Guide 23 mins

Migrating from Looker to Superset for Enterprise Organisations

Enterprise Looker-to-Superset migration playbook: scoping, governance, cost benchmarks, cutover patterns and risk mitigation for mid-market and large teams.

The PADISO Team ·2026-06-17

Migrating from Looker to Superset for Enterprise Organisations

Why Organisations Move from Looker to Superset
Pre-Migration Assessment and Scoping
Cost Benchmarking and ROI
Governance and Security Architecture
Building Your Semantic Layer
Migration Patterns and Cutover Strategy
Performance Tuning and Optimisation
Training, Change Management and Handover
Post-Migration Monitoring and Support
Common Pitfalls and How to Avoid Them
Next Steps and Getting Started

Why Organisations Move from Looker to Superset {#why-organisations-move}

Looker is a powerful, mature BI platform—but it carries significant operational and financial weight. Enterprise teams increasingly migrate to Apache Superset for three core reasons: cost control, architectural flexibility, and independence from vendor lock-in.

Looker’s per-seat licensing model scales linearly with headcount. A 200-person organisation paying $3,000–$5,000 per user annually faces a $600,000–$1,000,000 annual bill. Superset, by contrast, is open-source and deployed on your infrastructure—you pay for compute, storage, and engineering time, not per-user access rights. Teams we’ve worked with in Sydney and across Australia have cut analytics spend by 40–60% within 12 months of migration, reinvesting those savings into data engineering, feature development, and security hardening.

Beyond cost, Looker embeds you into Google Cloud’s ecosystem. If your data lives on Snowflake, Redshift, or your own data warehouse, Superset gives you direct, native control over that connection. You own the semantic layer, the caching strategy, and the deployment footprint. There’s no dependency on Looker’s LookML parsing engine or its cloud-hosted infrastructure.

Third, Superset’s open-source foundation—backed by the Apache Software Foundation—means you’re not betting your analytics on a single vendor’s roadmap. The community is active; security patches are transparent; and you can fork or extend the codebase if you need to. For regulated industries (finance, health, government), this transparency is often a requirement.

That said, migration is not trivial. Looker’s strength is its semantic layer (LookML), its governance model (roles, permissions, content ownership), and its embedded analytics capabilities. Moving to Superset means rebuilding that semantic layer using tools like dbt or Cube, replicating governance through role-based access control (RBAC) and database-level permissions, and rearchitecting embedded dashboards if you serve analytics to end customers.

Pre-Migration Assessment and Scoping {#pre-migration-assessment}

Inventory Your Looker Estate

Before you move a single dashboard, you need to know what you’re moving. Most enterprise Looker instances contain hundreds of looks, explores, and dashboards—many of them unused, outdated, or redundant.

Conduct an audit:

Dashboard count and usage: Export your Looker content inventory. Identify which dashboards have been viewed in the last 90 days. Ruthlessly deprecate anything unused—you’ll save engineering effort and clarify your migration scope.
Explore and view complexity: Examine the LookML models powering your explores. Count derived tables, custom measures, and filters. These will need to be rebuilt as dbt models, Cube metrics, or Superset native SQL.
Embedded analytics: Identify any embedded dashboards or looks served to customers or external users. Embedded Looker dashboards require API-level authentication and session management; Superset’s embedding model differs, so you’ll need to rearchitect these separately.
Data sources and connections: List every database, data warehouse, and API Looker connects to. Check latency, query volumes, and row limits. These constraints will shape your Superset architecture.
User base and permissions model: Count active Looker users by role (viewer, developer, admin). Document your permission hierarchy (who can edit what, folder ownership, content policies). You’ll rebuild this in Superset using database roles, view-level permissions, and RBAC.

A typical enterprise audit takes 2–4 weeks and produces a spreadsheet with 200–500 rows. Use this as your source of truth for migration planning.

Define Your Target State

Decide what success looks like. Common targets include:

Reduce per-seat licensing cost by 50%+: Measure current Looker spend; set a target annual cost for Superset infrastructure and engineering.
Ship new dashboards 4× faster: Looker’s LookML requires data modelling expertise; Superset’s SQL-first approach can be faster for ad-hoc analytics. Measure time-to-dashboard before and after.
Achieve SOC 2 / ISO 27001 readiness: If your business requires compliance, Superset’s open-source nature and your control over deployment make audit-readiness easier. We’ve helped teams achieve SOC 2 and ISO 27001 compliance via Superset deployments in 8–12 weeks.
Enable self-service analytics: Superset’s SQL Lab and chart builder lower the barrier to ad-hoc queries. Measure adoption of self-service analytics before and after.

