Migrating from Sisense to Superset for Enterprise Organisations

Table of Contents

1. Why Enterprise Teams Migrate from Sisense to Superset
2. Pre-Migration Assessment and Scoping
3. Understanding the Architecture Differences
4. Cost Benchmarking and Financial Planning
5. Governance and Data Security During Migration
6. The Migration Playbook: Step-by-Step Execution
7. Cutover Patterns and Rollback Strategy
8. Post-Migration Optimisation and Operations
9. Real-World Migration Timelines and Lessons
10. Next Steps and Getting Help

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Why Enterprise Teams Migrate from Sisense to Superset

Sisense has been a workhorse for mid-market and enterprise analytics for over a decade. It handles complex data models, embedded analytics, and sophisticated dashboard logic. But increasingly, teams are asking harder questions about total cost of ownership (TCO), vendor lock-in, and the ability to own their analytics stack end-to-end.

Apache Superset has matured significantly. It’s open source, cloud-agnostic, and runs on commodity infrastructure. For organisations paying Sisense per-seat or per-user licensing, the arithmetic becomes compelling fast. A 50-person analytics team on Sisense can cost $500K–$1.5M annually. The same team on self-hosted Superset might cost $150K–$300K in infrastructure and engineering time.

But cost alone doesn’t drive migration. Enterprise teams are also moving because:

• Operational control: No more vendor lock-in. Your dashboards, data models, and configurations live in version control.
• Embedded analytics: Superset’s embedding API and multi-tenancy support make it ideal for product teams building analytics into SaaS platforms.
• Modern data stack alignment: Superset integrates natively with dbt, Kubernetes, and cloud data warehouses like Snowflake, BigQuery, and Redshift.
• Regulatory readiness: Open-source tooling simplifies SOC 2 and ISO 27001 audit trails. You control the codebase, access logs, and deployment pipelines.

According to the official Sisense vs Apache Superset comparison, the choice often comes down to use case fit and organisational appetite for operational complexity. Sisense excels at embedded analytics and complex OLAP cubes. Superset excels at speed-to-insight, cost efficiency, and operational transparency.

For enterprises running platform engineering initiatives across Australia and the United States, Superset has become the default choice for analytics infrastructure. It pairs well with modern data platforms and integrates cleanly into CI/CD pipelines.

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Pre-Migration Assessment and Scoping

Before you touch a single dashboard, you need to understand what you’re moving. This is the most critical phase, and it’s where most migrations stumble.

Audit Your Current State

Start by cataloguing everything in Sisense:

• Dashboards: Count them. Categorise by audience (executive, operational, embedded). Note which are actively used versus legacy.
• Data sources: List all connected databases, APIs, and data warehouses. Document connection strings, authentication methods, and refresh schedules.
• Custom code: Identify any JavaScript, Python, or custom plugins. These rarely port cleanly to Superset.
• Users and permissions: Export user lists and role assignments. Note which users have custom permissions or shared dashboard access.
• Scheduled reports: Document email distribution, schedules, and recipients.
• Embedded dashboards: If you’re embedding Sisense in a product, this is a major workstream.

Create a simple spreadsheet:

Asset	Count	Complexity	Owner	Criticality	Effort (days)
Dashboards	47	Low	Analytics	High	8
Dashboards	12	High	Finance	Critical	20
Data sources	8	Medium	Engineering	High	5
Custom code	3	High	Product	Medium	15

This forces clarity. You’ll quickly see which assets are worth migrating and which can be retired.

Identify Migration Candidates

Not everything needs to move. Some dashboards are:

• Rarely used (check access logs from the past 90 days).
• Redundant (multiple versions of the same dashboard).
• Built on deprecated data sources.
• Dependent on custom code that’s unmaintainable.

Propose retiring 20–30% of your Sisense estate. This dramatically reduces scope and cost. Stakeholders often resist until you show them the effort-to-value tradeoff.

