Migrating from Power BI to Apache Superset: The D23.io Playbook

Table of Contents

1. Why Migrate from Power BI to Apache Superset?
2. Pre-Migration Assessment and Planning
3. Data Source Remapping Strategy
4. Dashboard and Report Rebuild Process
5. Semantic Layer and Calculated Fields Translation
6. User Training and Change Management
7. Cutover Timeline and Go-Live Strategy
8. Post-Migration Optimisation and Support
9. Common Pitfalls and How to Avoid Them
10. Next Steps and Long-Term Roadmap

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Why Migrate from Power BI to Apache Superset?

Power BI has dominated the business intelligence landscape for a decade, but it comes with real constraints: per-seat licensing costs that scale linearly with headcount, vendor lock-in that makes switching expensive, limited customisation for embedded analytics, and architecture that doesn’t play well with modern data stacks built on open standards.

Apache Superset, by contrast, is a modern open-source BI platform that aligns with how data teams actually build today. It runs on your infrastructure, supports any SQL-queryable data source, integrates seamlessly with data warehouses like Snowflake, BigQuery, and Redshift, and costs a fraction of Power BI at scale. For teams building custom software development solutions or modernising their platform engineering stack, Superset fits natively into the workflow.

But the migration itself is non-trivial. Power BI’s proprietary data model, DAX calculations, and row-level security (RLS) don’t translate directly. This guide walks you through the exact process we use at PADISO to move teams from Power BI to Superset without losing data integrity, user adoption, or business continuity.

The stakes are high: a botched BI migration can cost weeks of rework, alienate end users, and delay decision-making across the business. Done right, you’ll cut licensing costs by 40–70%, gain full control over your analytics infrastructure, and unlock the ability to embed analytics into your product. We’ve done this for 15+ teams across financial services, retail, and SaaS—and we’ve learned what works.

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Pre-Migration Assessment and Planning

Audit Your Current Power BI Estate

Before you touch a single dashboard, you need a complete inventory of what you’re moving. Power BI sprawl is real: teams build dashboards in isolation, reports accumulate, and nobody knows what’s actually in use.

Start with a structured audit:

• Count all workspaces, apps, and reports. Use the Power BI Admin Portal to export a complete list. We typically find 30–50% of reports are unused or duplicated.
• Map data sources. Which databases, data warehouses, or cloud services feed your reports? Document connection strings, authentication methods, and refresh schedules. Many teams discover they have 12+ data sources when they thought they had 3.
• Identify critical vs. nice-to-have reports. Work with business stakeholders to rank dashboards by user count, decision impact, and refresh frequency. The top 20% of reports usually drive 80% of value.
• Document calculated columns, measures, and DAX logic. This is the hardest part. DAX is Power BI’s secret sauce, but it’s also the biggest migration blocker. Export the data model from each report and build a DAX translation matrix.
• Assess row-level security (RLS) requirements. If you’re using RLS to restrict data by department, region, or user, you’ll need to replicate this in Superset. It’s doable but requires planning.
• Check refresh schedules and latency requirements. How often do dashboards refresh? Do users need real-time data, or is hourly sufficient? This affects your Superset architecture.

This audit typically takes 2–3 weeks for a mid-sized Power BI estate (50–100 reports). Budget for it.

Define Your Target Architecture

Superset is flexible—too flexible, sometimes. You need a clear target architecture before you start building.

Decide on:

• Deployment model. Cloud-hosted (Preset, AWS, GCP) or self-managed (Docker, Kubernetes)? Self-managed gives you cost control and data residency; cloud-hosted gives you operational simplicity. For platform development in Sydney or other regulated environments, self-managed is often required.
• Data warehouse and semantic layer. Superset works best with a well-structured data warehouse and a semantic layer (dbt, Looker, or Superset’s own native semantic layer). If you’re migrating from Power BI’s import-based model, you’ll likely need to shift to a warehouse-native architecture. This is a feature, not a bug—it scales better and keeps data in one source of truth.
• Authentication and access control. Will you use LDAP, OAuth, or SAML? Superset supports all three, but the setup differs. If you’re managing SOC 2 or ISO 27001 compliance (which many of our clients are), you’ll want SAML and audit logging from day one.
• Embedding strategy. Are you embedding dashboards into your product or internal apps? Superset’s embedding is cleaner than Power BI’s, but requires a different approach. Plan this early.

