Table of Contents
- Why Mid-Market Teams Are Moving from Qlik to Superset
- Understanding the Scope of Your Migration
- Assessing Your Current Qlik Environment
- Designing Your Superset Architecture
- Governance and Access Control Strategy
- Cost Benchmarking and ROI Modelling
- The Migration Playbook: Phase-by-Phase Execution
- Cutover Patterns and Risk Mitigation
- Post-Migration Optimisation and Support
- Getting Started with PADISO
Why Mid-Market Teams Are Moving from Qlik to Superset {#why-mid-market-teams-are-moving}
Qlik has dominated the mid-market analytics space for over a decade. Its associative engine, intuitive UI, and tight integration with enterprise data warehouses made it the default choice for organisations with 500–5,000 employees and complex reporting needs. But the cost structure, licensing model, and inflexibility around embedded analytics have created a perfect storm of pressure.
Mid-market teams are hitting a ceiling. Per-seat licensing costs $50–$150 per user per month, which balloons when you need to distribute insights across the organisation. Qlik’s on-premises architecture demands dedicated infrastructure, patching, and security overhead. And when you want to embed dashboards into customer-facing products or internal applications, the licensing and architecture constraints become prohibitive.
Apache Superset, by contrast, is open-source, cloud-native, and cost-predictable. A mid-market organisation running Superset on a modest Kubernetes cluster or managed cloud service typically spends $5,000–$15,000 per month on infrastructure—regardless of user count. That’s a 60–80% cost reduction for many teams. More importantly, Superset’s architecture aligns with modern data stacks: it works seamlessly with cloud data warehouses like Snowflake, BigQuery, and Redshift, and it supports embedded analytics, custom plugins, and governance frameworks that Qlik either charges extra for or doesn’t support at all.
The migration from Qlik to Superset is not a rip-and-replace. It’s a deliberate, phased transition that requires careful scoping, governance design, and cutover planning. This guide walks you through the entire journey—from assessment to go-live—with concrete benchmarks, risk patterns, and real-world playbooks.
Understanding the Scope of Your Migration {#understanding-the-scope}
Before you commit resources to a migration, you need to understand what you’re actually moving. Scope creep is the silent killer of BI migrations. Many teams underestimate the effort because they count dashboards, not the underlying dependencies, data quality issues, and user behaviour patterns that keep those dashboards alive.
Defining the Migration Scope
Start with a complete inventory of your Qlik estate. This includes:
- Active dashboards and reports: How many Qlik Sense or QlikView applications are in production? Count both published dashboards and ad-hoc analyses that users create regularly.
- Data connections and sources: How many data sources feed into Qlik? Are they databases, APIs, cloud data warehouses, or flat files? What’s the refresh cadence—hourly, daily, or on-demand?
- User base and usage patterns: How many active users interact with Qlik each month? What percentage are casual viewers versus power analysts? Which dashboards drive business decisions?
- Custom logic and calculations: Qlik’s scripting language and set analysis can be complex. Document all custom expressions, aggregations, and business logic that sits inside Qlik.
- Embedded analytics and integrations: Are you embedding Qlik dashboards into customer-facing applications, portals, or internal tools? These require special attention during migration.
- Security and governance: What access controls, row-level security (RLS), and audit trails are currently enforced in Qlik?
This inventory becomes your migration manifest. It prevents you from discovering halfway through the project that you’ve missed a critical dashboard or that your governance model is incomplete.
Classifying Assets for Migration Priority
Not all dashboards are created equal. Use a simple classification matrix:
- Tier 1 (Critical): Dashboards that drive daily operational decisions or revenue reporting. These must migrate first and with zero downtime.
- Tier 2 (Important): Dashboards used weekly or monthly by key stakeholders. These can migrate in phase two, with a brief parallel-run window.
- Tier 3 (Nice-to-Have): Ad-hoc analyses, historical reports, or dashboards used by a small subset of users. These can migrate last or be decommissioned entirely.
This tiering approach helps you manage risk and build momentum. You get quick wins with Tier 3 assets, build confidence with Tier 2, and only tackle Tier 1 after your team has proven the process works.
Assessing Your Current Qlik Environment {#assessing-current-qlik}
A thorough assessment of your Qlik environment is the foundation of a successful migration. This is not a quick audit—it’s a detailed technical and business review that informs every decision downstream.