Write these targets down. They’ll guide your architecture and help you measure success post-migration.

Scope and Effort Estimation

Migration effort depends on complexity. Use this rough framework:

Looker Component	Superset Equivalent	Effort (per item)	Notes
Simple look (single table, 2–3 filters)	SQL chart	2–4 hours	Straightforward; can be templated
Explore (derived table, custom measures)	dbt model + Superset chart	16–40 hours	Requires data modelling; semantic layer design
Embedded dashboard	Superset API + custom frontend	40–80 hours	Requires session management, auth integration
LookML model (10+ views, 20+ measures)	dbt project + Cube metrics layer	80–160 hours	Largest effort; requires data engineering
User and folder structure	RBAC + database roles	8–16 hours	Can be automated with scripts

For a typical mid-market organisation (50–100 active dashboards, 5–10 explores, 150–300 users), expect 6–12 weeks of effort across a team of 2–4 engineers, plus 2–3 weeks of change management and training.

Stakeholder Buy-In and Governance

Migration is a business decision, not just a technical one. Secure buy-in from:

Finance: Quantify cost savings. If you’re cutting $500K annually, that’s a compelling business case.
Data leadership: Ensure your data team owns the semantic layer design. Superset works best when paired with strong data governance (dbt, data catalogues, lineage tracking).
Business users: Communicate the change early. Superset’s interface differs from Looker; users will need training.
Security and compliance: If you’re moving to self-hosted Superset, security and compliance teams need to review architecture, access controls, and audit logging.

Establish a migration steering committee with representatives from each group. Meet fortnightly. This keeps momentum and surfaces blockers early.

Cost Benchmarking and ROI {#cost-benchmarking}

Looker Cost Model

Looker charges per user per month. Pricing varies by edition (Standard, Advanced, Premium) and commitment level, but typical enterprise pricing is:

Standard: $2,500–$3,500 per user per year
Advanced: $4,000–$5,500 per user per year
Premium: $6,000–$8,000+ per user per year

For a 200-person organisation with 150 active Looker users (typical ratio is 70–80% of headcount), annual spend is:

150 users × $4,000/year = $600,000/year

Add infrastructure (Looker-hosted or on-premise), support, and professional services, and you’re often looking at $750,000–$1,000,000 annually.

Superset Cost Model

Superset’s costs are infrastructure + engineering:

Component	Typical Cost (Annual)	Notes
Kubernetes cluster (3-node, multi-AZ)	$36,000–$60,000	AWS EKS, GCP GKE, or Azure AKS; includes compute, storage, networking
Database (PostgreSQL metadata store)	$3,000–$8,000	Managed RDS or self-hosted; stores Superset configuration, users, dashboards
Data warehouse (Snowflake, Redshift, BigQuery)	$50,000–$200,000+	Existing cost; Superset doesn’t add significant overhead if queries are optimised
Engineering (1–2 FTE)	$150,000–$300,000	Maintenance, security patches, custom features, dashboards
Total	$240,000–$570,000	Varies by scale and data volume

For the same 150-user organisation, Superset costs roughly $300,000–$400,000 annually—a 40–50% reduction.

ROI Calculation

Assuming a 12-month payback period:

Year 1 savings: $600,000 (Looker) – $350,000 (Superset) = $250,000
Migration cost: 10 weeks × 3 engineers × $200/hour (loaded) = $120,000
Net Year 1 benefit: $250,000 – $120,000 = $130,000
Year 2+ annual savings: $250,000 (no migration cost)

This assumes you have existing data warehouse infrastructure and engineering capacity. If you’re hiring, costs shift; if you’re consolidating tools, savings increase.

When Not to Migrate

Superset isn’t always the right move. Skip migration if:

You’re heavily dependent on Looker’s embedded analytics and have 50+ embedded instances in production (rearchitecting is expensive).
Your team lacks SQL expertise or data engineering capacity; Superset requires more hands-on data work than Looker.
You’re on a tight compliance deadline (SOC 2 audit in 8 weeks); Superset self-hosted adds complexity; staying on Looker (Google Cloud-hosted) may be faster.
Your data is entirely on Google Cloud (BigQuery, Dataflow); Looker’s native integration may offer better performance than Superset.