Build Your Migration Timeline

A realistic migration timeline depends on scope, but here’s a rule of thumb:

• Small estate (1–10 critical reports): 6–8 weeks
• Medium estate (20–50 reports): 10–14 weeks
• Large estate (100+ reports): 16–24 weeks

Our playbook assumes a medium estate with a 12-week timeline:

• Weeks 1–2: Assessment and planning (this section)
• Weeks 3–4: Data source setup and testing
• Weeks 5–8: Dashboard rebuild and UAT
• Weeks 9–10: User training and cutover planning
• Weeks 11–12: Pilot cutover, monitoring, and rollback plan

Build in a 2-week buffer for unexpected issues. You’ll need it.

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Data Source Remapping Strategy

Inventory and Validate All Data Sources

Power BI often abstracts data sources behind a proprietary model. In Superset, you’re working directly with SQL, so you need to understand your sources at a deeper level.

For each Power BI data source, document:

• Source type: SQL Server, Azure SQL, Snowflake, BigQuery, Salesforce, SharePoint, etc.
• Connection details: Server, database, schema, port, authentication method.
• Refresh schedule: How often does Power BI pull data? Is it scheduled, on-demand, or real-time push?
• Row count and table structure: Understand the size and shape of the data.
• Transformations applied in Power BI: Power Query transformations, calculated columns, relationships. These need to move somewhere—either into your data warehouse or into Superset’s semantic layer.

Create a spreadsheet with columns for source name, type, connection string, owner, and migration status. Update it as you progress.

Set Up Superset Data Connections

Superset connects to data sources via SQLAlchemy, which means it supports most SQL-queryable systems: PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, Databricks, Athena, and 40+ others. See the Apache Superset Official Website for the full list.

For each data source:

1. Test connectivity from the Superset environment. Network access is the most common blocker. If Superset is cloud-hosted and your database is behind a corporate firewall, you’ll need a VPN tunnel or allowlist.
2. Create a read-only database user. Don’t connect as an admin. Create a service account with SELECT permissions on the tables you need. This is a security best practice and required for SOC 2 compliance.
3. Validate query performance. Run a few sample queries against your largest tables. Superset’s performance depends on query speed. If your Power BI data model was optimised for import, you may need to add indexes or materialised views in your warehouse.
4. Test incremental refresh. If Power BI was doing incremental refreshes, you’ll need to set up the same logic in your warehouse or via Superset’s cache invalidation.

Document the Superset connection name, credentials (stored securely in environment variables), and any special parameters (e.g., SSL mode, timezone settings).

Map Power BI Datasets to Superset Tables and Semantic Layers

This is where the real work begins. Power BI’s dataset abstraction—with its calculated columns, measures, and relationships—needs to move somewhere.

You have three options:

Option 1: Migrate logic to the data warehouse (recommended). Use dbt, SQL views, or stored procedures to replicate Power BI’s data model in your warehouse. This is the most scalable approach and aligns with modern data stack practices. Your Superset dashboards then query clean, pre-aggregated tables.

Option 2: Use Superset’s native semantic layer. Superset 2.0+ includes a semantic layer that lets you define metrics, dimensions, and relationships in Superset itself. This is faster to implement but less portable if you ever switch BI tools again.

Option 3: Hybrid approach. Move complex transformations to the warehouse, use Superset’s semantic layer for dashboard-specific metrics.

We recommend Option 1 for most teams, especially if you’re using platform engineering practices. It keeps your BI tool agnostic and your data logic version-controlled.

For each Power BI dataset:

• List all calculated columns and measures.
• Determine whether each should live in the warehouse (as a column or view) or in Superset (as a metric).
• Document the DAX formula and its equivalent SQL or dbt expression.
• Test the output against Power BI to ensure numerical accuracy.

This translation is tedious but critical. A 1% difference in a KPI calculation will erode user trust immediately.

Handle Authentication and Data Access Control

If you’re using Power BI’s row-level security (RLS), you’ll need to replicate it in Superset. Superset supports RLS via SQL filters, but the mechanism is different.

