Migrating from Sisense to Superset for Mid-Market Organisations

Table of Contents

1. Why Mid-Market Teams Are Moving to Superset
2. The Business Case: Cost, Speed, and Control
3. Scoping Your Migration: The Foundation
4. Architecture and Governance Setup
5. The Technical Migration Path
6. Cost Benchmarks and Budgeting
7. Cutover Strategy and Execution
8. Post-Migration Operations and Scaling
9. Common Pitfalls and How to Avoid Them
10. Next Steps and Getting Started

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Why Mid-Market Teams Are Moving to Superset

Sisense has served mid-market organisations well for years. Its ElastiCube engine, drag-and-drop interface, and embedded analytics capabilities made it a natural choice for companies outgrowing basic BI tools. But at the mid-market scale—typically 50–500 employees with 20–200 analytics users—the per-seat licensing model, vendor lock-in, and operational overhead become friction points.

Apache Superset, by contrast, is an open-source analytics and visualisation platform that offers mid-market teams three things Sisense increasingly doesn’t: cost predictability, architectural control, and the ability to embed analytics directly into product. When you’re managing a modernisation programme, platform re-platforming, or rapid scaling, Superset’s flexibility becomes a competitive advantage.

We’ve worked with teams across financial services, retail, and SaaS rebuilding their analytics stacks. The pattern is consistent: Sisense works until it doesn’t. The moment you need custom SQL, real-time dashboards at scale, or embedded analytics without per-seat costs, the migration conversation starts. This guide walks you through it.

The Real Economics of the Switch

A mid-market organisation running 100 Sisense users at £400–600 per seat annually is spending £40,000–60,000 per year on licensing alone. Add infrastructure, support, and custom development, and you’re at £80,000–120,000 annually. Superset, deployed on managed cloud infrastructure (AWS, GCP, Azure), typically costs £15,000–35,000 per year for equivalent scale, including compute, storage, and operational overhead.

That’s a 50–70% cost reduction. More importantly, it’s a shift from vendor-dependent licensing to infrastructure costs you control. You own the code, the configuration, and the deployment. This matters when you’re moving fast.

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The Business Case: Cost, Speed, and Control

Migrating from Sisense to Superset isn’t just about cutting costs. It’s about enabling your engineering and analytics teams to move faster, integrate analytics into your product, and reduce dependency on a single vendor.

Cost Transparency and Predictability

Sisense’s per-seat model creates a perverse incentive: as your organisation grows and you add analytics users, costs rise linearly. Superset’s consumption-based model—where you pay for compute, storage, and data transfer—scales with actual usage, not headcount. A team of 200 users running lightweight dashboards costs far less than 200 users running heavy OLAP queries. This alignment between cost and value is powerful.

We’ve seen teams reduce their analytics spend by £40,000+ annually while improving performance and user adoption. That capital can be reinvested in analytics talent, better data infrastructure, or product features.

Speed to Insight

Sisense’s ElastiCube model requires pre-aggregation and cube building. This works well for static, predictable queries but creates bottlenecks when you need ad-hoc analysis, real-time dashboards, or rapid iteration. Superset connects directly to your data warehouse (Snowflake, BigQuery, Redshift, ClickHouse) and supports SQL-based queries, making it ideal for teams that need flexibility.

Migration timelines typically run 8–16 weeks for mid-market organisations, depending on complexity. During that window, your team gains the ability to author dashboards without waiting for cube refreshes, iterate on metrics in hours instead of days, and push analytics into product surfaces.

Architectural Control and Product Embedding

One of the biggest wins we see is the ability to embed analytics without per-seat licensing. If you’re building a SaaS product and want to give customers dashboards, Superset’s embedded analytics capability—combined with its REST API and Python SDK—makes that feasible at scale. Sisense’s embedded model requires additional licensing and is often cost-prohibitive for product teams.

For mid-market SaaS companies, this unlocks a new revenue stream or product feature. For enterprises modernising internal tools, it means your product teams can ship analytics features without negotiating with finance.

Vendor Independence and Long-Term Flexibility

Open-source platforms carry less risk of sudden pricing changes, feature deprecation, or vendor acquisition disruption. Superset is backed by the Apache Software Foundation and has a large, active community. If your needs evolve—you want to integrate with a new data source, customise the UI, or move infrastructure—you’re not blocked by vendor roadmaps.

This is especially valuable for mid-market organisations planning 3–5 year technology roadmaps. You’re building on a foundation you control.

