Migrate to Analytify: 2026 Migration Checklist for Tableau, Looker, Power BI & Metabase
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Migrating from Tableau to Analytify
Why teams migrate from Tableau
Most teams migrate from Tableau because of (1) per-user licence costs that scale brutally with company size, (2) limited embedded analytics options without paying for Tableau Embedded, (3) no native AI/GenBI grounded on governed metrics, and (4) workbook sprawl that turns governance into a perpetual catch-up project. Analytify replaces all of that with one platform: per-user pricing, built-in embedded SDK, semantic-layer-native AI assistant, and governance enforced at the model not the workbook.
Tableau → Analytify Compatibility
- Tableau workbooks → Analytify dashboards (manual rebuild on the same warehouse data; no auto-import — but typically faster than fixing the original)
- Hyper extracts → live warehouse queries (Snowflake, BigQuery, Databricks, Postgres native)
- Tableau calculated fields → semantic-layer measures (cleaner, governed, single source of truth)
- Tableau Server / Tableau Cloud → Analytify Cloud or self-hosted (VPC, SOC 2)
- Row-level security in Tableau → row-level security in Analytify, defined in code
Step-by-Step Migration Checklist
- List your top 20 Tableau dashboards by usage (Tableau Cloud > Admin > Views, sort by view count)
- Identify the 3-5 underlying datasources / extracts each dashboard depends on
- Confirm those data sources are reachable from a cloud warehouse (Snowflake/BigQuery/Databricks/Postgres). If not, move them via Fivetran/Airbyte first
- Map each Tableau calculated field to a semantic-layer measure in Analytify YAML
- Rebuild the top 20 dashboards in Analytify (parallel-running with Tableau for 4-8 weeks for confidence)
- Migrate row-level security: export Tableau RLS rules → re-implement in Analytify’s YAML / warehouse RLS
- Move scheduled report subscriptions (replace Tableau email subscriptions with Analytify scheduled exports / Slack delivery)
- Cut over: switch dashboard URLs, retire Tableau workbooks, downgrade Tableau licence
Common Pitfalls to Avoid
Typical Timeline
| Phase | Focus | What you’re doing |
|---|---|---|
| Week 1-2 | Audit + scope | List top 20 dashboards, map data sources, confirm warehouse connectivity |
| Week 3-4 | Semantic layer | Define core metrics + dimensions + RLS in Analytify YAML |
| Week 5-8 | Rebuild dashboards | Top 20 dashboards rebuilt; validate parallel against Tableau |
| Week 9-10 | User training + soft cutover | Train heavy users; switch dashboard URLs; both tools running |
| Week 11-12 | Retire Tableau | Decommission Tableau Server / Cloud licence; archive workbooks |
Ready to map your migration?
30-minute call with a senior solutions engineer. We’ll review your current stack, identify the top 5-10 dashboards to migrate first, and walk through the semantic-layer + embedded analytics architecture for your scenario. No card, no commitment.
Migrating from Looker to Analytify
Why teams migrate from Looker
Migrations from Looker accelerated after Google’s pricing changes and the deprecation of the standalone Looker product. Teams move because of (1) seat costs, (2) LookML lock-in, (3) limited generative AI grounded on Looker semantic models, and (4) the desire for a self-hostable alternative. Analytify’s built-in semantic layer reads dbt YAML metrics natively and offers an open-source path with no licence ceiling.
Looker → Analytify Compatibility
- LookML model → Analytify semantic layer (dbt-compatible YAML; native dbt metric reading)
- Looker Explores → Analytify dashboards / explore-style views
- Persistent derived tables (PDTs) → dbt models or Snowflake/BigQuery materialised views
- Looker Embedded → Analytify embedded SDK (often cheaper, more flexible)
- Looker dashboards / Looks → rebuild in Analytify (manual but fast on shared semantic layer)
Step-by-Step Migration Checklist
- Export your LookML repository (git access — your team already has this)
- Audit which Explores are actually used (Looker Admin > Usage > Recently viewed)
- Translate LookML measures and dimensions to Analytify semantic-layer YAML (most patterns map 1:1)
- Move PDTs to dbt models that materialise into your warehouse
- Rebuild dashboards in Analytify on the new semantic layer
- For embedded use cases: swap Looker Embedded SDK for Analytify embedded SDK (URL-signed iframe or JS SDK)
- Migrate row-level filters: Looker user attributes → Analytify RLS policy with the same JWT claims
- Cut over: redirect dashboard URLs, retire Looker instance, save 50-70% on annual cost
Common Pitfalls to Avoid
Typical Timeline
| Phase | Focus | What you’re doing |
|---|---|---|
| Week 1-2 | Audit + scope | List Explores by usage, export LookML repo, map PDTs |
| Week 3-5 | Semantic layer migration | Translate LookML to dbt + Analytify YAML for top 10 Explores |
| Week 6-8 | Rebuild dashboards | Top dashboards rebuilt; embedded use cases swapped to Analytify SDK |
| Week 9-10 | User training + cutover | Train Looker analysts on Analytify; switch URLs |
| Week 11-12 | Retire Looker | Decommission instance; downgrade Google Cloud spend |
Ready to map your migration?