Assess Data Model Complexity

Sisense excels at complex OLAP cubes with hierarchical dimensions and pre-aggregated measures. Superset works differently—it queries your underlying data warehouse directly. If your Sisense instance relies heavily on cubes, you’ll need to either:

1. Rebuild those cubes as materialised views in your warehouse.
2. Use Superset’s native data model (simpler, but less powerful).
3. Adopt a semantic layer tool like dbt or Looker to bridge the gap.

For most enterprises, option 1 (materialised views) is the path of least resistance. Your data engineering team can own this work in parallel with dashboard migration.

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Understanding the Architecture Differences

Sisense and Superset are fundamentally different architectures. Understanding these differences shapes your migration strategy.

Sisense Architecture

Sisense is a monolithic, embedded OLAP platform. It includes:

• A proprietary query engine optimised for complex analytical queries.
• An in-memory data model (ElastiCube) that pre-aggregates and caches data.
• Tight coupling between the UI, query layer, and data model.
• Built-in embedding and white-labelling capabilities.
• Per-user licensing tied to the application.

When you build a Sisense dashboard, you’re writing queries against a pre-built cube. The cube is refreshed on a schedule (hourly, daily, etc.). Query performance is typically fast because the data is pre-aggregated.

Apache Superset Architecture

Superset is a lightweight, modular analytics layer that sits on top of your data warehouse. It includes:

• A metadata layer that defines tables, columns, and relationships.
• A query abstraction layer (SQLAlchemy) that translates Superset logic into SQL.
• A Flask-based web application for UI and API.
• Pluggable authentication (LDAP, OAuth, SAML).
• Multi-tenancy and role-based access control (RBAC).
• Open-source code deployed on your infrastructure (or via managed services like Preset).

When you build a Superset dashboard, you’re writing SQL queries (or using the visual query builder) against your warehouse directly. There’s no pre-aggregation layer. Performance depends on your warehouse query optimisation and indexing.

Key Implications for Migration

1. Query performance: Superset queries hit your warehouse directly. You’ll need to invest in warehouse optimisation (indexing, materialised views, partitioning) to achieve Sisense-like performance.

2. Data freshness: Sisense cubes refresh on a schedule. Superset queries are always fresh. This is usually a win, but it means you need to manage warehouse query load more carefully.

3. Custom logic: Sisense supports custom JavaScript and plugins. Superset uses Python and SQL. If you have complex custom code, you’ll need to rewrite it.

4. Embedding: Superset’s embedding model is different. If you’re embedding dashboards in a product, you’ll need to use Superset’s embedding API and manage authentication differently.

5. Scalability: Superset scales horizontally (add more application servers). Sisense scales by throwing more hardware at the ElastiCube. For large enterprises, Superset is often more cost-effective.

For organisations pursuing platform engineering in Sydney and across Australia, this architectural shift is a feature, not a bug. It aligns analytics infrastructure with modern data stack practices and cloud-native deployment patterns.

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Cost Benchmarking and Financial Planning

Cost is often the driver for migration, but you need to be honest about what you’re actually saving.

Sisense Total Cost of Ownership

Sisense costs break down into:

• Licensing: Per-seat or per-user ($5K–$15K per user annually, depending on edition).
• Infrastructure: Virtual machines, storage, backup ($50K–$200K annually).
• Support and maintenance: Annual support contracts ($30K–$100K).
• Professional services: Implementation, training, custom development ($100K–$500K upfront).

For a 50-person team with moderate infrastructure:

• 50 users × $10K/year = $500K
• Infrastructure and support = $100K
• Total annual: ~$600K

Apache Superset Total Cost of Ownership

Superset costs break down into:

• Infrastructure: Kubernetes cluster, managed database, caching layer ($50K–$150K annually).
• Engineering time: Deployment, customisation, maintenance (1–2 FTE, $150K–$300K annually).
• Data warehouse optimisation: Indexes, materialised views, query tuning ($50K–$100K annually).
• Managed service (optional): Preset or similar managed Superset offering ($100K–$300K annually, depending on usage).