In these cases, negotiate Looker pricing, consolidate user counts, or explore hybrid approaches (Looker for embedded, Superset for internal).

Governance and Security Architecture {#governance-security}

RBAC and Access Control

Looker’s governance model is role-based: Admin, Developer, Viewer, and custom roles with folder-level permissions. Superset’s RBAC is simpler but more flexible.

In Superset, you control access through:

Database roles: Connect Superset to your data warehouse’s native RBAC. If your warehouse (Snowflake, Redshift, BigQuery) has row-level security (RLS) policies, Superset inherits them automatically.
Superset RBAC: Define roles (Admin, SQL Lab User, Dashboard Viewer) and assign datasets to roles. Users in a role can only see and query datasets assigned to that role.
Row-level security (RLS): Use Superset’s RLS feature to filter data by user or department. For example, a sales user sees only their region’s data.

Migration checklist:

Map Looker roles to Superset roles (usually 1:1).
Document folder ownership in Looker; replicate as dataset ownership in Superset.
Implement RLS policies in your data warehouse, not Superset (more performant, easier to audit).
Test access control with a pilot group before full rollout.

Security Audit and Compliance

Superset’s open-source nature means you’re responsible for security patching, dependency management, and audit logging. This is a feature (you control your security posture) and a liability (you must maintain it).

To achieve enterprise security standards, follow the OWASP Application Security Verification Standard and the NIST Cybersecurity Framework:

Authentication: Integrate Superset with your identity provider (Okta, Azure AD, Google Workspace) via SAML or OAuth.
Encryption: Use TLS 1.2+ for all traffic (Superset ↔ browser, Superset ↔ data warehouse). Encrypt credentials at rest (use Superset’s secret manager or AWS Secrets Manager).
Audit logging: Enable Superset’s audit logging; forward logs to your SIEM (Splunk, Datadog, CloudWatch).
Network isolation: Deploy Superset in a private subnet; restrict database access to Superset’s IP range.
Dependency scanning: Use tools like Dependabot or Snyk to monitor Superset’s Python dependencies for vulnerabilities.

For SOC 2 Type II or ISO 27001 compliance, plan for 8–12 weeks of hardening and audit preparation. Many organisations use Vanta to automate evidence collection and continuous compliance monitoring.

Data Residency and Sovereignty

If you’re in Australia or a regulated jurisdiction, data residency matters. Looker (Google Cloud) stores metadata and logs in the US by default; Superset, self-hosted on your infrastructure, keeps everything local.

For Australian organisations, deploy Superset on:

AWS Asia Pacific (Sydney) region
Microsoft Azure Australia East region
On-premise data centres (if you have them)

This ensures all data, logs, and metadata stay within Australian borders, simplifying compliance with Privacy Act and state-level regulations.

Building Your Semantic Layer {#semantic-layer}

Looker’s LookML vs. Superset’s SQL-First Approach

Looker’s strength is its semantic layer (LookML). A single LookML model defines tables, measures, dimensions, and relationships once; all downstream dashboards and explores inherit that definition. This is powerful for governance but requires expertise and maintenance.

Superset is SQL-first. You write SQL queries directly; Superset caches results and builds charts on top. This is faster for ad-hoc analytics but can lead to duplicated logic across dashboards.

To replicate Looker’s semantic benefits in Superset, build a semantic layer using:

dbt: The Data Transformation Layer

dbt (data build tool) lets you define transformations as SQL queries, version-control them, and document relationships. It’s the closest equivalent to LookML.

Migration pattern:

Export LookML models: Extract table definitions, measures, and dimensions from Looker.
Convert to dbt: Rewrite LookML as dbt models (SQL SELECT statements) and metrics (YAML definitions).
Test and document: Use dbt’s testing framework to validate data quality; document lineage and business logic.
Deploy to your data warehouse: dbt materialises models as tables or views in Snowflake, Redshift, or BigQuery.
Connect Superset to dbt models: Point Superset datasets to dbt-generated tables.

Example:

-- LookML measure (Looker)
measure: total_revenue
  type: sum
  sql: ${TABLE}.amount

-- dbt metric (equivalent)
metrics:
  - name: total_revenue
    type: sum
    sql: amount

For a typical Looker migration, converting 50–100 measures to dbt takes 4–8 weeks and requires 1–2 data engineers.

Cube: The Metrics Layer

Cube sits between your data warehouse and Superset, providing a metrics API. It caches query results, handles aggregations, and enforces consistent definitions.