Technical Assessment
Work with your Qlik administrator to extract metadata and configuration details. This includes:
- Qlik Sense or QlikView: Are you running Sense (cloud-native, modern) or QlikView (legacy, on-premises)? QlikView migrations are typically more complex because the application design and scripting patterns are older and often more tightly coupled to on-premises infrastructure.
- Data model architecture: Qlik’s associative engine relies on a specific data model structure. Document the fact tables, dimensions, and join logic. Understand which fields are used for filtering and which are calculated on-the-fly.
- Refresh logic and ETL: How does data flow into Qlik? Is there an ETL tool (Talend, Informatica, custom scripts) upstream, or does Qlik pull directly from source systems? What’s the refresh frequency and data latency requirement?
- Custom extensions and plugins: Qlik allows custom visualisations and extensions. Identify which ones are in use—they may not have direct equivalents in Superset and may need to be rebuilt or replaced.
- Performance baselines: Measure query response times, dashboard load times, and concurrent user capacity under typical load. You’ll use these as benchmarks for your Superset environment.
Use Qlik Documentation to understand the metadata export options available. Many Qlik environments have built-in audit logs and metadata repositories that you can query to build a comprehensive inventory.
Business Assessment
Beyond the technology, understand the business context:
- Stakeholder interviews: Talk to the CFO, business analysts, and department heads who depend on Qlik. What dashboards are non-negotiable? What pain points do they experience (slow load times, licensing costs, inability to embed)?
- User behaviour analysis: Analyse Qlik audit logs to identify which dashboards are actually used, how frequently, and by whom. This often reveals that 80% of usage is concentrated in 20% of dashboards. The rest are legacy reports that can be retired.
- Regulatory and compliance requirements: Are there dashboards that feed into regulatory reporting, financial statements, or audit trails? These require special care during migration and may have specific data retention or lineage requirements.
- Integration dependencies: Which downstream systems consume data from Qlik? Are there scheduled exports, API calls, or manual workflows that depend on Qlik dashboards?
This business context prevents you from over-engineering the migration. It also helps you communicate ROI to stakeholders: “We’re retiring 40 dashboards that nobody uses, consolidating the 20 that matter, and rebuilding them in Superset at 70% lower cost.”
Designing Your Superset Architecture {#designing-superset-architecture}
Superset’s architecture is fundamentally different from Qlik’s. Qlik is a monolithic, in-memory analytics engine. Superset is a lightweight visualisation and orchestration layer that sits on top of your data warehouse. Understanding this difference is critical to designing a successful Superset environment.
Choosing Your Deployment Model
You have three main options:
Managed Superset (SaaS)
Vendors like Preset (the commercial arm of Apache Superset) offer fully managed cloud hosting. You get automatic updates, built-in scaling, and zero infrastructure overhead. This is ideal for teams without dedicated platform engineering capacity. Costs typically range from $5,000–$20,000 per month depending on usage and features.
Self-Hosted Superset (Kubernetes)
You deploy Superset on your own Kubernetes cluster (EKS, GKE, AKS). This gives you full control over configuration, plugins, and data access. It’s more complex operationally but more cost-effective at scale. A typical mid-market deployment runs on a small cluster with 2–4 nodes, costing $2,000–$5,000 per month on AWS or Azure.
Self-Hosted Superset (Docker Compose)
For smaller teams or proof-of-concepts, Docker Compose offers a lightweight deployment option. This is not recommended for production because it lacks auto-scaling and high availability, but it’s useful for testing and development.
Most mid-market organisations choose either Managed Superset (if they want simplicity) or Kubernetes (if they want cost control and customisation). The decision often depends on your existing cloud infrastructure and engineering capacity.
Data Warehouse and Connectivity
Superset is a visualisation layer—it doesn’t store data. It connects to your data warehouse and queries it on-demand. This is a fundamental shift from Qlik, which loads data into memory.
Choose a cloud data warehouse that aligns with your existing infrastructure:
- Snowflake: Most flexible, works with Superset out-of-the-box, excellent for mid-market deployments.
- BigQuery: Native integration with Superset, ideal if you’re on Google Cloud.
- Redshift: Good choice if you’re committed to AWS.
