Through rigorous UAT sessions, we make sure the reports are user-friendly, high-performing, and aligned with user needs. Upon approval, deployment is automated. This automation gives end users immediate access to the reports and grants your teams the insights they need without delay.

The final step in the process is the transfer of ownership—handing over code, reports, and workspace details to you.

For many companies, Power BI migration ends here. Yet, for you to truly harness its potential, successful adoption is critical. Thus, our commitment extends to ensuring post-migration success through the next two crucial steps.

5. Center of Excellence (CoE)

Through our CoE training sessions, clients and their team members are empowered to become independent Power BI users.

The CoE sessions conducted familiarize team members with Power BI capabilities, governance, and best practices. The goal is to transition users smoothly as legacy systems phase out. Our custom training includes regular office hours with certified engineers, an advanced curriculum, and pre-built solutions and frameworks. These CoE sessions can shorten the Power BI adoption timeframe from years to months. 

Looking to further boost Power BI adoption? Check out our virtual CoE trainings, available year-round:

  Admin CoE 

6. Decommissioning

A streamlined data environment is pivotal for efficient operations. We methodically phase out redundant reports, ensuring your team works with the most relevant and updated data. With our support, your seamless migration to Power BI becomes the first step to unlocking a wealth of actionable insights for strategic decision-making.

Figure 1: Complete Process Overview

Benefits of Migrating to Power BI

By migrating to Power BI, our clients have benefitted from:

Quicker Insights for Decisionmakers
  Reduce latency between data sources and reports. 
  Improve scalability to support your growing organization.
  Gain real-time insights for immediate decision-making.
  Harness advanced visualization tools for clearer, interactive data representation.

Self-Service BI
  Empower business users to create reports and customize dashboards without needing developer expertise.
  Use natural language queries for straightforward data questions and answers.

Centralized reporting
  Streamline report management with centralized administrative tools.
  Integrate seamlessly with Microsoft ecosystems, such as Dynamics 365, Office 365, and more.
  Consolidate data accuracy with a single source of truth across reporting layers.
  Dive deeper into data with drill-down and drill-through functionalities.
  Protect data with robust security features like row-level security and Azure Active Directory integration.

  Benefit from competitive pricing structures suitable for various business sizes.

Customization and expansion
  Personalize reports with visuals from an expansive marketplace and our certified custom visuals.
  Combine data from varied sources, ensuring cohesive insights.
  Shorten the learning curve with an intuitive, user-friendly interface.

Power BI Migration Case Studies

As the 2021 Microsoft Power BI Partner of the Year, we bring unparalleled expertise to the process of migrating from various data visualization platforms to Power BI. Our certified professionals specialize in managing large data volumes. We ensure not just a seamless migration, but also uninterrupted business operations throughout the process. Read on to learn how migrating to Power BI can be a game-changer.

Tableau to Power BI

Client: An international fast-moving consumer goods (FMCG) company.


High global operations costs: With operations in numerous countries, the high licensing fees for Tableau multiplied quickly, reallocating funds that could be used for other global initiatives.

Need real-time analytics: Tableau's data refresh rates fell short of delivering the real-time analytics required for the client’s supply chain and retail operations.

Low resource efficiency: Tableau's server demands were a bottleneck when handling the client’s high-volume data, making Power BI a more efficient choice.


Migrated 250+ Tableau workbooks to Power BI: Streamlined data management and reduced complexity allowed for faster business-critical decision-making.

300% increase in adoption through COE trainings: Accelerated employee proficiency in Power BI enabled more departments to use data-driven insights for better business outcomes.

Achieved the same functionalities with increased performance: Reduced lag and faster query processing sped up real-time data analysis.

Easy navigation and optimized design: Improved user experience increased report usage and reduced the time needed to extract insights.

Better organized and decluttered reports: Improved data visualizations led to more accurate data interpretation and strategic planning.

Qlik to Power BI

Client: Education First (EF), a global education foundation with offices in 50 countries.


On-premises limitation: Qlik's primary deployment is on-premises, which lacks the flexibility and scalability of cloud-based solutions. This limitation made it challenging for Education First to adapt to remote work scenarios and expand its global operations.

Lack of data modernization: Qlik's architecture and features were not fully aligned with modern data analytics practices, preventing EF from adapting to the changing business landscape.

High operational and maintenance costs: The total cost of ownership for Qlik was not only limited to licensing but also included significant operational costs to maintain and update the platform.

Seasonal needs: Qlik had limitations in quickly scaling up or down to meet the seasonal needs of Education First, affecting both costs and performance.


Cloud-based scalability and centralization: The shift to cloud-based Power BI enabled greater scalability and centralized reporting, aligning with Education First's need for flexible and global operations.

Security and dynamic visuals: Power BI's row-level security and extensive visual options addressed top concerns of security and access to dynamic visuals.

Low-code architecture for citizen development: Power BI's low-code architecture allowed non-technical users to contribute to analytics efforts, facilitating easier report creation and maintenance.

Seamless integration with multiple data sources: The ability of Power BI to easily integrate with 20+ SQL databases and 30 Excel sources simplified data management and reporting.