In Power BI, RLS is enforced at the dataset level using DAX filters. In Superset, RLS is enforced via SQL WHERE clauses applied at query time. For example:

SELECT * FROM sales WHERE region = '{{ current_user.region }}'

To set this up:

1. Define user attributes in Superset. Store region, department, or other RLS criteria in Superset’s user metadata.
2. Create RLS rules for each dataset. Superset lets you apply SQL filters to tables based on user attributes.
3. Test thoroughly. Verify that each user sees only the data they should. RLS bugs are security bugs.

If you’re managing compliance (SOC 2, ISO 27001), document your RLS rules as part of your access control policy. NIST guidance on secure cloud business applications recommends role-based access control with audit logging—Superset supports this natively.

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Dashboard and Report Rebuild Process

Prioritise Dashboards for Migration

Don’t rebuild everything at once. Start with your top 20% of dashboards—the ones that drive decisions, have the most users, or refresh most frequently.

Rank dashboards by:

• User count: How many people access this dashboard daily or weekly?
• Decision impact: Does this dashboard inform budget, hiring, or product decisions?
• Refresh frequency: Real-time or hourly dashboards are higher priority than monthly reports.
• Complexity: Start with simpler dashboards to build team confidence, then tackle complex ones.

Aim to rebuild your top 20% in the first 6 weeks. This gives you early wins and time to refine your process before tackling the long tail.

Rebuild Dashboards in Superset

Superset’s UI is different from Power BI. Your team will need to learn it, but it’s intuitive once you understand the model: datasets → charts → dashboards.

For each dashboard:

1. Create a blank Superset dashboard. Name it consistently (e.g., “[DEPT] [DASHBOARD_NAME] - Superset”).
2. Identify all charts in the Power BI dashboard. For each chart, note its type (bar, line, scatter, etc.), dimensions, measures, and filters.
3. Create charts in Superset. Use the dataset you created in the previous step. Superset’s chart builder is visual and supports most chart types Power BI does. A few differences to watch:
   • Superset is SQL-first. You can write custom SQL for any chart, giving you more flexibility than Power BI’s visual query builder.
   • Aggregations are explicit. In Superset, you choose SUM, AVG, COUNT, etc. explicitly. Power BI sometimes infers this; Superset doesn’t.
   • Drill-down is different. Power BI’s drill-down is built-in; Superset uses cross-filters and linked dashboards instead.
4. Apply filters and interactivity. Superset supports dashboard-level filters and chart-to-chart interactions. These work differently than Power BI but are often more flexible.
5. Match styling. Use Superset’s theming to match your brand colours and fonts. This matters for user adoption.

Validate Output Against Power BI

Before you move a dashboard to production, validate it against the Power BI original.

• Run the same filters and verify the numbers match. A mismatch usually means a calculation is wrong or a filter is missing.
• Check formatting. Numbers, dates, and currency should display the same way.
• Test performance. Does the Superset dashboard load as fast as Power BI? If not, you may need to optimise your queries or add caching.
• Have a business user review it. They’ll spot issues a technical person might miss.

Document any discrepancies and their root cause. This builds confidence in the migration.

Handle Advanced Features

Some Power BI features don’t have direct equivalents in Superset:

• Power BI’s Q&A (natural language queries): Superset doesn’t have this. You can work around it with pre-built dashboards and filters.
• Power BI’s AI visuals (key influencers, decomposition tree): These are Power BI-specific. Recreate the analysis manually or use Python notebooks in Superset.
• Power BI’s paginated reports: Superset doesn’t have paginated reports. Use Superset dashboards or export to PDF.
• Power BI’s real-time dashboards: Superset supports real-time via WebSocket, but setup is more complex. For most use cases, 1-minute refresh is sufficient.

If a feature is critical and Superset can’t replicate it, flag it early. Sometimes you’ll keep a small Power BI instance for specific reports, accepting the hybrid cost.

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Semantic Layer and Calculated Fields Translation

Translate DAX to SQL or dbt

DAX is Power BI’s calculation language. It’s powerful but proprietary. Moving it to Superset means translating it to SQL or dbt.