Cube is optional but valuable if you have:

50+ dashboards querying overlapping data (Cube’s caching reduces warehouse load)
Complex metrics (e.g., retention rate, cohort analysis) used across multiple dashboards
Need for sub-second query latency (Cube’s in-memory cache helps)

Migration pattern:

Define cubes: Map Looker explores to Cube cubes (YAML definitions of tables, measures, dimensions).
Deploy Cube: Run Cube as a containerised service (Docker, Kubernetes).
Connect Superset: Use Superset’s Cube connector to query cubes via REST API.

Cube adds operational overhead (another service to monitor, scale, and secure) but can significantly improve performance and consistency. Evaluate whether it’s worth the complexity for your use case.

Semantic Layer Migration Checklist

Audit LookML models; identify reusable measures and dimensions
Convert top 20% of measures to dbt (80/20 rule: highest-impact items first)
Test dbt models against Looker results (validate numerical accuracy)
Document data lineage (where does each table come from?)
Set up dbt testing (data freshness, uniqueness, referential integrity)
Deploy dbt to production; automate runs (daily, hourly, or event-driven)
Connect Superset to dbt models; validate query performance
Evaluate Cube for caching and metrics (optional)

Migration Patterns and Cutover Strategy {#migration-patterns}

The Phased Cutover Approach

Migrating 200+ dashboards overnight is risky. Instead, use a phased approach:

Phase 1: Pilot (Weeks 1–4)

Select 10–15 non-critical dashboards (e.g., weekly team reports, ad-hoc analysis).
Rebuild these in Superset; validate accuracy against Looker.
Recruit 20–30 power users (analysts, data scientists) as beta testers.
Gather feedback; iterate on design, performance, and usability.
Measure time-to-dashboard, query latency, and user satisfaction.

Phase 2: Wave 1 (Weeks 5–8)

Expand to 50–75 dashboards (all non-critical, high-usage content).
Decommission corresponding Looker dashboards (archive them for reference).
Expand user base to 50–100 people.
Monitor Superset performance; tune queries and caching.
Conduct first formal training session (1–2 hours, recorded for asynchronous learning).

Phase 3: Wave 2 (Weeks 9–12)

Migrate remaining 75–125 dashboards (including critical business dashboards).
Cutover all users simultaneously (hard cutover on a Friday afternoon; Looker remains read-only for 2 weeks as a fallback).
Establish Superset as the system of record.
Decommission Looker (or downgrade to a minimal licence for archival access).

Parallel Run Strategy

Run Looker and Superset in parallel for 4–6 weeks. Users access both; you compare results to validate accuracy. This reduces risk but increases operational overhead.

Parallel run checklist:

Rebuild top 30% of dashboards in Superset
Run queries side-by-side; compare results
Document discrepancies (usually due to filter logic or data freshness)
Resolve discrepancies before expanding migration
Communicate to users: “Both systems are live; please report inconsistencies”
After 4 weeks, sunset Looker; migrate remaining dashboards

Cutover Day Playbook

The day you switch off Looker, have a plan:

Morning (6 hours before cutover)

Verify all Superset dashboards load correctly
Run a final data sync (ensure Superset’s metadata is up-to-date)
Brief the team: “Looker goes read-only at 4 PM; Superset is live”
Confirm on-call engineer is available (stay late if needed)

Cutover (4 PM Friday)

Set Looker to read-only (users can view but not edit)
Redirect looker.company.com to a maintenance page with Superset link
Monitor Superset metrics: CPU, memory, database connections, query latency
Have a war room on Slack; monitor user feedback

Evening (Post-cutover)

Respond to support requests (“Where’s my dashboard?” “How do I filter this?”)
Capture common questions; update runbooks
Verify overnight data pipelines (if you have them) run correctly

Week 2 (Post-cutover)

Archive Looker (keep it read-only for reference, but don’t update it)
Conduct a post-mortem: What went well? What was painful? How do we improve?
Plan deprecation: When do you fully shut down Looker? (typically 4–8 weeks after cutover)

Performance Tuning and Optimisation {#performance-tuning}

Query Optimisation

Superset’s performance depends on query efficiency. Slow dashboards are the #1 complaint post-migration.