Reduced costs with scalable features: Power BI’s scalable features that adjusted to EF’s current needs led to significant cost reductions.

Find out more about our QlikView to Power BI migration

SAP Business Objects (SAP BO) to Power BI

Client: A multinational food, snack, and beverage corporation with 250,000+ team members.


Performance and scalability issues: When dealing with large datasets, SAP BO experienced performance lags, affecting the organization’s need for quick insights and scalability.

Lack of real-time analytics: SAP BO's limitations in real-time data analytics did not meet the corporation's needs for immediate insights, particularly in supply chain and retail operations.

• Weak data governance: The limited data governance tools available in SAP BO made it a less viable choice for a multinational organization requiring strong governance capabilities.


90% faster report loading: Reports that took up to 5 minutes to load in SAP BO load in under 20 seconds in Power BI, enabling quicker decision-making.

Real-time KPIs with Azure Analysis Services: The back-end Azure Analysis Services in Power BI not only sped up data loading but also provided built-in time intelligence for on-the-go KPI analysis.

Global data residency: Power BI's cloud architecture supports multiple data residency requirements, helping to meet compliance standards for global operations.

• Improved data governance: Power BI provides superior data governance tools, such as row-level security and audit trails, that enable better control and compliance across the organization.

• Flexible visual reporting: The improved customization options in Power BI allow for more visually appealing and informative reports, enabling deeper insights and better decision-making.

MicroStrategy to Power BI

Client: A global Fortune 500 retailer.


Single platform consolidation: Having migrated their other systems to Power BI for better long-term scalability, the client wants to bring all functionalities under a single platform. Operating on multiple platforms lead to increased costs and complexities in managing different systems.

Visualizations: MicroStrategy’s visualization capabilities are more limited compared to Power BI, which offers more dynamic and interactive visualization options.

Resource intensive: MicroStrategy is resource-intensive, requiring substantial server and hardware resources, particularly for large datasets and complex analytics operations.

Deployment challenges: Deploying MicroStrategy on-premises challenging and requires a well-trained IT team to manage the deployment and maintenance of the platform.


Unified reporting and analytics environment: Centralizing all reports under Power BI simplified the operational landscape and enabled more integrated analytics. This increased cohesion improves collaboration and decision-making.

Improved data visualization: Power BI’s superior visualization capabilities improved the quality of insights derived from the reports.

Resource optimization: Transitioning to the Power BI freed up vital server and hardware resources, promoting system efficiency and generating savings on infrastructure costs.

Simplified deployment and maintenance: Adopting Power BI allowed IT teams to concentrate on strategic operations rather than the maintenance tasks associated with on-prem systems.

Looker to Power BI

Client: A leading retail firm that provides office supplies. 


Ease of usability: Looker’s steep learning curve, particularly for users without a SQL background, is a significant drawback. This learning curve led to higher training costs and decreased efficiency.

High operational and maintenance costs: The operational and maintenance costs associated with running Looker were high. Costs included licensing fees, server upkeep, and other routine maintenance expenses which compounded over time.

Inadequate self-service analytics: Looker was not sufficiently equipped to enable self-service analytics, limiting who was able to explore data and generate reports.


User-friendly interface: Power BI’s user-friendly and simpler interface allowed users to easily customize report views and derive valuable retail insights. This interface improved user experience and promoted effective data analysis.

Empowered self-service analytics: The client used Power BI's self-service analytics to enable non-technical team members to create insightful reports and dashboards. A data-driven culture is now promoted across the organization.

Cost-efficiency and savings: The shift to Power BI has brought down the total cost of ownership for the client. Through optimized resource utilization and reduced infrastructure costs, the client could reallocate the budget to other strategic initiatives.

Seamless sharing of findings: Thanks to Power BI’s built-in export functionalities, users can seamlessly share their findings with others. This functionality was an efficient tool for meeting the client's reporting needs.

Cognos to Power BI

Client: A global service provider in the health, tax & accounting, legal & regulatory, and finance industries.  


Performance bottlenecks with high volume reporting: Due to the high volume of reports, the existing Cognos system faced performance bottlenecks and high costs per click.

Enterprise integration: Power BI offers better compatibility with the client’s existing Microsoft solutions, streamlining business intelligence tasks.

Limited UI features hampering operations: The limited UI features in Cognos negatively affected the ease of business operations, making report generation and customization a challenging task.


Cost per click reduced by 50%: The migration led to a remarkable ~50% reduction in cost per click, generating significant cost savings and optimizing the use of resources.

Better visuals and UI increased report use: Power BI’s higher quality visuals have boosted the use and creation of reports, enabling better data interpretation and insights extraction.

Rapid data loading and customization: Power BI can load dense data in less than 3 seconds, allowing the running of reports without delays. This speed improved efficiency and user satisfaction.

Mobile and cloud-enabled business processes: Adopting Power BI introduced advanced mobile and cloud capabilities to the organization, modernizing the business intelligence setup.


While our six-step migration strategy provides a general framework for success, each organization’s needs are different. Need help achieving a successful Power BI migration? Partner with us by reaching out to

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