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Scoping Your Migration: The Foundation

A successful migration starts with rigorous scoping. Many organisations underestimate the effort because they focus only on the technical lift—moving dashboards and datasets—and miss the governance, data quality, and change management work.

Step 1: Audit Your Current State

Begin by documenting what you’re actually running in Sisense. This includes:

Dashboards and Reports

• Total count and active usage (how many users view each dashboard weekly?)
• Complexity: number of visualisations, filters, drill-downs, and custom calculations
• Embedded dashboards vs. standalone dashboards
• Scheduled reports and email distributions
• Mobile usage and requirements

Data Sources and Connectivity

• All connected databases, data warehouses, and APIs
• ElastiCube definitions and refresh schedules
• Custom transformations or calculations within Sisense
• Data quality issues or known gaps

User Base and Permissions

• Total active users and user segments (executives, analysts, operations)
• Role-based access control (RBAC) rules and how they map to your organisation
• Dashboard ownership and support responsibilities

Custom Development

• Custom plugins, extensions, or integrations
• Scheduled jobs or automation
• API integrations with downstream systems

This audit typically takes 2–4 weeks for a mid-market organisation and should involve your analytics team, BI developers, and data engineering leadership. Document everything in a spreadsheet or database. This becomes your migration inventory.

Step 2: Identify Migration Tiers

Not all dashboards are equal. Tier them by business criticality and complexity:

Tier 1: Critical Path Dashboards used daily by revenue-facing teams, operations, or executive leadership. These must work flawlessly on day one of cutover. Examples: sales pipeline, financial reporting, operational KPIs.

Tier 2: Important but Non-Critical Dashboards used weekly or monthly by functional teams. These can migrate in the first 2–4 weeks post-cutover. Examples: marketing performance, HR analytics, product metrics.

Tier 3: Nice-to-Have Historical or exploratory dashboards with low usage. These can migrate later or be archived. Examples: one-off analyses, deprecated reports, experimental dashboards.

Tier 1 dashboards should represent 20–30% of your total count but 70–80% of your usage. This focus makes the migration manageable and reduces cutover risk.

Step 3: Define Your Data Architecture

Superset requires a different data architecture than Sisense. Rather than pre-aggregated ElastiCubes, Superset queries your data warehouse directly. This means you need:

• A modern data warehouse: Snowflake, BigQuery, Redshift, or ClickHouse. If you don’t have one, you’ll need to build it as part of the migration.
• Semantic layer or dbt models: Superset works best with a well-defined semantic layer—either via Superset’s native data model or via dbt. This ensures consistent metric definitions across dashboards and reduces query complexity.
• Query optimisation: Superset queries can be slow if your warehouse isn’t optimised. You’ll need to review indexing, partitioning, and query patterns.

If you’re currently using Sisense with a traditional data warehouse, you likely already have most of this. If you’re using Sisense’s ElastiCube with a relational database, you’ll need to either migrate to a cloud data warehouse or implement a semantic layer in Superset.

Step 4: Estimate Effort and Timeline

For a mid-market organisation with 50–100 active dashboards, a typical migration timeline is:

• Weeks 1–2: Assessment and planning
• Weeks 3–6: Data architecture setup and Superset infrastructure deployment
• Weeks 7–12: Dashboard migration (Tier 1 and 2)
• Weeks 13–16: Testing, optimisation, and cutover
• Weeks 17+: Post-cutover support and Tier 3 migration

Total effort: 400–800 hours for a team of 3–5 people (internal plus external support). Budget 12–16 weeks for a full migration, with parallel operations running for 4–8 weeks during cutover.

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Architecture and Governance Setup

Before you migrate a single dashboard, you need to establish the governance and architectural foundation that will support your analytics at scale.

Choosing Your Deployment Model

Superset can be deployed in several ways:

Managed Superset (Preset) Preset is a commercial managed service built on Apache Superset. You get hosting, upgrades, and support without operational overhead. Cost: typically £1,500–5,000 per month depending on usage. Timeline: days to weeks. Best for: teams that want Superset’s flexibility without ops burden.

Self-Hosted on Kubernetes Deploy Superset on your existing Kubernetes infrastructure (EKS, GKE, AKS). You manage updates, scaling, and monitoring. Cost: £500–1,500 per month for compute plus ops time. Timeline: 4–8 weeks to set up properly. Best for: teams with strong Kubernetes expertise and existing K8s infrastructure.