For the same 50-person team, self-hosted:

• Infrastructure = $80K
• Engineering time (1.5 FTE) = $225K
• Data warehouse optimisation = $75K
• Total annual: ~$380K

Self-hosted savings: ~$220K annually, or 37%.

If you use a managed service (Preset):

• Preset service = $150K
• Engineering time (0.5 FTE) = $75K
• Data warehouse optimisation = $75K
• Total annual: ~$300K

Managed service savings: ~$300K annually, or 50%.

The Hidden Costs

Honest conversations with CFOs require acknowledging what you’re not saving:

• Migration effort: 3–6 months of engineering time to migrate dashboards, rebuild data models, and train teams. Budget $200K–$500K.
• Warehouse optimisation: Superset queries hit your warehouse directly. You’ll need better indexing, partitioning, and possibly more warehouse capacity. Budget $50K–$150K in year one.
• Operational complexity: You’re now responsible for deploying, scaling, and maintaining Superset. This is cheaper than Sisense licensing, but it’s not free.
• Retraining: Your analytics team needs to learn Superset, SQL, and potentially Kubernetes. Budget training time and external consulting.

Break-even typically occurs in year 1–2. After that, the savings compound.

Financial Modelling Template

Use this template to model your specific situation:

Cost Category	Sisense (Annual)	Superset (Annual)	Difference
Licensing	$500K	$0	+$500K
Infrastructure	$100K	$80K	+$20K
Engineering	$50K	$225K	-$175K
Warehouse optimisation	$0	$75K	-$75K
Support and training	$50K	$25K	+$25K
Total Year 1	$700K	$405K	+$295K
Total Year 2+	$700K	$405K	+$295K

In this model, you break even in month 4 and save $295K annually thereafter.

For enterprises evaluating this trade-off, platform engineering teams across North America are increasingly choosing Superset for cost and operational control. The financial case is strong, but only if you’re prepared to invest in engineering capacity upfront.

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Governance and Data Security During Migration

If you’re moving to Superset to improve your security posture or pass SOC 2 / ISO 27001 audits, you need to bake governance into the migration plan from day one.

Access Control and Authentication

Superset supports multiple authentication methods:

• LDAP/Active Directory: Sync users and groups from your corporate directory.
• OAuth 2.0: Integrate with Okta, Azure AD, Google Workspace.
• SAML: Enterprise single sign-on.
• Database: Built-in user management (less ideal for large teams).

For most enterprises, LDAP or OAuth is the standard. During migration:

1. Audit all Sisense user accounts and access levels.
2. Map Sisense roles to Superset roles (Admin, Editor, Viewer, Guest).
3. Implement group-based access control (GBAC) so that access is managed via directory groups, not individual assignments.
4. Test authentication before cutover.

Row-Level Security (RLS)

If your Sisense dashboards use row-level security (e.g., sales reps see only their region’s data), you need to implement this in Superset too. Superset supports RLS via:

• SQL-based RLS: Add WHERE clauses based on the logged-in user.
• Database-level RLS: Use your warehouse’s native RLS (e.g., Snowflake’s dynamic data masking).
• Superset RBAC: Restrict datasets and dashboards to specific roles.

RLS is often the most complex part of migration. Plan 2–4 weeks for design and testing.

Audit Logging and Compliance

Superset logs all user actions (dashboard views, query executions, configuration changes) to a database. For SOC 2 and ISO 27001:

• Enable audit logging (it’s enabled by default).
• Route logs to a centralised SIEM or logging platform (Datadog, Splunk, etc.).
• Implement log retention policies (typically 1–2 years).
• Test log integrity and searchability.

When migrating, document:

• Which dashboards are accessed by whom (from Sisense audit logs).
• Who owns each dashboard (for governance).
• Which data sources are sensitive (PII, financial data, etc.).

This becomes your baseline for Superset access policies.