30-minute call with a senior solutions engineer. We’ll review your current stack, identify the top 5-10 dashboards to migrate first, and walk through the semantic-layer + embedded analytics architecture for your scenario. No card, no commitment.
Migrating from Power BI to Analytify
Why teams migrate from Power BI
Power BI works well for Microsoft-centric reporting, but teams migrate when (1) they need true embedded analytics for non-Microsoft customers, (2) Power BI Premium capacity costs balloon, (3) they want governed metric definitions that survive workbook copy-paste, or (4) they need self-hosted / VPC deployment Power BI doesn’t support. Analytify is warehouse-native, has a code-first semantic layer, and offers SOC 2 + HIPAA-eligible deployment outside the Microsoft tenant.
Power BI → Analytify Compatibility
- Power BI datasets → Analytify semantic-layer measures (DAX → semantic-layer YAML; many measures translate cleanly)
- Power BI dataflows → dbt models in your warehouse
- Power BI reports → Analytify dashboards (rebuild on shared semantic layer)
- Power BI Embedded → Analytify embedded SDK (typically simpler embed flow + lower cost)
- Row-level security in PBIX → Analytify RLS policy in YAML / warehouse
Step-by-Step Migration Checklist
- List Power BI workspaces and datasets by usage (Power BI Service > Usage metrics)
- Export DAX definitions for top measures (use Tabular Editor or Power BI Desktop “External Tools”)
- Translate DAX measures to Analytify semantic-layer YAML — most aggregations and filters port cleanly
- Move dataflows to dbt models running on your cloud warehouse
- Rebuild top reports in Analytify (parallel run with Power BI for verification)
- For Power BI Embedded scenarios: swap Power BI Embedded SDK for Analytify embedded SDK — usually cuts capacity costs significantly
- Migrate RLS rules from PBIX to Analytify policies (often 1:1 translation)
- Cut over: redirect URLs, retire Power BI Premium capacity or downgrade Pro licences
Common Pitfalls to Avoid
Typical Timeline
| Phase | Focus | What you’re doing |
|---|---|---|
| Week 1-2 | Audit + scope | Top workspaces by usage; export DAX; map dataflows |
| Week 3-5 | Semantic layer migration | DAX → YAML for top 10 datasets |
| Week 6-8 | Rebuild reports | Top reports + embedded use cases |
| Week 9-10 | User training + cutover | Train Power BI users; switch URLs |
| Week 11-12 | Retire Power BI | Decommission Premium capacity / downgrade Pro licences |
Ready to map your migration?
30-minute call with a senior solutions engineer. We’ll review your current stack, identify the top 5-10 dashboards to migrate first, and walk through the semantic-layer + embedded analytics architecture for your scenario. No card, no commitment.
Migrating from Metabase to Analytify
Why teams migrate from Metabase
Metabase is a great starter BI tool but teams typically outgrow it when (1) they need a real semantic layer (Metabase’s metric definitions are dashboard-local), (2) embedded analytics requires fewer compromises (Metabase Embedded has limits on customisation and multi-tenancy), (3) they want native AI/GenBI grounded on governed metrics, or (4) they need enterprise compliance (SOC 2, HIPAA). Analytify is the natural next step that keeps the open-source ergonomics.