Common DAX patterns and their SQL equivalents:

Simple SUM:

Sales Amount := SUM(Sales[Amount])

SELECT SUM(amount) as sales_amount FROM sales

Conditional SUM (SUMIF):

High Value Sales := SUMIF(Sales[Amount] > 1000, Sales[Amount])

SELECT SUM(CASE WHEN amount > 1000 THEN amount ELSE 0 END) as high_value_sales FROM sales

Year-over-year growth:

YoY Growth := ([Current Year Sales] - [Prior Year Sales]) / [Prior Year Sales]

SELECT 
  (SUM(CASE WHEN YEAR(date) = 2024 THEN amount ELSE 0 END) - 
   SUM(CASE WHEN YEAR(date) = 2023 THEN amount ELSE 0 END)) / 
  SUM(CASE WHEN YEAR(date) = 2023 THEN amount ELSE 0 END) as yoy_growth
FROM sales

Time intelligence (PARALLELPERIOD):

Sales SAMEPERIOD LY := CALCULATE([Sales Amount], PARALLELPERIOD(Date[Date], -1, YEAR))

SELECT 
  SUM(CASE WHEN YEAR(date) = YEAR(CURRENT_DATE) - 1 THEN amount ELSE 0 END) as sales_prior_year
FROM sales
WHERE MONTH(date) = MONTH(CURRENT_DATE)

For each DAX measure:

1. Understand what it calculates. Read the formula, check its usage in Power BI reports.
2. Write the SQL equivalent. Test it against your data warehouse to ensure accuracy.
3. Decide where it lives. Simple aggregations can stay in Superset. Complex logic should move to a dbt model or database view.
4. Document it. Create a translation guide for your team.

DAX translation is time-consuming but essential. Budget 2–3 days per complex measure.

Build Your Semantic Layer in dbt or Superset

Once you’ve translated DAX, you need to implement it somewhere. We recommend dbt for most teams, especially those using platform engineering practices.

With dbt:

• Create staging models that clean and standardise raw data.
• Create mart models that implement your business logic (the translated DAX).
• Define tests to ensure data quality and prevent regressions.
• Version control everything so you can track changes and roll back if needed.

Example dbt model:

version: 2

models:
  - name: fct_sales
    description: "Fact table with sales transactions and calculated metrics"
    columns:
      - name: sales_id
        tests:
          - unique
          - not_null
      - name: sales_amount
        description: "Total transaction amount"
      - name: high_value_flag
        description: "1 if amount > 1000, else 0"

Then in Superset, you query fct_sales directly. The semantic layer is in dbt, not in Superset or Power BI.

Alternatively, use Superset’s native semantic layer if you want everything in one place. It’s simpler for small teams but less portable.

Test Calculated Fields Against Power BI

Before you deploy, validate every calculation:

1. Run the same query in Power BI and Superset. Do the numbers match?
2. Test edge cases. What happens with NULL values, zero values, negative values?
3. Check performance. Is the query fast enough for your users?
4. Have a data analyst review it. They’ll spot logical errors.

A 0.1% difference in a calculation can snowball into a trust issue if users discover it later.

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User Training and Change Management

Build a Training Plan

Superset’s UI is different from Power BI. Your users will need training, and they’ll resist if they don’t understand the new tool.

Structure training in tiers:

Tier 1: Executives and business users (1 hour)

• How to navigate dashboards
• How to use filters and drill-down
• Where to find the dashboard they need
• Who to contact for help

Tier 2: Power users and analysts (4 hours)

• Everything in Tier 1
• How to create and edit charts
• How to export data
• How to use the SQL editor for custom queries
• How to request new dashboards

Tier 3: Dashboard creators and admins (2 days)

• Superset architecture and data model
• How to create datasets and semantic layers
• How to set up RLS and access control
• How to optimise query performance
• Troubleshooting and debugging

Schedule training 2 weeks before go-live. This gives users time to ask questions and build confidence.

Communicate the Why

Users resist change, especially when they’re comfortable with Power BI. Communicate the benefits:

• Cost savings: “We’re cutting BI licensing costs by 50%, which means more budget for product development.”
• Speed: “Dashboards will load faster and update more frequently.”
• Control: “We own our analytics infrastructure now, not Microsoft.”
• Integration: “Superset integrates with our data warehouse and product, enabling embedded analytics.”