Self-Hosted on Docker or Virtual Machines Simpler than Kubernetes but less scalable. Cost: £200–500 per month for compute. Timeline: 2–4 weeks. Best for: small teams or proof-of-concepts, not production mid-market deployments.

For mid-market organisations, we typically recommend either Preset (if ops overhead is a concern) or Kubernetes (if you have the expertise and want full control). Self-hosted on VMs is usually a false economy—you save money initially but pay for it in operational complexity later.

Data Governance and Semantic Layer

Sisense’s ElastiCube approach centralises data logic. Superset distributes it across your data warehouse and dashboard definitions. This requires a clear governance model:

Option 1: dbt Semantic Layer Use dbt to define your metrics, dimensions, and data models. Superset queries these dbt models. This ensures consistency and makes metrics reusable across dashboards. Setup: 4–8 weeks. Maintenance: ongoing as your data evolves.

Option 2: Superset Native Data Models Define datasets, metrics, and calculations within Superset. This is simpler to set up but less portable and harder to version control.

Option 3: Hybrid Use dbt for core metrics and dimensions, Superset for dashboard-specific calculations. This balances flexibility and governance.

We recommend Option 1 (dbt) for mid-market organisations because it separates data logic from presentation, makes metrics reusable, and enables better version control. If you don’t already have dbt, implementing it is a 6–10 week project that pays for itself quickly through reduced dashboard maintenance.

Role-Based Access Control (RBAC)

Superset supports RBAC via role definitions and row-level security (RLS). Map your Sisense roles to Superset:

• Admin: Full access to all dashboards, data sources, and settings
• Editor: Can create and edit dashboards, but only for assigned datasets
• Viewer: Read-only access to assigned dashboards
• Analyst: Can create ad-hoc queries and dashboards, but only on approved data sources

Implement row-level security for sensitive data (e.g., sales data filtered by region, financial data filtered by department). This requires coordination with your data engineering team to add RLS filters in your data warehouse.

Security and Compliance

If you’re subject to SOC 2 or ISO 27001 compliance requirements, Superset’s architecture needs to support audit-readiness. Key considerations:

• Authentication: Integrate with your SSO provider (Okta, Azure AD, Google Workspace) for centralised identity management
• Audit logging: Enable Superset’s audit logs to track dashboard access, modifications, and data exports
• Encryption: Use TLS for data in transit and encryption at rest for sensitive data
• Data access controls: Implement RLS and dataset-level permissions to prevent unauthorised data access
• Infrastructure security: Run Superset in a private VPC with no public internet access, use managed services where possible

For organisations pursuing formal compliance, we recommend working with a platform engineering partner experienced in SOC 2 compliance and analytics infrastructure. The governance setup can mean the difference between a smooth audit and a stressful one.

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The Technical Migration Path

Once your architecture and governance are in place, the actual migration follows a predictable pattern.

Phase 1: Data Source Migration

Start by connecting Superset to your data warehouse. This is usually straightforward—Superset supports Apache Superset Documentation for all major warehouses (Snowflake, BigQuery, Redshift, ClickHouse, Postgres).

1. Create database connection: Add your data warehouse as a data source in Superset
2. Test connectivity: Run a simple query to verify the connection works
3. Set up credentials: Use IAM roles or service accounts rather than static credentials
4. Configure caching: Set up Superset’s caching layer to improve query performance
5. Document data lineage: Map which Sisense data sources correspond to which Superset datasets

This phase typically takes 1–2 weeks and should be completed before dashboard migration begins.

Phase 2: Dataset and Metric Definition

Create Superset datasets that correspond to your Sisense data sources and ElastiCubes. For each dataset:

1. Define the base query: Write the SQL that pulls data from your warehouse
2. Add calculated columns: Create derived metrics (e.g., revenue per customer, conversion rate)
3. Set up filters: Define available filters for dashboards using this dataset
4. Configure caching: Set appropriate cache expiration times based on data freshness requirements
5. Document the dataset: Add descriptions and owner information for governance

If you’re using dbt, this step is simpler—Superset can auto-discover dbt models and use them as datasets.

Phase 3: Dashboard Migration

Now migrate your Tier 1 dashboards. This is where the real work happens.