- Databricks: Increasingly popular for organisations building modern data stacks with Apache Spark and Delta Lake.
Your data warehouse becomes the source of truth. All Superset dashboards query it directly. This means your data quality, governance, and performance depend on the warehouse—not on Superset.
Semantic Layer and Governance
Qlik’s strength is its semantic layer—the business logic that sits between raw data and dashboards. Superset doesn’t have a built-in semantic layer, but you can build one using the semantic layer approach that powers modern analytics.
There are two patterns:
Pattern 1: Database Views and Curated Datasets
Create a set of clean, well-documented database views in your data warehouse that encapsulate business logic. Analysts and dashboard builders query these views, not raw tables. This is simple, performant, and gives you a single source of truth for metrics.
Pattern 2: Superset Datasets and Metrics
Use Superset’s native dataset and metrics functionality to define business logic. Superset datasets are reusable query definitions that can be shared across dashboards. Metrics are aggregations (revenue, count, average) that are defined once and used everywhere. This approach is more flexible but requires discipline to avoid duplication.
Most mid-market teams use a hybrid: critical metrics and dimensions are defined in the data warehouse (Pattern 1), and Superset datasets provide additional flexibility for dashboard-specific logic (Pattern 2).
Infrastructure and Scaling
Unlike Qlik, Superset’s performance doesn’t degrade as you add users. It’s stateless—each query is independent. But you do need to plan for:
- Query load: How many concurrent queries will Superset execute? If your dashboards refresh every 5 minutes and you have 500 users, that’s 100 queries per minute. Your data warehouse needs to handle this.
- Caching strategy: Superset supports query caching to reduce load on the warehouse. Cache frequently-accessed dashboards for 1 hour; less-common reports for 15 minutes.
- Database connection pooling: Superset uses a connection pool to manage database connections. Size this based on expected concurrency.
If you’re deploying on Kubernetes, you can auto-scale the Superset pods based on CPU and memory. This ensures performance during peak usage (month-end close, board meetings) without wasting resources during quiet periods.
For teams looking to modernise their platform infrastructure while implementing Superset, platform development services in Sydney, Melbourne, or across Australia can help design and deploy a production-grade architecture tailored to your data and governance requirements.
Governance and Access Control Strategy {#governance-strategy}
Qlik’s governance model is role-based and tightly integrated. Superset’s is more flexible but requires deliberate design. A weak governance strategy during migration is a common failure point—teams end up with inconsistent data, duplicate dashboards, and security gaps.
User Roles and Permissions
Define clear roles in Superset:
- Viewer: Can view published dashboards, no editing or creation.
- Editor: Can create and edit dashboards, but cannot manage data sources or users.
- Admin: Full access to data sources, dashboards, users, and configuration.
Map your Qlik user base to these roles. Most organisations find that 70% of users are viewers, 20% are editors, and 10% are admins.
Data Source Access Control
In Superset, you control access to dashboards, not underlying data. If a user has access to a dashboard, they can see all the data in it. This is different from Qlik, which can enforce row-level security at the data level.
If you need fine-grained access control (e.g., sales reps can only see their own region’s data), you have two options:
Option 1: Row-Level Security in the Data Warehouse
Create separate datasets or views for each access level. A sales rep’s dashboard queries a view that filters for their region. This is clean and performant but requires careful data warehouse design.
Option 2: Superset’s Native RLS
Superset supports row-level security through its native RLS feature, which uses database roles and dynamic SQL filters. This is more flexible but adds complexity to your data warehouse and Superset configuration.
Most mid-market teams use Option 1 because it’s simpler and aligns with modern data warehouse best practices.
Dashboard and Dataset Governance
Establish clear ownership and naming conventions:
- Ownership: Every dashboard has an owner (usually a business analyst or data analyst). The owner is responsible for updates, documentation, and decommissioning.
- Naming convention: Use a consistent naming scheme (e.g.,
[Department]_[Process]_[Frequency]) so dashboards are easy to find and understand. - Documentation: Every dashboard should have a description that explains its purpose, refresh frequency, and key metrics. Link to the underlying data sources and business logic.
- Deprecation process: Establish a process for retiring old dashboards. Don’t just delete them—archive them for 6 months in case someone needs historical access.