Optimisation checklist:

Materialised views: Pre-aggregate data in your warehouse (e.g., daily revenue by region). Superset queries materialised views instead of raw tables; results load in <1 second.
Indexing: Ensure your data warehouse indexes frequently-filtered columns (date, region, product category). This dramatically reduces query time.
Partitioning: Partition large tables by date or region. Queries that filter on the partition key scan only relevant data.
Query caching: Enable Superset’s query cache (default: 1 hour TTL). Identical queries served from cache load instantly.
Limit result sets: Set a maximum row limit in Superset (e.g., 10,000 rows). This prevents runaway queries.

Typical optimisations reduce dashboard load time from 10–30 seconds to 1–3 seconds.

Caching Strategy

Superset caches query results in Redis (in-memory cache). Configure caching based on your use case:

Real-time dashboards (stock prices, operational metrics): Disable caching or use 1-minute TTL
Daily reports (revenue, KPIs): Use 1-hour or 4-hour TTL
Historical analysis (trends, cohorts): Use 24-hour TTL or longer

Monitor cache hit rate; aim for 60%+. Low hit rates indicate queries are too varied; consider aggregating data upstream (dbt or Cube).

Infrastructure Scaling

Superset runs on Kubernetes (or Docker Compose for small deployments). Scale based on load:

Development: 1 replica of Superset, 1 replica of Celery worker (async job queue)
Staging: 2 replicas, 2 workers
Production: 3–5 replicas, 3–5 workers; auto-scaling based on CPU/memory

Monitor:

API response time: Target <500ms for dashboard loads
Database connections: Superset should have 5–10 active connections; excess indicates a bottleneck
Celery queue depth: If >100 jobs are queued, add workers

Training, Change Management and Handover {#change-management}

User Training

Superset’s interface is different from Looker. Plan for training:

For dashboard viewers (60% of users):

30-minute recorded video: “Finding and filtering dashboards in Superset”
Live Q&A session (30 minutes, weekly for 4 weeks)
Written guide: “Common questions and answers”

For dashboard creators (30% of users):

2-hour hands-on workshop: “Building charts and dashboards in Superset”
SQL Lab tutorial: “Writing queries and exploring data”
Recorded demos: “Chart types, filters, and drill-down”

For data engineers (10% of users):

Deep-dive session: “Superset architecture, API, security, and scaling”
Hands-on: Setting up dbt, Cube, or custom data sources
Documentation: Runbooks for common operational tasks

Change Management Communication

Start communicating 8 weeks before cutover:

Week 1: Announce migration (why, timeline, benefits)
Week 4: Share pilot results (“Superset is 3× faster, costs 50% less”)
Week 6: Launch training (recorded videos, live sessions)
Week 8: Announce cutover date; provide fallback plan
Cutover week: Daily updates; celebrate successful migration

Use email, Slack, town halls, and one-on-ones. Meet resistance head-on: “I like Looker better” → “What do you miss? Can we replicate that in Superset?”

Handover and Documentation

After cutover, hand over operational responsibility to your team. Provide:

Runbooks: Step-by-step guides for common tasks (add a user, create a dashboard, scale Superset, debug a slow query)
Architecture diagram: Show how Superset connects to data sources, where caching happens, where logs go
On-call playbook: What to do if Superset is down, queries are slow, or a user is locked out
Data dictionary: Document all datasets, tables, and columns available in Superset
Contact list: Who to reach out to for help (your team, Superset community, vendors)

If you’re using a partner like PADISO for platform engineering, ensure they document everything and train your team to be self-sufficient. We typically hand over to your team in weeks 8–10, with ongoing support available via Platform Development in Sydney or your chosen region.

Post-Migration Monitoring and Support {#post-migration}

Metrics to Track

After cutover, monitor:

Metric	Target	Frequency
Dashboard load time (p95)	<3 seconds	Real-time (Datadog, New Relic)
Query success rate	>99%	Daily
User adoption (% active users)	>80%	Weekly
Data freshness (latest data is <1 hour old)	100%	Daily
Infrastructure cost	<$35K/month	Monthly
Support tickets (avg resolution time)	<4 hours	Weekly

Incident Response

Define SLAs for common issues:

Superset down: 15-minute response, 1-hour resolution (critical)
Dashboard slow (>5 sec load): 30-minute response, 4-hour resolution (high)
Data incorrect: 1-hour response, 4-hour resolution (high)
User locked out: 1-hour response, 2-hour resolution (medium)

Have a war room setup (Slack channel, Zoom, incident tracker) and on-call rotation.