For each dashboard:

1. Recreate the structure: Add visualisations to a new Superset dashboard in the same layout as the original
2. Migrate visualisations: For each chart, recreate it in Superset using the equivalent visualisation type
   • Sisense table → Superset table
   • Sisense bar chart → Superset bar chart
   • Sisense pivot table → Superset pivot table or custom SQL query
3. Adjust queries: Superset uses SQL, so translate Sisense’s visual query builder into SQL. This is usually straightforward but requires SQL knowledge
4. Recreate filters and drill-downs: Set up dashboard filters that match the original behaviour
5. Test and validate: Compare the original Sisense dashboard with the new Superset version side-by-side

For mid-market organisations, this phase is typically 8–12 weeks for Tier 1 and 2 dashboards. Complexity varies:

• Simple dashboards (4–6 visualisations, basic filters): 8–16 hours each
• Moderate dashboards (8–15 visualisations, multiple filters, drill-downs): 24–40 hours each
• Complex dashboards (15+ visualisations, custom calculations, embedded logic): 40–80 hours each

Parallelise this work across your team. If you have 2–3 BI developers, you can migrate 10–15 dashboards per week.

Phase 4: Data Quality and Validation

As you migrate, validate that numbers match. This is critical and often overlooked.

1. Compare row counts: Ensure the migrated dashboard shows the same number of records as the original
2. Validate metrics: Compare key metrics (revenue, users, conversion rate) side-by-side
3. Check drill-downs: Verify that drill-down paths and filters work correctly
4. Test edge cases: Check how dashboards handle null values, zero values, and extreme values
5. Performance test: Ensure query times are acceptable (typically <10 seconds for most queries)

Data quality issues often surface here. Common problems:

• Metric calculation differences: Sisense and Superset may calculate aggregations differently (e.g., how they handle null values in sums)
• Timezone issues: Dates and times may be calculated differently if timezone handling isn’t consistent
• Rounding differences: Sisense and SQL may round differently
• Filter logic: Complex filters may not translate exactly

Allocate 20–30% of your migration effort to data validation and bug fixes.

Phase 5: Performance Optimisation

Once dashboards are migrated, optimise query performance. This is where Superset’s flexibility shines compared to Sisense.

1. Profile slow queries: Identify which dashboards or visualisations are slow
2. Optimise SQL: Rewrite queries to use indexes, partitions, and aggregate tables
3. Implement caching: Cache frequently-accessed datasets to reduce warehouse load
4. Use aggregate tables: For heavy dashboards, pre-aggregate data in your warehouse
5. Tune infrastructure: Scale Superset’s compute or database connections if needed

A well-optimised Superset deployment typically sees 30–50% faster query times compared to the equivalent Sisense setup, because you’re querying your warehouse directly rather than through pre-aggregated cubes.

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Cost Benchmarks and Budgeting

Understanding the true cost of migration and ongoing operations is critical for getting stakeholder buy-in.

Migration Costs

For a mid-market organisation migrating 50–100 dashboards:

Component	Cost Range	Notes
Planning and scoping	£8,000–15,000	4–6 weeks, internal + external
Infrastructure setup	£5,000–12,000	Data warehouse, Superset deployment, networking
Dashboard migration	£30,000–60,000	50–100 dashboards at £300–600 each
Data architecture (dbt, semantic layer)	£10,000–25,000	Optional but recommended
Testing and validation	£8,000–15,000	4–6 weeks
Training and change management	£5,000–10,000	User training, documentation
Contingency (10–15%)	£10,000–20,000	Unexpected complexity
TOTAL	£76,000–157,000	Typical range for mid-market

This is a one-time cost. For a mid-market organisation currently spending £80,000–120,000 annually on Sisense, the migration pays for itself in 9–18 months through licensing savings alone.

Ongoing Operational Costs

Post-migration, your annual analytics spend typically looks like:

Component	Cost Range	Notes
Superset hosting (Preset or self-hosted)	£15,000–35,000	Depends on deployment model and scale
Data warehouse compute	£20,000–50,000	Snowflake, BigQuery, etc. (shared with other workloads)
Ops and maintenance	£10,000–25,000	1 FTE for mid-market deployment
New dashboard development	£15,000–30,000	1–2 FTE for analytics engineering
TOTAL	£60,000–140,000	Typical range for mid-market

This is a 30–50% reduction compared to Sisense. More importantly, costs are now driven by actual usage and infrastructure needs, not per-seat licensing.