Data Lineage and Governance

Superset doesn’t include native data lineage (yet), but you can implement it via:

• dbt: Build dashboards on top of dbt models, which have built-in lineage.
• Apache Atlas or Collibra: Integrate Superset with a data governance platform.
• Custom metadata: Document data sources and transformations in a shared wiki or data dictionary.

For enterprises with strict governance requirements, platform engineering teams in regulated industries often pair Superset with dbt and a data governance layer. This provides complete lineage, transformation tracking, and audit trails.

Encryption and Network Security

During migration, ensure:

• Data in transit: All connections (Superset to warehouse, users to Superset) use TLS 1.2+.
• Data at rest: Warehouse data is encrypted (check your warehouse settings).
• Database credentials: Store warehouse credentials in a secrets manager (AWS Secrets Manager, HashiCorp Vault, etc.), not in Superset configuration files.
• Network isolation: Superset should only have outbound access to your warehouse, not to the internet.

If you’re self-hosting Superset, deploy it in a private VPC with restricted ingress (VPN, bastion host, or corporate network only).

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The Migration Playbook: Step-by-Step Execution

Now for the practical work. This is the phase where most teams either succeed or stumble. A structured playbook reduces risk.

Phase 1: Preparation (Weeks 1–2)

Objectives: Set up infrastructure, establish team structure, and run proof-of-concept.

Tasks:

1. Provision Superset infrastructure:

   • Deploy Superset on Kubernetes (or use Preset for managed hosting).
   • Configure authentication (LDAP, OAuth, or SAML).
   • Set up a separate database for Superset metadata.
   • Configure caching (Redis) for performance.

2. Establish a migration team:

   • Analytics lead (owns dashboard prioritisation and testing).
   • Data engineer (owns data model and warehouse optimisation).
   • Platform engineer (owns Superset deployment and infrastructure).
   • Project manager (owns timeline and communication).

3. Run a proof-of-concept:

   • Pick 2–3 high-value, low-complexity dashboards.
   • Recreate them in Superset.
   • Validate performance and functionality.
   • Document the process and lessons learned.

4. Publish a communication plan:

   • Announce the migration to stakeholders.
   • Share the timeline and what to expect.
   • Set expectations: dashboards may be unavailable during cutover.

Phase 2: Data Model and Warehouse Optimisation (Weeks 3–8)

Objectives: Rebuild your data model in Superset and optimise warehouse queries.

Tasks:

1. Define the Superset data model:

   • Create datasets (tables or views) for each major analytical area (sales, finance, operations, etc.).
   • Define columns, data types, and relationships.
   • Set up calculated fields (e.g., revenue = quantity × price).
   • Configure filters and drill-down dimensions.

2. Optimise warehouse queries:

   • Profile slow queries (use your warehouse’s query profiler).
   • Create indexes on frequently filtered columns.
   • Create materialised views for pre-aggregated data (replacing Sisense cubes).
   • Partition large tables by date or region.
   • Test query performance end-to-end.

3. Implement row-level security:

   • Define RLS rules for each dataset.
   • Test that users see only data they’re authorised to access.

4. Set up monitoring and alerting:

   • Monitor Superset CPU, memory, and query latency.
   • Set up alerts for slow queries or failed connections.

Phase 3: Dashboard Migration (Weeks 6–14)

Objectives: Migrate dashboards from Sisense to Superset in waves.

Tasks:

1. Batch dashboards by complexity and priority:

   • Wave 1 (Weeks 6–8): Low-complexity, high-value dashboards (20–30 dashboards).
   • Wave 2 (Weeks 9–11): Medium-complexity dashboards (15–25 dashboards).
   • Wave 3 (Weeks 12–14): High-complexity, custom-code dashboards (5–10 dashboards).

2. For each dashboard:

   • Recreate the dashboard in Superset (charts, filters, layout).
   • Validate data accuracy (compare Sisense and Superset side-by-side).
   • Test performance under load (if critical).
   • Document the dashboard (owner, refresh schedule, dependencies).
   • Get sign-off from the dashboard owner.

