How to Migrate from Power BI to Amazon QuickSight (Step-by-Step Guide)

Why Migrate from Power BI to Amazon QuickSight?

Amazon QuickSight has a Serverless architecture, uses a pay-per-session pricing model, and supports seamless integration. It makes an affordable and scalable option for companies. While Power BI has powerful features, QuickSight is better suited for companies using AWS infrastructure. It offers the following benefits.

• ML-powered insights: Amazon QuickSight includes QuickSight Q, which has natural language queries over structured data. Users can type in normal Q&A questions, and the system generates visual answers instantly. 
• Cost-effective: QuickSight uses a unique Pay-per-session model, which means you pay only when users interact with the dashboard. 
• Simplified Architecture: QuickSight is cloud-native, which eliminates the need to manage servers, desktop authoring tools, or on-premises gateways, reducing operational overhead.
• Embedded Analytics: QuickSight provides robust SDKs and APIs to make embedded dashboards look like part of its own UI. It allows you to use customizable themes and branding, and usage-based billing for end users.
• Better Scalability: Power BI’s licensing model can become costly at scale. QuickSight’s pay-per-session approach is more cost-effective, especially for large companies. What this does is make your costs a lot more predictable than with PowerBI. Aside from this, Quicksight also reduces your overall pricing overhead. On the whole, Amazon, being the largest cloud service provider, is also a good consideration. This goes especially considering that, as a business, they’re serving users and experienced profit (90 billion USD in net revenue in 2024) with the large user base they’ve amassed.

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Things to Consider Before Migrating from Power BI to Amazon QuickSight

Before migrating from Power BI to QuickSight, we need to check technical fit, cost, user experience, and change-management impact. The critical considerations to weigh before initiating your migration to Amazon QuickSight.

• Logic Calculations: The most significant hurdle is that Power BI’s DAX does not support QuickSight. It uses calculated fields, dataset-level transformations, and SPICE in-memory acceleration. Complex DAX-heavy dashboards require rebuilding effort in QuickSight. Some teams that use Amazon Bedrock to parse DAX and suggest the equivalent QuickSight syntax. This can still require human validation, but reduces manual logic up to 80%.
• SPICE Engine constraints: QuickSight’s SPICE (Super-fast, Parallel, In-memory Calculation Engine) and Power BI’s Import Mode have different boundaries. QuickSight Enterprise supports up to 5-minute incremental refreshes, but SPICE capacity is a shared pool across your account.
• Cloud Infrastructure Alignment: QuickSight is more optimized for AWS-native environments. If the ecosystems are heavily based on Azure or Microsoft technologies, the Power BI to QuickSight migration increases complexity.
• License and Cost Structure: QuickSight offers Pay-per-session reader pricing, author-based pricing, and SPICE capacity pricing. So, based on the number of active dashboard viewers, frequency of report access, and the need for embedded analytics.
• Security and Governance: The Security Model is different from Power BI and QuickSight. QuickSight utilizes AWS IAM, user groups, and namespaces, with row-level security configurations. 
• Need for Embedded Analytics: For SaaS products, evaluate embedding capabilities by secure dashboard embedding, API-driven management, and Multi-tenant architecture.

How to Migrate from Power BI to Amazon QuickSight (Step-by-Step)

Migrating from Power BI to QuickSight is a structured process that moves your analytics from a client-server model to a cloud-based environment. The steps below denote your Power BI to QuickSight migration process

Step 1: Access Your Power BI Reports

When starting to migrate from Power BI to Amazon QuickSight, look at your existing Power BI reports to make sure that there is a structured route to your migration process. In doing this, prioritization helps you look at reports first while eliminating redundancies in the process, which in turn also reduces storage costs. So overall, we will identify which needs full migration, optimization, or retirement.

• Identify Key Reports: Catalog all Power BI dashboards, reports, and datasets. Prioritize based on usage and business impact. In doing so, you lower the overall effort needed in the migration process.‍
• Analyze Complexity: Review embedded DAX calculations, data transformations, and dependencies that might influence migration. By doing this, what you’re doing is making sure there are minimal performance issues later on and that the operational overhead is not as high.‍
• Define Migration Phases: After this, we’d recommend you sort reports into logical phases to improve the transition process. By carrying out your migration in phases, you prevent business disruption that can cost you customer trust, and you control the cost of the overall effort.

Step 2: Extract Data from Power BI

Extracting data correctly is a major part of making sure there is consistency and integrity when migrating from Power BI to Amazon QuickSight. 

• Access Source Databases and Employ ETL Tools: Use Power BI REST APIs to export datasets while preserving key metadata. Retrieve data directly from the sources rather than Power BI’s internal storage for more control. Use AWS Glue or other ETL solutions to structure data for QuickSight.‍
• Establish Connectivity: Pull Power BI reports from on-premises or Azure-based sources by migrating datasets to AWS, rebuilding ETL pipelines, and optimizing schemas for analytics performance. Now start connecting QuickSight to your data sources. Ensure QuickSight has proper IAM permissions to access those services.