Cost Optimisation Opportunities

After migration, several optimisations can further reduce costs:

1. Shared data warehouse: If your data warehouse is shared with other workloads (data science, product analytics), negotiate cost allocation. Superset typically accounts for 10–20% of total warehouse spend.
2. Aggregate tables: Pre-compute heavy aggregations to reduce query costs. This trades storage for compute savings.
3. Query caching: Aggressive caching can reduce warehouse queries by 40–60%, cutting costs significantly.
4. Archive old dashboards: Remove dashboards with <1 user per month. This reduces maintenance burden and query load.
5. Right-size infrastructure: As you optimise queries, you may need less Superset compute. Review monthly and adjust.

We typically see mid-market organisations reduce their analytics spend by 40–60% within 12 months of migration, after accounting for migration costs.

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Cutover Strategy and Execution

The cutover—switching from Sisense to Superset—is the highest-risk phase of the migration. Get this wrong and you lose user trust and business continuity.

Cutover Patterns

There are three main cutover approaches:

Big Bang Cutover Switch all dashboards from Sisense to Superset on a single date. Pros: simple, fast. Cons: high risk, requires extensive testing.

Best for: organisations with <30 critical dashboards and strong change management.

Phased Cutover (Recommended) Switch dashboard tiers in waves over 4–8 weeks. Week 1: Tier 1 dashboards. Week 2–3: Tier 2. Week 4+: Tier 3 and archive.

Pros: lower risk, allows time to fix issues, builds user confidence. Cons: requires parallel operations and more coordination.

Best for: mid-market organisations with 50+ dashboards.

Parallel Operations Run both Sisense and Superset simultaneously for 4–12 weeks. Users gradually migrate to Superset. Sisense is decommissioned once all dashboards are migrated.

Pros: zero business risk, users control their migration pace. Cons: expensive (paying for both platforms), requires careful change management.

Best for: risk-averse organisations or those with complex, mission-critical dashboards.

For most mid-market organisations, we recommend phased cutover with 4 weeks of parallel operations for critical dashboards.

Pre-Cutover Checklist

Before you switch on a single dashboard:

• All Tier 1 dashboards migrated and validated
• Data matches Sisense within acceptable tolerance (<1% variance)
• Performance is acceptable (queries <10 seconds)
• RBAC and permissions configured correctly
• Backup and disaster recovery tested
• User training completed (at least 80% of users)
• Support team trained and on standby
• Incident response plan documented
• Rollback plan documented and tested
• Executive stakeholders briefed and aligned

Cutover Day Execution

For Tier 1 dashboard cutover:

1. Announce the change: Send communication to all users 1 week before, 1 day before, and on the day
2. Monitor closely: Have your support team and BI developers standing by for 8 hours post-cutover
3. Verify access: Spot-check that key users can access dashboards and see correct data
4. Track issues: Use a shared incident log to track and resolve problems
5. Communicate status: Send updates to stakeholders every 2 hours for the first 8 hours
6. Prepare rollback: If critical issues arise, be ready to roll back to Sisense within 30 minutes

Most organisations complete Tier 1 cutover without major issues. The key is preparation and standing by for problems.

Post-Cutover Support

For 2–4 weeks after cutover, maintain elevated support:

• Daily standups: Review issues, trends, and user feedback
• Weekly optimisation: Identify slow dashboards and optimise queries
• User feedback loops: Actively solicit feedback and iterate on dashboard design
• Documentation updates: Update internal documentation and runbooks
• Training refreshers: Offer optional training sessions for users who missed the initial training

After 4 weeks, most organisations report that user adoption and satisfaction exceed their expectations.

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Post-Migration Operations and Scaling

Migration is not the end—it’s the beginning. Your Superset deployment needs to evolve with your business.

Operational Excellence

Establish clear operational practices:

1. Dashboard governance: Define who can create dashboards, naming conventions, and approval workflows
2. Metric definitions: Maintain a living document of metric definitions to ensure consistency
3. Data freshness SLAs: Define how fresh data needs to be for different use cases
4. Query performance budgets: Set maximum acceptable query times and monitor against them
5. Regular audits: Monthly reviews of dashboard usage, performance, and cost

If you’re pursuing compliance (SOC 2, ISO 27001), these practices support your audit readiness. We’ve helped teams across Australia and internationally establish governance frameworks that pass audits on the first try via tools like Vanta.

Scaling Analytics

As your organisation grows, your analytics needs will too. Superset scales in several dimensions:

User growth: Superset handles thousands of concurrent users. Monitor connection pool size and cache hit rates.