3. Migrate scheduled reports:

   • Set up email alerts and exports in Superset.
   • Test delivery and formatting.
   • Update distribution lists and schedules.

4. Handle custom code:

   • For simple JavaScript, rewrite as Superset custom visualisations or Python code.
   • For complex logic, consider building a custom Superset plugin (requires engineering effort).
   • For unmaintainable code, retire the dashboard.

Phase 4: User Acceptance Testing (Weeks 12–15)

Objectives: Validate that Superset dashboards meet business requirements.

Tasks:

1. Invite stakeholders to test:

   • Sales, finance, operations teams test dashboards in their domain.
   • Identify discrepancies between Sisense and Superset.
   • Gather feedback on usability and performance.

2. Fix issues:

   • Data accuracy issues (usually caused by incorrect filters or aggregations).
   • Performance issues (add indexes, optimise queries, increase caching).
   • Usability issues (improve dashboard layout, add tooltips, simplify filters).

3. Train users:

   • Record videos on how to use Superset (filtering, exporting, sharing).
   • Run live training sessions for each team.
   • Set up a Slack channel or wiki for questions.

Phase 5: Cutover (Week 16)

Objectives: Switch from Sisense to Superset with minimal disruption.

Tasks:

1. Freeze Sisense: No new dashboards or changes after a certain date (e.g., Friday EOD).

2. Final validation: Spot-check 10–20 critical dashboards to ensure data accuracy.

3. Update documentation and links:

   • Update wiki, email signatures, and internal docs with Superset links.
   • Redirect old Sisense links to Superset (if possible).

4. Go live:

   • Announce the switch to all users.
   • Monitor Superset closely for the first 24 hours (CPU, memory, error rates).
   • Have a rollback plan (see next section).

5. Decommission Sisense (after 1–2 weeks of parallel running):

   • Archive Sisense data and configurations.
   • Cancel Sisense licensing and support contracts.
   • Document lessons learned.

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Cutover Patterns and Rollback Strategy

Cutover is where things get real. You need a plan for what happens if something breaks.

Cutover Patterns

Pattern 1: Big Bang Cutover (all dashboards at once)

• Pros: Clean break, faster decommissioning of Sisense.
• Cons: High risk, difficult to debug issues, hard to rollback.
• Best for: Small teams (<20 dashboards) or very low-risk migrations.

Pattern 2: Phased Cutover (by team or business unit)

• Pros: Lower risk per team, easier to debug, can pause if needed.
• Cons: Longer overall timeline, need to maintain both systems in parallel.
• Best for: Most enterprises (recommended).

Pattern 3: Parallel Running (both systems live for weeks)

• Pros: Maximum safety, users can compare results, easier to debug.
• Cons: Expensive, confusing for users, hard to coordinate updates.
• Best for: Mission-critical dashboards where data accuracy is paramount.

For most enterprises, phased cutover by team is the sweet spot. Cut over finance first (3–5 days), then sales (3–5 days), then operations, etc. This spreads risk and allows you to debug issues before moving to the next team.

Rollback Strategy

If something goes wrong during cutover, you need to be able to roll back quickly. Plan for:

1. Keep Sisense running for at least 1 week after Superset cutover.

2. Document rollback triggers: If X happens, we roll back immediately (e.g., >10% of dashboards showing incorrect data).

3. Establish a rollback procedure:

   • Notify all users that we’re rolling back.
   • Switch DNS or load balancer to point back to Sisense.
   • Investigate the issue (don’t rush).
   • Fix the issue in Superset.
   • Retry cutover 1–2 days later.

4. Test rollback before cutover. Actually switch back to Sisense and make sure it works.

Monitoring During Cutover

Set up aggressive monitoring for the first 48 hours:

• Application metrics: CPU, memory, request latency, error rates.
• Database metrics: Query latency, slow query log, connection pool utilisation.
• Business metrics: Number of users logged in, dashboards viewed, queries executed.
• Data quality: Spot-check 10–20 critical metrics (revenue, user count, etc.) against Sisense.