Audit and Compliance
Superset logs all user actions—dashboard views, data exports, configuration changes. Enable audit logging and export logs to a central system (CloudWatch, Datadog, Splunk) for compliance and troubleshooting.
If you need to pass SOC 2 or ISO 27001 audits, Superset’s audit logs are critical. Document your access control policies and show auditors how Superset enforces them. For teams pursuing compliance, platform development services can help design and implement audit-ready architectures.
Cost Benchmarking and ROI Modelling {#cost-benchmarking}
Cost savings are often the primary driver of a Qlik-to-Superset migration. But the savings are only real if you model them correctly and account for hidden costs.
Qlik Cost Baseline
Start by calculating your current Qlik spend. This includes:
- Per-seat licensing: Multiply your user count by the per-seat cost. For a mid-market organisation with 200 Qlik users at $100/month, that’s $240,000/year.
- Server infrastructure: If you’re running QlikView on-premises, factor in hardware, virtualisation, and data centre costs. Cloud-hosted Qlik Sense typically costs $5,000–$10,000/month.
- Maintenance and support: Qlik support contracts, patches, and upgrades. Budget 10–15% of licensing costs annually.
- Professional services: Customisation, integration work, and training. This is often the largest hidden cost, ranging from $50,000–$200,000 per year for mid-market teams.
For a typical mid-market organisation:
| Cost Category | Annual Cost |
|---|---|
| Per-seat licensing (200 users @ $100/month) | $240,000 |
| Infrastructure (cloud or on-premises) | $60,000 |
| Support and maintenance | $30,000 |
| Professional services | $100,000 |
| Total Annual Qlik Cost | $430,000 |
Superset Cost Model
Now calculate your Superset costs:
- Infrastructure: Managed Superset (Preset) costs $10,000–$20,000/year. Self-hosted on Kubernetes costs $24,000–$60,000/year (cluster costs, not including staff time).
- Data warehouse: Your biggest cost. Snowflake, BigQuery, or Redshift typically cost $10,000–$50,000/month depending on data volume and query complexity. But you’re probably already paying for a data warehouse, so this is not incremental.
- Implementation and migration: One-time cost of $50,000–$150,000 to design, build, and migrate dashboards. This is front-loaded.
- Ongoing support and optimisation: $20,000–$40,000/year for a dedicated analyst to maintain dashboards, optimise queries, and support users.
For a typical mid-market organisation using managed Superset:
| Cost Category | Annual Cost |
|---|---|
| Managed Superset (Preset) | $15,000 |
| Data warehouse (incremental) | $0* |
| Ongoing support | $30,000 |
| Total Annual Superset Cost | $45,000 |
*Assumes you already have a cloud data warehouse. If not, add $120,000–$300,000/year.
ROI and Payback Period
Using the above benchmarks:
- Annual savings: $430,000 (Qlik) − $45,000 (Superset) = $385,000
- Implementation cost: $100,000 (one-time)
- Payback period: 100,000 / 385,000 = 3.1 months
- Year 1 ROI: (385,000 − 100,000) / 100,000 = 285%
This is a conservative estimate. Many organisations see even better ROI because:
- They retire 30–40% of dashboards that nobody uses, reducing support costs further.
- They reduce per-seat licensing by consolidating tools (Qlik + Tableau + Looker → just Superset).
- They build new capabilities (embedded analytics, custom plugins) that would be prohibitively expensive in Qlik.
Cost Benchmarks by Organisation Size
| Organisation Size | Qlik Annual Cost | Superset Annual Cost | Annual Savings | Payback Period |
|---|---|---|---|---|
| 100–200 users | $200,000–$400,000 | $40,000–$60,000 | $140,000–$360,000 | 2–4 months |
| 200–500 users | $400,000–$1,000,000 | $60,000–$100,000 | $300,000–$940,000 | 1–4 months |
| 500+ users | $1,000,000+ | $100,000–$200,000 | $800,000+ | 1–3 months |
These benchmarks assume you’re migrating to managed Superset. Self-hosted deployments have higher upfront infrastructure costs but lower per-month costs, extending the payback period to 4–6 months.
The Migration Playbook: Phase-by-Phase Execution {#migration-playbook}
A successful Qlik-to-Superset migration follows a structured, phased approach. Rushing the process is the most common cause of failure.