Metabase → Analytify Compatibility
- Metabase questions → Analytify dashboards (often quicker rebuild than refactoring queries)
- Metabase models → Analytify semantic-layer measures (with cleaner versioning via git)
- Metabase metrics (legacy) → Analytify governed metrics (single source of truth across all dashboards)
- Metabase Embedded → Analytify embedded SDK (with multi-tenant RLS, white-label theming)
- Metabase native queries (SQL) → semantic-layer queries (or keep as raw SQL where it’s the cleanest path)
Step-by-Step Migration Checklist
- Audit usage (Metabase Admin > Audit Log) to find top 20 questions/dashboards
- Identify which questions rely on Metabase models vs raw SQL
- Build the semantic layer in Analytify (define governed metrics + dimensions in YAML)
- Rebuild top dashboards in Analytify; many will simplify because semantic layer eliminates duplicate metric logic
- For embedded use cases: swap Metabase Embedded for Analytify embedded SDK; gain multi-tenant RLS and white-label control
- Migrate scheduled subscriptions / alerts
- Cut over: retire Metabase or keep it as a free tool for ad-hoc analyst exploration alongside Analytify’s governed layer
Common Pitfalls to Avoid
Typical Timeline
| Phase | Focus | What you’re doing |
|---|---|---|
| Week 1-2 | Audit + scope | Top 20 questions, identify metrics layer needs |
| Week 3-4 | Semantic layer + rebuild | Build Analytify YAML, rebuild top dashboards |
| Week 5-6 | Embedded swap (if applicable) | Replace Metabase Embedded with Analytify SDK |
| Week 7-8 | Cutover or coexist | Migrate users; decide whether to retire Metabase |
Ready to map your migration?
30-minute call with a senior solutions engineer. We’ll review your current stack, identify the top 5-10 dashboards to migrate first, and walk through the semantic-layer + embedded analytics architecture for your scenario. No card, no commitment.
Why Migrate to Analytify (the Common Reasons)
Across all four source tools, the four most common drivers we hear:
- Predictable per-user pricing. No surprise capacity bills, no per-row meters, unlimited end-user/embedded viewers.
- Single platform for internal BI + embedded analytics. Stop stacking Tableau + Looker Embedded + custom builds. One semantic layer, one product.
- AI assistant grounded on governed metrics. GenBI that doesn’t hallucinate because it queries your semantic layer, not raw tables.
- Open-source core + self-hosting option. Auditable code, no vendor lock-in, VPC deployment for regulated industries.
Migration Risk: Things That Always Bite Teams
- Underestimating semantic-layer work. Most teams discover their existing metric logic is duplicated 5-10x across dashboards. Use the migration to consolidate.
- Not running both systems in parallel. Cut-over-on-one-day migrations fail. Plan 4-8 weeks of parallel run.
- Leaving security migration for the end. Move RLS / entitlements to Analytify in week 3-4, not week 11. Auditors will catch a gap.
- Skipping the dashboard audit. 30-50% of dashboards in any incumbent BI tool are dead weight. Migrating them just moves the cruft.
What’s in a Migration Demo
If you book a migration demo (link below), our SE will:
- Review your current architecture (BI tool, warehouse, embedded use cases, source systems)
- Identify the top 5-10 dashboards or use cases to migrate first (highest-value, lowest-risk)
- Walk you through your specific tool → Analytify mapping (DAX/LookML/calc fields → semantic-layer measures)
- Show the embedded SDK if relevant + multi-tenant RLS
- Talk through pricing for your scenario; many teams cut their BI bill by 50-70% post-migration
- Hand you a written migration plan within 24 hours of the call
Ready to map your migration?
30-minute call with a senior solutions engineer. We’ll review your current stack, identify the top 5-10 dashboards to migrate first, and walk through the semantic-layer + embedded analytics architecture for your scenario. No card, no commitment.
FAQs
How long does a typical migration take?
5-12 weeks depending on number of dashboards, embedded use cases, and team size. Most teams complete the first 80% of value in the first 6 weeks.
Will I lose dashboards or data during migration?
No. Both systems run in parallel during the migration window. You only retire your old BI tool when you’re confident every critical dashboard is migrated and verified.
Do you offer migration services or do I do it myself?
Both. You can do it yourself with documentation and Slack support, or work with our solutions team for a guided migration (typically 4-8 weeks of paid services for mid-market, 8-16 weeks for enterprise).
What about my custom calculations and formulas?
Most calculations port cleanly to the semantic-layer YAML. Complex DAX or LookML constructs may need rewrites, but the migration is also an opportunity to clean up duplicate / inconsistent metric logic.
Can I migrate one team or business unit at a time?
Yes — many enterprise migrations roll out by business unit. Analytify supports running alongside your existing tool indefinitely.
How do I get a written migration plan?
Book a migration demo via the buttons on this page. Our SE will follow up within 24 hours with a written plan tailored to your stack and goals.