Have someone on-call 24/7 for the first 48 hours. Seriously.

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Post-Migration Optimisation and Operations

Migration isn’t done when you flip the switch. You’ve got months of optimisation ahead.

Week 1–2: Stabilisation

• Monitor error rates and performance metrics closely.
• Fix bugs and data accuracy issues as they arise.
• Answer user questions and provide support.
• Document issues and resolutions for future reference.

Week 3–4: Optimisation

Once things are stable, start optimising:

1. Query performance:

   • Identify slow dashboards (use Superset’s query profiler).
   • Add indexes or materialised views to the warehouse.
   • Increase caching TTL for non-real-time dashboards.
   • Consider pre-computing heavy queries.

2. User experience:

   • Gather feedback from users.
   • Improve dashboard layouts and filters.
   • Create templates for common dashboard types.
   • Build a self-service analytics capability (users can create their own dashboards).

3. Operational efficiency:

   • Automate Superset backups and disaster recovery.
   • Set up alerting for infrastructure issues.
   • Document runbooks for common operations (scaling, upgrades, troubleshooting).

Month 2+: Continuous Improvement

• Establish a cadence for dashboard reviews (quarterly).
• Retire unused dashboards.
• Invest in advanced features (custom visualisations, plugins, API integrations).
• Plan for Superset upgrades (new versions are released every 3–4 months).

Operational Handover

Transition from a project team to an operational team:

• Platform team: Owns Superset infrastructure, scaling, and upgrades.
• Analytics team: Owns dashboards, data quality, and user support.
• Data engineering team: Owns data model optimisation and warehouse queries.

Define clear ownership and escalation paths. Set up a Slack channel for cross-team coordination.

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Real-World Migration Timelines and Lessons

What does this actually look like in practice? Here are timelines from real migrations.

Case Study 1: SaaS Company (50 Dashboards, 100 Users)

Timeline: 14 weeks

• Week 1–2: Infrastructure setup, PoC (2 dashboards).
• Week 3–6: Data model design, warehouse optimisation.
• Week 6–10: Dashboard migration (Wave 1 and 2).
• Week 11–13: UAT and user training.
• Week 14: Cutover (phased by team over 1 week).
• Week 15+: Stabilisation and optimisation.

Cost: $250K (1.5 FTE engineers for 14 weeks + infrastructure).

Lessons:

• Underestimated warehouse optimisation (took 3 weeks instead of 2).
• Dashboard owners were slow to test and sign off (add 1 week buffer).
• Superset’s performance was excellent after indexing; no surprises.

Case Study 2: Enterprise Financial Services (200 Dashboards, 500 Users)

Timeline: 24 weeks

• Week 1–2: Infrastructure, PoC, team setup.
• Week 3–10: Data model design, RLS implementation, warehouse optimisation.
• Week 8–18: Dashboard migration (3 waves, 60–70 dashboards per wave).
• Week 19–22: UAT, training, parallel running with Sisense.
• Week 23–24: Phased cutover (by department over 2 weeks).
• Week 25+: Stabilisation and decommissioning.

Cost: $600K (2–3 FTE engineers for 24 weeks + infrastructure + managed Superset service).

Lessons:

• RLS implementation was the biggest bottleneck (8 weeks for design and testing).
• Parallel running with Sisense for 2 weeks was invaluable for confidence.
• User adoption was slower than expected; needed more training and change management.
• Warehouse queries were initially slow; took 3 weeks of indexing and partitioning to match Sisense performance.

Common Pitfalls

1. Underestimating scope: “We have 50 dashboards” becomes “We have 150 dashboards when you include embedded, archived, and one-off reports.” Audit thoroughly.

2. Ignoring data model complexity: Sisense cubes are powerful. If you don’t rebuild them properly in Superset, queries will be slow. Plan 4–6 weeks for warehouse optimisation.

3. Rushing training: Users need time to learn Superset. Allocate 2–4 weeks for training and support. This pays for itself in reduced support tickets.