Step 3: Transform Data for QuickSight Compatibility

QuickSight’s full potential can be obtained when the data is flat and pre-aggregated. Data transformation makes sure that datasets align with QuickSight’s requirements and enhance performance when migrating from Power BI.

• Standardize Formats and redesign: Adjust schemas, field types, and relationships to match QuickSight’s architecture. Both Power BI and QuickSight differ in their modeling layer and DAX calculations. QuickSight uses SPICE (in-memory engine), recreates DAX logic into QuickSight formulas. They validate joins and aggregations. ‍
• Define Data Joins and Use AWS Glue: Set up relationships to support accurate aggregations and queries in QuickSight. Utilize AWS Glue to clean, format, and prepare data efficiently.

Step 4: Migrating Data into Amazon QuickSight

Loading data efficiently is critical to ensure users can interact with reports seamlessly.

• Optimize SPICE: Use QuickSight’s SPICE engine for faster performance with large datasets. It can handle up to 1TB of data in-memory for sub-second dashboard performance. ‍
• Schedule Refreshes and Translate Logic: Set automated data refresh intervals to maintain data accuracy. Recreate your DAX measures using QuickSight’s calculated fields using Amazon Q for Business.

Step 5: Security Configurations

Security mapping is the most critical one. When comparing the security configurations, Power BI relies on Azure Active Directory and RLS rules. But QuickSight is managed by AWS IAM policies, QuickSight user groups, Dataset-level permissions, and Row-level security rules. Create a separate dataset that maps users/groups to specific data values and re-implement security rules.

Step 6: Rebuild Reports and Dashboards in QuickSight

Start rebuilding the dashboards, focus on using QuickSight’s unique features such as ML Insights and narratives. It ensures continuity and unlocks new capabilities for your ecosystem.

• Analyze Existing Reports and Use QuickSight Features: Identify key metrics, filters, and visual elements to be replicated. Utilize calculated fields, interactive dashboards, and ML-powered insights of QuickSight. Other features of QuickSight, such as ML insights and Narratives, provide auto-generated text summaries of your data. Use filters, drill-downs, and interactive elements for better engagement of customers.

Step 7: Validate and Test Data

Run both Power BI and QuickSight in parallel. It is an important step to check data integrity and alignment with business requirements. To do this effectively, we would recommend that users:

• Cross-Check Metrics and Gather User Feedback: Compare key figures between Power BI and QuickSight. Look for missing values, inconsistencies, and incorrect mappings. Engage end users to validate reports and confirm alignment with expectations.

Step 8: Deploy to QuickSight and Configure Access

Deployment makes reports accessible and ensures there is proper governance and visibility on your full ecosystem.

• Publish Dashboards and Monitor Usage: Define user access controls to maintain security and compliance. Make sure reports are rendered properly and ready for distribution. Track reports engagement to refine dashboards based on user behavior.

What to Do After You Migrate from Power BI to Amazon QuickSight

A Power BI to QuickSight migration is considered successful only when post- migration steps are carried out.

• Validate KPI Consistency: Do a comparison of key KPIs between Power BI and QuickSight. Verify calculated fields and aggregations and test filters, drill-down, and parameters. By doing a parallel validation phase, we report discrepancies that can impact decision-making.
• Optimize SPICE Engine: QuickSight supports datasets up to 2TB. Check SPICE ingestion logs to see if any datasets are failing to approach the limit. Set up an incremental refresh to only pull in the last 24 hours of data by reducing the load on your source database.
• Governance and Security: Verify that Row-level Security (RLS) is implemented. Provide group-based permissions to individual users. Use IAM Groups or QuickSight Groups to manage access at scale.
• Train Users and Document Migration Processes: Proper training and documentation improve adoption and ease future maintenance. Develop user guides, FAQs, and video tutorials tailored to different roles. Offer hands-on training for report building and data analysis. Keep a record of migration steps, data sources, and dashboard configurations.
• Decommission Power BI: Set your Power BI reports to Read-Only. This allows users to check any discrepancies in new QuickSight reports. So once everything is ready, decommission Power BI and cancel its license.

Why Choose Entrans for Your Power BI to Amazon QuickSight Migration

Power BI has limitations when it comes to large-scale cloud analytics. Migrating to Amazon QuickSight offers a more scalable, cost-effective, and AWS-friendly solution.

At Entrans, we have PowerBI experts, certified AWS professionals, and teams equipped to handle maintaining and automating your cloud infrastructure, data pipelines, and even help you put CI/CD systems in place.

Want to know about how we make the Power BI to QuickSight migration smoother? Book a consultation call.