Data growth: As your data warehouse grows, queries may slow. Implement aggregate tables, partitioning, and query optimisation.

Dashboard proliferation: As teams create more dashboards, governance becomes critical. Implement clear naming conventions, ownership, and archival policies.

Embedded analytics: If you’re embedding dashboards in your product, Superset’s API and SDK make this straightforward. Plan for security (API keys, SSO integration) and performance (query caching).

Team Structure and Skills

A mature Superset deployment typically requires:

• Analytics Engineer (1 FTE): Owns data models, dbt, and metric definitions
• BI Developer (1–2 FTE): Creates and maintains dashboards
• Data Engineer (shared): Maintains data warehouse and optimises queries
• Platform Engineer (0.5 FTE): Manages Superset infrastructure and upgrades

For mid-market organisations, this is often 2–3 people. Compare that to Sisense, which typically requires 1–2 people but limits your flexibility.

Continuous Improvement

Superset is actively developed. New features ship regularly. Plan for:

• Quarterly upgrades: Stay current with bug fixes and security patches
• Feature adoption: Review new features quarterly and adopt those that add value
• Community engagement: Monitor the Apache Superset Documentation and community forums for best practices
• Tool integration: As your analytics stack evolves, integrate new tools (dbt, data quality platforms, etc.)

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Common Pitfalls and How to Avoid Them

We’ve seen many migrations succeed and fail. Here are the common pitfalls and how to avoid them.

Pitfall 1: Underestimating Effort

Most organisations estimate 4–6 weeks for migration. Reality is 12–16 weeks. The gap comes from:

• Data quality issues that surface during validation
• Complex dashboard logic that doesn’t translate directly
• Governance and training work that gets deferred
• Infrastructure setup taking longer than expected

How to avoid: Add 30–50% contingency to your timeline. Plan for 16 weeks, hope for 12.

Pitfall 2: Ignoring Data Architecture

Organisations often try to migrate dashboards without fixing the underlying data architecture. This leads to slow queries, inconsistent metrics, and user frustration.

How to avoid: Invest in data architecture upfront. If you don’t have dbt, implement it before dashboard migration. If your data warehouse isn’t optimised, optimise it first.

Pitfall 3: Insufficient User Training

Users expect Superset to work exactly like Sisense. When it doesn’t, they get frustrated. Many migrations fail because users weren’t trained adequately.

How to avoid: Budget 2–4 weeks for user training. Train on the dashboard interface, how to create filters, how to export data, and how to request new dashboards. Offer multiple training sessions and create video tutorials.

Pitfall 4: Not Planning for Parallel Operations

Organisations often decommission Sisense too early, before all dashboards are migrated and users are comfortable. This forces a disruptive cutover.

How to avoid: Plan for 4–8 weeks of parallel operations. Keep Sisense running until all Tier 1 and 2 dashboards are migrated and users are confident.

Pitfall 5: Inadequate Testing

Data mismatches, performance issues, and security problems surface after cutover because testing was insufficient.

How to avoid: Allocate 20–30% of migration effort to testing. Compare numbers side-by-side, test filters and drill-downs, load test with realistic user volumes, and test security controls.

Pitfall 6: Lack of Executive Sponsorship

Migrations require cross-functional coordination and trade-offs. Without executive sponsorship, they stall.

How to avoid: Get a senior executive (CFO, CTO, VP of Data) to sponsor the migration. Have them communicate the rationale and timeline to the organisation. Check in weekly.

Pitfall 7: Not Planning for Ongoing Support

After cutover, teams assume everything will run smoothly. When issues arise, there’s no support structure.

How to avoid: Plan for 4 weeks of elevated support post-cutover. Have your team standing by to fix issues and optimise performance. After 4 weeks, transition to normal support.

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Next Steps and Getting Started

If you’re considering migrating from Sisense to Superset, here’s how to get started.

Assess Your Readiness

Before committing to migration, assess whether Superset is the right choice for your organisation:

1. Do you have a modern data warehouse? (Snowflake, BigQuery, Redshift, ClickHouse)
   • If no, Superset may not be the right choice. Consider Metabase or Tableau instead.
2. Do you have SQL expertise on your team?
   • If no, you’ll need to hire or partner with external support.
3. Are you willing to invest in governance and data architecture?
   • If no, you may be better off staying with Sisense.
4. Do you have 12–16 weeks and £100k+ to invest?
   • If no, consider a phased approach or smaller pilot.