Phase 0: Foundation (Weeks 1–4)
Objectives: Set up Superset infrastructure, establish governance, and validate the migration approach on a small pilot.
Activities:
- Deploy Superset: Choose your deployment model (managed or self-hosted) and deploy a production-grade environment. Include monitoring, logging, and backup.
- Configure data sources: Connect Superset to your data warehouse. Test connectivity, query performance, and caching.
- Design governance framework: Document user roles, naming conventions, and access control policies.
- Build pilot dashboards: Select 2–3 low-risk Qlik dashboards and rebuild them in Superset. This is your proof-of-concept.
- User acceptance testing (UAT): Have business analysts validate that the pilot dashboards match the Qlik originals. Measure performance and user experience.
- Train the team: Run a half-day workshop for analysts and dashboard builders on Superset basics: creating datasets, building charts, and configuring dashboards.
Success criteria:
- Superset is deployed and stable.
- Pilot dashboards are functionally equivalent to Qlik originals.
- Performance is acceptable (dashboard load time < 3 seconds).
- Team is confident in the process.
Typical effort: 1 FTE (full-time equivalent) for 4 weeks, plus part-time support from your data warehouse team.
Phase 1: Quick Wins (Weeks 5–12)
Objectives: Migrate Tier 3 (nice-to-have) dashboards to build momentum and refine the process.
Activities:
- Identify quick-win dashboards: Select 10–15 Tier 3 dashboards that are simple, self-contained, and used by a small audience.
- Rebuild in Superset: Create datasets and dashboards in Superset. Reuse the pilot approach.
- Parallel run: Run both Qlik and Superset versions side-by-side for 2 weeks. Have users validate that numbers match.
- Cutover: Decommission Qlik versions and publish Superset as the single source of truth.
- Gather feedback: Collect user feedback on usability, performance, and data accuracy. Refine your process based on learnings.
Success criteria:
- 10–15 dashboards migrated and live.
- Zero data discrepancies between Qlik and Superset.
- User satisfaction > 80%.
- Cutover process is repeatable and low-risk.
Typical effort: 1.5 FTE for 8 weeks.
Phase 2: Core Dashboards (Weeks 13–20)
Objectives: Migrate Tier 2 (important) dashboards that are used weekly or monthly by key stakeholders.
Activities:
- Batch migration: Migrate 20–30 Tier 2 dashboards in groups of 5–10. Each batch goes through the same parallel-run and cutover process.
- Optimise data warehouse: As you migrate more dashboards, you’ll identify opportunities to optimise the data warehouse. Create materialized views or aggregate tables to improve query performance.
- Enhance governance: Implement row-level security, caching strategies, and audit logging based on Phase 1 learnings.
- Scale infrastructure: Monitor Superset and data warehouse performance. Add capacity if needed.
Success criteria:
- 20–30 Tier 2 dashboards migrated.
- Data warehouse performance is stable under increased query load.
- Governance is enforced (RLS, audit logging, access control).
Typical effort: 2 FTE for 8 weeks.
Phase 3: Critical Dashboards (Weeks 21–28)
Objectives: Migrate Tier 1 (critical) dashboards that drive daily operations and revenue reporting.
Activities:
- Meticulous planning: For each critical dashboard, create a detailed cutover plan. Identify dependencies, define rollback procedures, and schedule the cutover during a low-risk window (e.g., weekend, after month-end close).
- Extended parallel run: Run Qlik and Superset versions for 4 weeks, not 2. Have multiple stakeholders validate data and functionality.
- Cutover rehearsal: Do a full dry-run of the cutover process. Identify and fix any issues before the real cutover.
- Cutover execution: Execute the cutover during the planned window. Have a rollback plan ready in case something goes wrong.
- Monitoring and support: Monitor Superset closely for 2 weeks post-cutover. Have support staff on standby for user issues.
Success criteria:
- All Tier 1 dashboards migrated with zero downtime.
- Data accuracy validated by finance, operations, and executive teams.
- User adoption > 95%.
Typical effort: 2 FTE for 8 weeks, plus executive and business stakeholder time.
Phase 4: Optimisation and Decommissioning (Weeks 29+)
Objectives: Retire Qlik infrastructure, optimise Superset for performance and cost, and hand off to operations.