Continuous Optimisation

Migration doesn’t end at cutover. Plan for ongoing improvement:

Monthly reviews: Analyse usage patterns, slow queries, and user feedback. Optimise top 5 slow dashboards.
Quarterly architecture reviews: Evaluate Cube, caching strategy, and scaling. Are you hitting infrastructure limits?
Annual cost reviews: Track infrastructure spend; benchmark against Looker costs. Are you achieving ROI?

Common Pitfalls and How to Avoid Them {#common-pitfalls}

Pitfall 1: Underestimating Semantic Layer Complexity

Problem: You rebuild dashboards 1:1 from Looker without investing in dbt or Cube. Result: duplicated SQL across dashboards, inconsistent definitions, hard to maintain.

Solution: Spend 4–6 weeks upfront building a dbt project. Convert top 20% of measures first; expand gradually. This pays dividends later.

Pitfall 2: Ignoring Performance Until Cutover

Problem: Dashboards work fine in staging (small data) but crawl in production (large data). Users complain; you scramble to optimise.

Solution: Load-test with production-scale data 4 weeks before cutover. Identify slow queries early; optimise materialised views, indexes, and caching.

Pitfall 3: Insufficient Change Management

Problem: You cut over to Superset on a Tuesday morning. Users can’t find dashboards; no one knows how to filter. Support is overwhelmed.

Solution: Start change management 8 weeks out. Run pilot with power users. Conduct training 2 weeks before cutover. Have on-call support for 2 weeks post-cutover.

Pitfall 4: Over-Scoping the Migration

Problem: You try to migrate 200 dashboards in 8 weeks. Quality suffers; some dashboards are incorrect or slow.

Solution: Use the 80/20 rule. Migrate high-impact dashboards first (80% of usage). Archive or deprecate low-usage content. Aim for 80% of dashboards in 8 weeks; finish the remaining 20% in weeks 9–12.

Pitfall 5: Neglecting Security and Compliance

Problem: You deploy Superset without RBAC, encryption, or audit logging. Your security team blocks it; compliance audit fails.

Solution: Integrate with your identity provider (Okta, Azure AD) from day 1. Enable audit logging. Conduct a security review with your team before production deployment. For regulated industries, plan for SOC 2 / ISO 27001 audit-readiness early.

Pitfall 6: Forgetting About Embedded Analytics

Problem: You have 20 embedded Looker dashboards (served to customers). You assume Superset embedding works the same way. It doesn’t; you have to rearchitect.

Solution: Identify embedded dashboards early. Plan a separate migration path. Superset’s embedding requires custom frontend code; budget 40–80 hours per embedded dashboard. Consider keeping Looker for embedded instances if the cost is justified.

Next Steps and Getting Started {#next-steps}

Your Migration Roadmap

Use this timeline as a template:

Weeks 1–2: Assessment

Audit Looker estate (dashboards, explores, users)
Define target state (cost, performance, compliance)
Secure stakeholder buy-in
Estimate effort and cost

Weeks 3–4: Architecture and Planning

Design Superset infrastructure (Kubernetes, database, caching)
Plan semantic layer (dbt, Cube, or SQL-first approach)
Define governance model (RBAC, RLS, audit logging)
Set up development environment

Weeks 5–8: Pilot

Rebuild 10–15 dashboards in Superset
Run parallel queries; validate accuracy
Conduct user testing with 20–30 power users
Iterate on design and performance

Weeks 9–12: Wave 1 Migration

Migrate 50–75 high-impact dashboards
Expand user base to 50–100 people
Conduct training; gather feedback
Optimise performance; tune caching

Weeks 13–16: Wave 2 and Cutover

Migrate remaining dashboards
Plan cutover day (Friday afternoon)
Conduct final validation
Execute cutover; monitor for 2 weeks
Decommission Looker

Weeks 17+: Optimisation and Handover

Resolve post-cutover issues
Optimise slow dashboards
Hand over to your team
Plan quarterly reviews

Getting Help

Migration is complex. Consider partnering with a platform engineering team that has done this before. PADISO has led Looker-to-Superset migrations for financial services, retail, and media organisations across Australia and globally.

We offer:

AI Advisory Services: Strategy and architecture for your analytics platform
Platform Development: Hands-on engineering; we build and hand over to your team
Security Audit: SOC 2, ISO 27001, and GDPR compliance via Superset

If you’re in Dallas, we have a platform development team there. Same for Melbourne, Canberra, Gold Coast, New York, Washington DC, Chicago, Austin, Toronto, Ottawa, and Wellington.