Share these messages in team meetings, email, and documentation. Make it clear that this is a business decision, not a technical one.

Create Documentation and Support

Build a knowledge base:

• Dashboard directory: A searchable list of all dashboards with descriptions and owners.
• FAQ: Common questions and answers (“How do I export data?”, “Why is my dashboard loading slowly?”).
• Video tutorials: Screen recordings of common tasks (filtering, drilling down, exporting).
• Feedback form: Let users report issues or request features.

Designate a support contact (ideally a power user, not just IT). They’ll handle questions and escalate issues.

Plan a Phased Rollout

Don’t cut over all users at once. Use a phased approach:

Phase 1: Pilot (Week 1)

• 10–20 power users and analysts
• Intensive support and daily check-ins
• Feedback loop to fix issues before wider rollout

Phase 2: Early adopters (Week 2)

• 50–100 users
• Reduce support intensity, but still monitor closely
• Gather feedback on dashboards and usability

Phase 3: Full rollout (Week 3)

• All users
• Keep Power BI available as a fallback for 2 weeks
• Monitor performance and user adoption

This approach reduces risk and gives you time to fix issues before they affect everyone.

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Cutover Timeline and Go-Live Strategy

Plan the Cutover Sequence

Cutover is the moment you switch from Power BI to Superset. It needs to be choreographed carefully.

T-minus 4 weeks:

• Finalise all dashboard rebuilds
• Complete user training
• Publish the cutover plan and timeline

T-minus 2 weeks:

• Run parallel testing: users run reports in both Power BI and Superset, comparing results
• Fix any discrepancies
• Finalise the rollback plan

T-minus 1 week:

• Freeze changes to Power BI (no new reports or updates)
• Perform a final data reconciliation
• Brief support teams

Cutover day:

• Start with the pilot group (Phase 1)
• Monitor Superset performance and user experience
• Have rollback procedures ready
• Schedule check-ins every 2 hours

Post-cutover:

• Monitor for 1 week intensively, then 2 weeks moderately
• Gather feedback and prioritise fixes
• Celebrate the win

Prepare a Rollback Plan

If cutover goes wrong, you need to revert to Power BI quickly. Plan for this:

• Keep Power BI running in parallel for 2 weeks. Don’t decommission it immediately.
• Document the rollback procedure. How long does it take? Who approves it? What’s the communication plan?
• Identify rollback triggers. If more than 10% of users report critical issues, or if a KPI report is inaccurate, trigger rollback.
• Test the rollback. Don’t assume it will work. Actually test reverting to Power BI in a staging environment.

Having a rollback plan isn’t pessimistic—it’s professional. It reduces the stress of cutover and lets you move confidently.

Monitor Performance and Adoption

Post-cutover, track:

• Dashboard load times. Is Superset as fast as Power BI? If not, optimise queries or add caching.
• User adoption. How many users are logging in? Are they using Superset or avoiding it?
• Support tickets. What issues are users reporting? Prioritise the most common ones.
• Data accuracy. Are users spotting discrepancies between Power BI and Superset? Investigate and fix immediately.

Schedule a post-mortem 1 week after cutover. What went well? What could be better? Use this feedback to improve the process for future migrations.

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Post-Migration Optimisation and Support

Optimise Query Performance

After cutover, you’ll likely discover performance issues. Superset’s performance depends on query speed, so optimisation is critical.

Common bottlenecks:

• Large table scans. If a dashboard queries a 100M-row table without a WHERE clause, it will be slow. Add indexes or materialised views.
• Complex joins. If your dashboard joins 5+ tables, consider pre-aggregating in a mart table.
• Unoptimised SQL. Sometimes the SQL generated by Superset’s visual query builder isn’t optimal. Rewrite it manually.
• Caching issues. Superset caches query results, but the cache can get stale. Configure cache TTL appropriately for each dashboard.

Tools to help:

• Query profiling. Use your database’s EXPLAIN PLAN to understand query performance.
• Superset’s query inspector. View the SQL generated by visual charts and optimise it.
• Database monitoring. Track slow queries and identify patterns.