Activities:
- Decommission Qlik: Remove user access to Qlik. Archive historical data for compliance. Decommission servers and cancel licensing.
- Optimise Superset: Review dashboard performance, query patterns, and caching strategies. Optimise slow dashboards and eliminate unused ones.
- Handoff to operations: Document Superset architecture, runbooks, and support procedures. Train operations team on monitoring, scaling, and troubleshooting.
- Measure ROI: Calculate actual cost savings, time-to-value, and user satisfaction. Compare against your initial projections.
Success criteria:
- Qlik completely decommissioned.
- Superset is stable, performant, and cost-optimised.
- Operations team is confident in managing the environment.
- ROI targets are met or exceeded.
Typical effort: 1 FTE for 4 weeks, plus part-time operations support ongoing.
Total Migration Timeline
From start to finish: 28–32 weeks (6.5–8 months) for a mid-market organisation with 50–100 dashboards.
Breakdown:
- Phase 0 (Foundation): 4 weeks
- Phase 1 (Quick Wins): 8 weeks
- Phase 2 (Core): 8 weeks
- Phase 3 (Critical): 8 weeks
- Phase 4 (Optimisation): 4 weeks
This timeline assumes a dedicated migration team (2–3 FTE) and part-time support from business analysts and data warehouse engineers. If you have less capacity, extend each phase by 2–4 weeks.
Cutover Patterns and Risk Mitigation {#cutover-patterns}
The cutover—the moment you switch from Qlik to Superset—is the highest-risk point in the migration. A failed cutover can disrupt business operations and undermine stakeholder confidence. Here are proven patterns that minimise risk.
Pattern 1: Big Bang Cutover
How it works: You migrate all dashboards simultaneously and decommission Qlik on a specific date.
Pros:
- Fast—you’re done in a single event.
- Clear communication—stakeholders know exactly when to switch.
- No parallel-run costs—you’re not paying for both systems.
Cons:
- High risk—if something goes wrong, you have no fallback.
- Difficult to troubleshoot—you can’t isolate issues to a specific dashboard.
- User resistance—people are forced to change all at once.
When to use: Only for small migrations (< 10 dashboards) or when you have very high confidence in your Superset environment. Not recommended for mid-market organisations.
Pattern 2: Phased Cutover (Recommended)
How it works: You migrate dashboards in batches (Tiers 3, 2, 1) over 6–8 months. Each batch has a 2–4 week parallel-run window before cutover.
Pros:
- Low risk—you can roll back a single dashboard if needed.
- Proven process—each batch validates the migration approach.
- User adoption—people get time to adjust to Superset.
- Learning—you optimise the process with each batch.
Cons:
- Longer timeline—6–8 months is a significant commitment.
- Parallel-run costs—you’re paying for both systems during the transition.
- Complexity—managing multiple versions of the same dashboard.
When to use: For mid-market organisations with 50+ dashboards and high business criticality. This is the pattern we recommend.
Pattern 3: Hybrid Cutover
How it works: You migrate low-risk dashboards immediately (Pattern 1), then use a phased approach for critical dashboards.
Pros:
- Balanced risk—quick wins reduce timeline, phased approach reduces risk for critical assets.
- Cost-effective—you decommission low-risk dashboards early, reducing parallel-run costs.
- Flexibility—you can adjust the timeline based on progress.
Cons:
- Complexity—managing multiple cutover patterns.
- Communication—stakeholders need to understand different timelines for different dashboards.
When to use: For mid-market organisations with 30–50 dashboards and mixed criticality. This is a pragmatic middle ground.
Risk Mitigation Strategies
Regardless of which pattern you choose, implement these risk mitigations:
Data Validation
Before cutover, validate that Superset dashboards produce the same numbers as Qlik. This is non-negotiable. Create a validation checklist for each dashboard:
- Key metrics (revenue, count, average) match within 0.1%.
- Filters and drill-downs work as expected.
- Date ranges and time zones are correct.
- Sorting and ranking are accurate.
Have business analysts (not just data analysts) validate the numbers. They understand the business context and can spot anomalies.
Performance Testing
Load-test Superset before cutover. Simulate peak usage (e.g., 200 concurrent users, all viewing dashboards). Measure:
- Dashboard load time (should be < 3 seconds).