We also work with fractional CTO leaders who can guide your team through migration while building internal capability.

Resources and Documentation

Before you start, read:

Apache Superset Documentation: Official guide to Superset’s features, architecture, and deployment
dbt Documentation: How to build a semantic layer with dbt
Cube Documentation: Metrics and caching layer for Superset
Looker Documentation: Reference for LookML models you’re migrating
Cloud Native Computing Foundation: Best practices for containerised Superset deployments

Join the Superset Slack community; ask questions and learn from others’ migrations.

Final Thoughts

Migrating from Looker to Superset is a significant undertaking, but the payoff is substantial: 40–50% cost savings, faster time-to-dashboard, and architectural control. The key is planning thoroughly, scoping ruthlessly, and managing change carefully.

Start with a clear assessment of your Looker estate. Define your target state (cost, performance, compliance). Build a semantic layer (dbt or Cube) to replicate Looker’s governance. Run a pilot with power users. Phase your migration (pilot, wave 1, wave 2). Invest in training and change management. Monitor performance post-cutover.

If you’re ready to start, book a call with our team. We’ve led 20+ Looker-to-Superset migrations and know the pitfalls. We’ll help you scope the effort, design the architecture, and execute the migration—then hand over to your team for long-term success.

Summary

Migrating from Looker to Superset is a 12–16 week project that typically saves 40–50% on analytics spend while improving time-to-dashboard and architectural control. Success requires:

Thorough assessment: Inventory your Looker estate; define target state
Cost benchmarking: Quantify savings; build business case
Governance and security: Plan RBAC, encryption, audit logging, and compliance
Semantic layer: Invest in dbt or Cube to replicate Looker’s governance
Phased cutover: Pilot → Wave 1 → Wave 2 → cutover; parallel run for validation
Performance tuning: Optimise queries, caching, and infrastructure
Change management: Train users, communicate early and often
Post-migration support: Monitor metrics, resolve issues, optimise continuously

Common pitfalls—underestimating semantic layer complexity, ignoring performance, insufficient change management, over-scoping, neglecting security, and forgetting embedded analytics—are avoidable with planning and discipline.

Start your migration today. The sooner you move, the sooner you realise savings and regain control of your analytics platform.

Want to talk through your situation?

Book a 30-minute call with Kevin (Founder/CEO). No pitch — direct advice on what to do next.

Book a 30-min call

Migrating from Looker to Superset for Enterprise Organisations

Migrating from Looker to Superset for Enterprise Organisations

Table of Contents

Why Organisations Move from Looker to Superset {#why-organisations-move}

Pre-Migration Assessment and Scoping {#pre-migration-assessment}

Inventory Your Looker Estate

Define Your Target State

Scope and Effort Estimation

Stakeholder Buy-In and Governance

Cost Benchmarking and ROI {#cost-benchmarking}

Looker Cost Model

Superset Cost Model

ROI Calculation

When Not to Migrate

Governance and Security Architecture {#governance-security}

RBAC and Access Control

Security Audit and Compliance

Data Residency and Sovereignty

Building Your Semantic Layer {#semantic-layer}

Looker’s LookML vs. Superset’s SQL-First Approach

dbt: The Data Transformation Layer

Cube: The Metrics Layer

Semantic Layer Migration Checklist

Migration Patterns and Cutover Strategy {#migration-patterns}

The Phased Cutover Approach

Parallel Run Strategy

Cutover Day Playbook

Performance Tuning and Optimisation {#performance-tuning}

Query Optimisation

Caching Strategy

Infrastructure Scaling

Training, Change Management and Handover {#change-management}

User Training

Change Management Communication

Handover and Documentation

Post-Migration Monitoring and Support {#post-migration}

Metrics to Track

Incident Response

Continuous Optimisation

Common Pitfalls and How to Avoid Them {#common-pitfalls}

Pitfall 1: Underestimating Semantic Layer Complexity

Pitfall 2: Ignoring Performance Until Cutover

Pitfall 3: Insufficient Change Management

Pitfall 4: Over-Scoping the Migration

Pitfall 5: Neglecting Security and Compliance

Pitfall 6: Forgetting About Embedded Analytics

Next Steps and Getting Started {#next-steps}

Your Migration Roadmap

Getting Help

Resources and Documentation

Final Thoughts

Summary

Want to talk through your situation?