Target: most dashboards should load in under 5 seconds. If a dashboard takes longer, optimise or break it into multiple dashboards.

Set Up Monitoring and Alerting

Monitor Superset’s health:

• Uptime monitoring. Is Superset accessible? Set up a synthetic monitor that checks every 5 minutes.
• Query performance. Track the 95th percentile query time. Alert if it spikes.
• Data freshness. Are dashboards refreshing on schedule? Alert if a refresh fails.
• User activity. How many users are active? Are they using Superset as expected?

Use tools like Datadog, New Relic, or Prometheus for monitoring. Integrate alerts with Slack or email so you catch issues early.

Establish a Maintenance Cadence

Superset, like any software, needs ongoing care:

• Weekly: Review support tickets and prioritise fixes.
• Monthly: Update Superset and dependencies. Test in staging first.
• Quarterly: Review dashboard usage and archive unused ones. Review data freshness and optimise slow queries.
• Annually: Audit access control and RLS rules. Update documentation.

Assign ownership. Someone needs to be responsible for Superset’s health. This could be a data engineer, a platform engineer, or a BI analyst.

Plan for Growth

As your team adopts Superset, usage will grow. Plan for it:

• More dashboards. Establish a dashboard request process and prioritise backlog.
• More users. Superset scales, but you may need to add resources (more database connections, more cache, more Superset instances).
• More data. Your data warehouse will grow. Monitor storage and query performance.
• More integrations. Teams will want to embed Superset in their products. Plan for this.

Review capacity every quarter. If you’re at 80% utilisation, plan to scale before you hit limits.

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Common Pitfalls and How to Avoid Them

Pitfall 1: Underestimating DAX Translation

Problem: DAX is complex, and translating it to SQL takes longer than expected. Teams often discover halfway through that a measure is more complicated than they thought.

Solution: Budget 3–5 days per complex measure for translation and testing. Create a translation guide early and validate every calculation against Power BI. If a measure is too complex to translate, consider keeping it in Power BI or simplifying it.

Pitfall 2: Ignoring Data Quality Issues

Problem: Power BI’s data model sometimes masks data quality issues. When you move to Superset and query the raw warehouse, you discover NULL values, duplicates, or inconsistencies that Power BI was hiding.

Solution: Run a data quality audit before migration. Use tools like dbt tests or Great Expectations to validate data. Fix issues in the source system or warehouse, not in Superset.

Pitfall 3: Poor Change Management

Problem: Users are comfortable with Power BI. They resist Superset, even if it’s objectively better. Adoption stalls, and you end up running both tools in parallel indefinitely.

Solution: Invest heavily in communication and training. Involve users early in the migration. Show them the benefits (cost savings, faster dashboards, new capabilities). Celebrate wins publicly. Assign a power user champion in each department to help their peers.

Pitfall 4: Inadequate Testing

Problem: Dashboards look right but have subtle bugs. A filter doesn’t work correctly. A calculation is off by 1%. These issues erode user trust.

Solution: Test rigorously. Run parallel testing where users compare Power BI and Superset results. Have a data analyst review every calculation. Test edge cases (NULL values, zero values, negative values). Test with real user workflows, not just isolated queries.

Pitfall 5: Neglecting Security and Compliance

Problem: You migrate to Superset but forget about RLS, audit logging, or encryption. Later, a security audit finds gaps, and you scramble to fix them.

Solution: Plan security from the start. If you’re managing SOC 2 or ISO 27001 compliance, build compliance requirements into your migration plan. Use SAML for authentication, enable audit logging, enforce encryption in transit and at rest. Document your security controls as you build them.

Pitfall 6: Cutting Over Too Fast

Problem: You’re eager to decommission Power BI and save costs. You cut over all users at once, hit unexpected issues, and have no fallback.

Solution: Use a phased rollout. Start with a pilot group, then early adopters, then full rollout. Keep Power BI running in parallel for at least 2 weeks. Have a documented rollback plan and test it.

Pitfall 7: Ignoring Performance Optimisation

Problem: Superset dashboards are slower than Power BI. Users complain. You don’t know why or how to fix it.