4. Forgetting about custom code: JavaScript, plugins, and custom logic in Sisense are hard to port. Identify these early and decide whether to rewrite, retire, or accept limitations.

5. Underestimating RLS complexity: If your Sisense dashboards use row-level security, budget 6–8 weeks for RLS design and testing in Superset.

6. Not planning for rollback: Assume something will go wrong. Keep Sisense running for at least 1 week after cutover. Test rollback before you need it.

For teams navigating complex migrations, platform engineering expertise across multiple geographies is invaluable. Experienced teams can help you avoid these pitfalls and compress timelines by 20–30%.

According to the Apache Superset documentation on introduction and capabilities, many organisations find that the migration effort is worth it for the operational control and cost savings. The official Apache Superset GitHub repository shows active development and a large community, which provides confidence in the platform’s future.

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Next Steps and Getting Help

You now have a roadmap for migrating from Sisense to Superset. Here’s how to move forward.

If You’re Ready to Start

1. Conduct a detailed audit of your Sisense estate (dashboards, data sources, users, custom code). Use the spreadsheet template from the Pre-Migration Assessment section.

2. Model the financial case using the TCO template. Be honest about engineering costs and warehouse optimisation effort.

3. Assemble a migration team: Analytics lead, data engineer, platform engineer, project manager. Allocate 3–6 months of their time.

4. Run a proof-of-concept: Pick 2–3 dashboards and recreate them in Superset. Learn what works and what doesn’t before committing to full migration.

5. Build a detailed project plan using the migration playbook as a template. Adjust timelines based on your scope and team capacity.

If You Need External Support

Migrating from Sisense to Superset is complex. If you don’t have the internal bandwidth or expertise, consider partnering with a platform engineering team.

PADISO provides platform development and engineering services across Australia and the United States, including analytics infrastructure migration. We’ve helped organisations in financial services, retail, and media migrate from legacy BI tools to modern, cost-effective stacks.

Our approach:

• Assessment: We audit your Sisense estate and model the financial case.
• Design: We design the target Superset architecture, data model, and governance policies.
• Migration: We execute the migration in phases, with your team, reducing risk and building internal capability.
• Optimisation: We optimise warehouse queries, implement RLS, and set up monitoring.
• Handover: We document everything and train your team to operate Superset independently.

We’ve also worked with teams building embedded analytics into SaaS products, which is where Superset’s API and multi-tenancy really shine.

If you’re in Sydney, we’re based locally and understand Australian regulatory requirements (ASIC, APRA, privacy legislation). If you’re in North America, we have teams in Dallas, New York, Chicago, Austin, Denver, Atlanta, Los Angeles, and Seattle.

We also serve Toronto and Melbourne.

Further Reading

For deeper technical details, refer to:

• Apache Superset’s official documentation for platform capabilities and configuration.
• Sisense’s official documentation on migrating Sisense in Linux environments for backup, restore, and asset export procedures.
• Case studies on migrating to Apache Superset from other BI tools for practical lessons.
• Engineering case study: migrating from Google Data Studio to Apache Superset for real-world insights.
• Apache Superset as an alternative to Looker for enterprise analytics tradeoffs and deployment considerations.

Final Checklist

Before you start, make sure you have:

• Executive sponsorship and budget approval.
• A dedicated project manager.
• A team of engineers (analytics, data, platform).
• A detailed audit of your Sisense estate.
• A financial model showing ROI.
• A risk register and mitigation plan.
• A communication plan for stakeholders.
• A rollback plan and contingency budget.
• Buy-in from dashboard owners and end users.

Migrating from Sisense to Superset is a significant undertaking, but the payoff is substantial: lower costs, better operational control, and a modern analytics stack that scales with your business. With a solid plan and the right team, you’ll be live in 12–24 weeks and realising savings within the first year.

Ready to get started? Contact PADISO to discuss your migration strategy or explore our case studies to see how we’ve helped other organisations navigate similar transformations.