If you answered yes to most of these, Superset is likely a good fit.

Run a Proof of Concept

Before committing to full migration, run a 4–6 week POC:

1. Select 5–10 representative dashboards (mix of simple and complex)
2. Migrate them to Superset (either self-hosted or Preset)
3. Validate data and performance
4. Get user feedback
5. Document lessons learned

A POC costs £15,000–25,000 and gives you real data to make a migration decision.

Build Your Team

You’ll need a core migration team:

• Migration lead (internal): Owns the overall programme
• BI developer (internal): Leads dashboard migration
• Data engineer (internal): Manages data architecture
• External partner (optional): Provides expertise and accelerates timeline

For mid-market organisations, we typically recommend bringing in external support for 50% of the effort. This accelerates the timeline and brings best practices.

Develop Your Migration Plan

Once you’ve committed, develop a detailed migration plan:

1. Weeks 1–2: Assessment and planning (you are here)
2. Weeks 3–6: Infrastructure setup and data architecture
3. Weeks 7–12: Dashboard migration (Tier 1 and 2)
4. Weeks 13–16: Testing, optimisation, and cutover
5. Weeks 17+: Post-cutover support and Tier 3 migration

Assign owners for each phase. Update the plan weekly. Share progress with stakeholders.

Engage a Partner

If you don’t have internal expertise, consider engaging a platform engineering partner. At PADISO, we’ve helped mid-market organisations across Australia and internationally migrate from Sisense to Superset, often as part of broader platform modernisation initiatives. Our experience spans financial services, retail, SaaS, and government sectors.

We typically work on a fixed-fee or time-and-materials basis, depending on your preference. We can provide:

• Migration assessment: 2-week engagement to scope effort and cost
• Migration execution: 12–16 week programme to plan, execute, and support cutover
• Post-migration optimisation: Ongoing support to scale your analytics and reduce costs

Our team includes senior engineers with experience in platform engineering across Australia, platform engineering in Sydney, platform engineering in Melbourne, and other major cities. We also work with teams in New York, Seattle, Atlanta, and across Canada and New Zealand.

If you’re pursuing SOC 2 or ISO 27001 compliance as part of your migration, we can help design audit-ready architecture from the start. We’ve worked with teams building Superset deployments that pass compliance audits without rework.

Resources and Further Reading

As you plan your migration, review these resources:

• Apache Superset official documentation: Apache Superset Documentation covers installation, configuration, and best practices
• Sisense migration guidance: Sisense Migration Documentation provides official migration concepts
• Operational considerations: What It Takes to Manage the Open Source BI Tool, Apache Superset discusses operational effort and cost
• Competitive analysis: 10 Sisense Alternatives: Top BI Competitors for 2026 provides context on alternatives
• Related BI platforms: Metabase Documentation, Kibana Documentation, and Power BI Documentation for comparative understanding
• General BI context: Business Intelligence Tools Guide for broader market perspective

The Bottom Line

Migrating from Sisense to Superset is a significant undertaking, but it’s one that pays for itself within 12–18 months through cost savings and unlocks flexibility that Sisense can’t match. The key is rigorous planning, realistic timelines, and strong governance.

If you’re a mid-market organisation considering this migration, the time to start planning is now. The sooner you move, the sooner you realise the cost savings and operational benefits.

We’re here to help. Whether you need a migration assessment, execution support, or ongoing platform engineering, reach out to discuss your situation. We’ve helped teams across industries make this transition successfully, and we can help yours too.

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Summary

Migrating from Sisense to Superset for a mid-market organisation requires:

1. Rigorous scoping: Audit your current state, tier your dashboards, and estimate effort realistically (12–16 weeks, £100k+)
2. Strong architecture: Invest in data architecture upfront—a modern data warehouse, semantic layer (dbt), and governance framework
3. Phased cutover: Migrate Tier 1 dashboards first, validate thoroughly, then proceed to Tier 2 and 3
4. Cost transparency: Plan for £60,000–140,000 annual spend post-migration, a 30–50% reduction compared to Sisense
5. User-centric change management: Train extensively, maintain parallel operations, and support actively post-cutover
6. Ongoing optimisation: Establish governance practices, monitor performance, and scale as your analytics needs grow

The organisations that succeed are those that treat migration as a programme, not a project—with executive sponsorship, cross-functional teams, and realistic timelines. The payoff is significant: lower costs, faster insights, and the flexibility to innovate.