Solution: Optimise proactively. Profile queries before cutover. Add indexes to your warehouse. Use materialised views for complex calculations. Configure caching appropriately. Monitor query performance post-cutover and optimise based on actual usage patterns.

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Next Steps and Long-Term Roadmap

Immediate Post-Migration (Weeks 1–4)

1. Monitor closely. Check in with users daily. Fix issues quickly.
2. Gather feedback. What’s working? What’s not? Prioritise improvements.
3. Optimise performance. If dashboards are slow, investigate and fix.
4. Decommission Power BI gradually. After 2 weeks, if things are stable, start archiving Power BI reports. Keep a few for reference, but stop maintaining them.

Medium Term (Months 2–6)

1. Expand dashboard coverage. Rebuild the remaining 80% of dashboards.
2. Embed analytics in your product. If you’re building custom software development solutions, embed Superset dashboards in your app. This is a huge value-add for customers.
3. Build self-service analytics. Teach power users to create their own dashboards. This reduces the backlog of dashboard requests.
4. Optimise the data warehouse. Review data freshness, storage costs, and query performance. Optimise based on actual usage.

Long Term (6–12 Months)

1. Integrate with your data stack. Connect Superset to your dbt project, data quality tools, and data catalogues. This creates a unified data experience.
2. Explore advanced features. Superset has features Power BI doesn’t: custom plugins, Python notebooks, advanced caching. Experiment with these.
3. Plan for scale. If your team grows or data volume explodes, plan for Superset scaling (more instances, database optimisation, caching layer).
4. Contribute to the community. If you build custom plugins or solve interesting problems, consider contributing back to Apache Superset. This builds your team’s reputation and helps the community.

Leverage PADISO for Continued Support

Migration doesn’t end at cutover. You’ll need ongoing support, optimisation, and strategic guidance. That’s where PADISO’s platform engineering services come in.

We specialise in helping teams like yours modernise their data infrastructure. Whether you’re migrating from Power BI, optimising your data warehouse, or building embedded analytics into your product, we have the expertise to move fast and build right.

Our approach:

• Fractional CTO leadership. Get senior technical guidance without hiring full-time.
• Co-build partnerships. We don’t just advise—we build alongside your team.
• Outcome-focused. We measure success by your metrics: faster dashboards, lower costs, higher adoption.
• Security-first. SOC 2 and ISO 27001 compliance are built in, not bolted on.

If you’re planning a Power BI migration or modernising your analytics stack, reach out to PADISO. We’ve done this for 15+ teams across financial services, retail, and SaaS. We know what works, and we know what doesn’t.

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Summary: The Migration Playbook at a Glance

Migrating from Power BI to Apache Superset is achievable if you follow a structured playbook:

1. Assess thoroughly. Audit your Power BI estate, understand your data sources, and define your target architecture. This takes time, but it’s essential.
2. Plan the timeline. A medium-sized estate (20–50 reports) takes 10–14 weeks. Budget for it.
3. Remap data sources. Test connectivity, validate query performance, and set up authentication. Don’t skip this—it’s where most migrations fail.
4. Rebuild dashboards systematically. Start with your top 20% of dashboards. Validate output against Power BI. Document everything.
5. Translate DAX carefully. DAX translation is the most time-consuming part. Budget 3–5 days per complex measure.
6. Train users extensively. Change management is as important as technical execution. Invest in communication, training, and support.
7. Cut over gradually. Use a phased rollout with a documented rollback plan. Keep Power BI running in parallel for 2 weeks.
8. Optimise relentlessly. Post-cutover, focus on performance, adoption, and user satisfaction.
9. Plan for growth. Superset scales, but you need to plan for it.

Done right, you’ll cut licensing costs by 40–70%, gain full control over your analytics infrastructure, and unlock new capabilities like embedded analytics and self-service BI. The investment pays for itself in 6–12 months.

Ready to start? The first step is an honest assessment of your current Power BI estate and your target state. If you’d like help with that, PADISO’s team has done this dozens of times. We can help you build a realistic roadmap, identify risks early, and execute the migration with confidence.

Your analytics infrastructure is too important to leave to chance. Let’s build it right.