- Query response time (should be < 5 seconds).
- Concurrent query capacity (should handle 2x expected peak).
- Data warehouse performance (CPU, memory, query queue).
If performance is below target, optimise queries, add caching, or scale infrastructure before cutover.
Rollback Plan
For every critical dashboard, have a rollback plan:
- Keep Qlik running in read-only mode for 2 weeks post-cutover.
- Document the process for re-enabling Qlik access if needed.
- Have a backup of Qlik data and configuration.
- Identify a single point of escalation (e.g., VP of Analytics) who can authorise a rollback.
You probably won’t need the rollback plan, but having it reduces anxiety and speeds decision-making if something goes wrong.
Communication and Training
Start communicating the migration 4–6 weeks before cutover:
- Week -6: Announce the migration and explain the business case (cost savings, new capabilities).
- Week -4: Share the timeline and identify which dashboards are affected.
- Week -2: Conduct training sessions on Superset (how to filter, export, share dashboards).
- Week -1: Send a reminder with the cutover date and what to expect.
- Day 0: Execute the cutover. Have support staff on standby.
- Week +1: Send a follow-up survey asking for feedback and issues.
Clear communication prevents surprises and builds confidence.
Post-Migration Optimisation and Support {#post-migration}
The migration doesn’t end when you go live. The weeks and months after cutover are critical for ensuring Superset is successful, performant, and cost-effective.
Performance Tuning
Once dashboards are live, monitor their performance closely:
- Query performance: Use Superset’s query log to identify slow queries. Optimise them by adding indexes, materializing views, or rewriting SQL.
- Caching strategy: Adjust cache TTLs based on actual usage. High-traffic dashboards might cache for 1 hour; low-traffic dashboards for 15 minutes.
- Data warehouse optimisation: Review query patterns and create aggregate tables or materialized views to improve performance.
A typical post-migration optimisation effort reduces dashboard load time by 20–30%.
User Adoption and Support
Some users will resist Superset because it’s different from Qlik. Mitigate this with:
- Dedicated support channel: Create a Slack channel or email alias for Superset questions. Respond within 2 hours.
- Documentation: Build a knowledge base with screenshots and step-by-step guides for common tasks (filtering, exporting, sharing).
- Office hours: Run weekly 30-minute sessions where users can ask questions and get help.
- Champions program: Identify power users in each department and train them as Superset champions. They become the first line of support for their peers.
After 4–6 weeks, most users will be comfortable with Superset. User adoption typically reaches 80%+ by week 8.
Cost Optimisation
Cost savings are a key driver of the migration, but you need to actively manage costs:
- Right-size infrastructure: Monitor Superset and data warehouse usage. If you’re over-provisioned, scale down.
- Retire unused dashboards: Identify dashboards with zero views in the last 30 days. Archive or delete them.
- Consolidate data sources: If you have multiple databases or schemas, consolidate them into a single data warehouse.
- Negotiate cloud contracts: If you’re using AWS, Azure, or GCP, use the cost savings from Qlik to negotiate volume discounts on cloud services.
Many organisations find additional cost savings in the months after migration by optimising the data warehouse and retiring unused assets.
Governance Enforcement
As Superset usage grows, governance can slip. Enforce it actively:
- Monthly audit: Review new dashboards for naming conventions, documentation, and ownership.
- Quarterly access review: Audit user access to ensure least-privilege principles are followed.
- Dashboard lifecycle management: Establish a process for approving new dashboards and retiring old ones.
- Metric governance: Maintain a registry of approved metrics and dimensions. Discourage ad-hoc calculations.
Getting Started with PADISO {#getting-started}
Migrating from Qlik to Superset is a significant undertaking. Many mid-market organisations benefit from partnering with experienced teams who’ve done this before.
PADISO is a Sydney-based venture studio and AI digital agency that partners with ambitious teams to ship AI products, automate operations, and modernise their data and analytics infrastructure. We’ve led Qlik-to-Superset migrations for financial services, retail, and media organisations across Australia, North America, and Canada.
We help with:
- Migration planning and scoping: We conduct a detailed assessment of your Qlik environment and design a phased migration roadmap tailored to your organisation’s risk tolerance and timeline.
- Architecture and infrastructure: We design a production-grade Superset environment that aligns with your cloud strategy, security requirements, and cost targets. Whether you choose managed Superset or self-hosted Kubernetes, we build it right.
- Dashboard rebuild and optimisation: We rebuild your critical dashboards in Superset, optimise queries for performance, and implement governance frameworks that scale with your organisation.
- Data warehouse modernisation: We help you consolidate fragmented data sources into a unified cloud data warehouse (Snowflake, BigQuery, or Redshift) that powers both Superset and downstream analytics.
- Training and handoff: We train your team on Superset architecture, operations, and best practices. We document everything so you can manage it independently post-migration.
Our platform development teams work across multiple cities and regions. Whether you’re in Sydney, Melbourne, Canberra, or the Gold Coast, we can support your migration with local expertise. For organisations in North America, we have platform development teams in New York, Washington DC, Chicago, Austin, Dallas, and Toronto. We also support teams across Canada and New Zealand.
If you’re considering a Qlik-to-Superset migration and want to discuss your specific situation, book a call with our platform development team. We’ll walk through your current environment, discuss your goals, and outline a migration approach that works for your organisation.
For teams in specific regions:
- Sydney and Australia: Platform Development in Sydney or Platform Development in Australia
- Melbourne: Platform Development in Melbourne
- Canberra: Platform Development in Canberra
- Gold Coast: Platform Development in Gold Coast
- New York: Platform Development in New York
- Washington DC: Platform Development in Washington, D.C.
- Chicago: Platform Development in Chicago
- Austin: Platform Development in Austin
- Dallas: Platform Development in Dallas
- Toronto: Platform Development in Toronto
- Canada: Platform Development in Canada
- United States: Platform Development in United States
- New Zealand: Platform Development in New Zealand
Summary and Next Steps
Migrating from Qlik to Superset is a strategic investment that delivers substantial cost savings (60–80% reduction in BI spend), improved flexibility for embedded analytics, and alignment with modern cloud data stacks. But it requires careful planning, disciplined execution, and strong governance.
Here’s what you need to do:
Immediate (This Week)
- Conduct a complete inventory of your Qlik estate—dashboards, users, data sources, and custom logic.
- Calculate your current Qlik spend (licensing, infrastructure, support, professional services).
- Identify your Tier 1 (critical) dashboards and estimate the effort to rebuild them in Superset.
Short-term (Next 2–4 Weeks)
- Choose your Superset deployment model (managed or self-hosted) and cloud data warehouse.
- Design your governance framework (roles, naming conventions, access control).
- Build a proof-of-concept with 2–3 pilot dashboards.
- Validate that Superset can deliver the performance and functionality you need.
Medium-term (Next 2–3 Months)
- Develop a detailed migration roadmap with timelines, dependencies, and risk mitigations.
- Allocate a dedicated migration team (2–3 FTE).
- Execute Phase 1 (quick wins) to build momentum and refine your process.
- Measure and communicate early wins to build stakeholder confidence.
Long-term (Next 6–8 Months)
- Execute Phases 2 and 3 (core and critical dashboards).
- Decommission Qlik and realise cost savings.
- Optimise Superset for performance and cost.
- Hand off to operations and establish governance.
The migration is a journey, not a sprint. But the destination—a modern, cost-effective, flexible analytics platform—is well worth the effort.
For detailed guidance tailored to your organisation’s specific situation, reach out to PADISO. We’ve built this playbook based on real migrations, and we’re here to help you execute it successfully. Book a call to discuss your Qlik-to-Superset migration today.
You can also explore how other organisations have modernised their analytics infrastructure. Check out Apache Superset’s official documentation to understand the platform’s capabilities, and review Qlik’s documentation to understand what you’re moving away from. For broader context on BI concepts and practices, Gartner’s explainer on business intelligence provides authoritative background. If you’re building a modern data stack, understanding what data migration means and how semantic layers power modern analytics will help you design a governance framework that scales. For organisations planning broader cloud infrastructure changes, AWS’s cloud data migration guide and Microsoft’s cloud adoption framework offer prescriptive guidance that applies to BI platform transitions as well.
Your analytics platform should be a competitive advantage, not a cost centre. Superset makes that possible.