Table of Contents
Introduction
For years, dbt Core has been the go-to solution for transforming data with SQL. It brought software engineering practices — like version control, testing, and documentation — into analytics. But as organizations scale across multiple teams, warehouses, BI tools, many have started to hit familiar challenges:
- the same metric appearing with different values in different dashboards.
- pipelines breaking without warning because of hidden upstream changes.
- SQL models becoming harder to maintain as the DAG grows.
- teams relying on separate tools for scheduling, deployment, governance, and lineage.
- development cycles slowing down and becoming error-prone
- rising cloud warehouse bills with limited visibility into cost drivers.
This is where dbt Fusion comes in.
dbt Fusion is the next evolution of dbt — a new, unified transformation engine designed to make analytics engineering faster, safer, and more scalable. It brings together everything teams already love about dbt, while solving the pain points that dbt Core alone can’t handle.
With dbt Fusion, teams get:
- a much faster, Rust-powered engine
- a shared semantic layer for consistent metrics across BI tools
- a hybrid development environment (local + cloud)
- built-in governance, role-based access, and audit logs
- metadata-driven orchestration and impact analysis
Instead of stitching together separate tools for Git, CI/CD, orchestration, documentation, and governance, dbt Fusion puts all of these pieces into one connected platform.
In this guide, we’ll walk through what dbt Fusion is, how it works, and why it represents a major shift in how modern data teams build and maintain analytics pipelines.
Whether you’re new to dbt or an experienced analytics engineer, this guide will give you a clear, practical understanding of why dbt Fusion matters — and how it can transform your workflow.
What Is dbt Fusion?
dbt Fusion is the next major evolution of the dbt ecosystem — a unified, modular data transformation platform designed to help teams build, test, govern, and deploy analytics pipelines more efficiently.
If dbt Core was the foundation of modern analytics engineering, dbt Fusion is the full house built on top of it.
Instead of relying on multiple separate tools for development, scheduling, documentation, approvals, semantic modeling, and governance, dbt Fusion brings all of these capabilities together in one connected system.
At its core, dbt Fusion combines several key components that dramatically improve both development speed and operational reliability:
1. A New Rust-Based Fusion Engine
dbt fusion replaces parts of dbt Core’s Python-based engine with a high-performance system that improves both runtime and reliability.
The Fusion engine is:
- faster (significantly faster parsing and DAG building)
- safer (errors caught at compile time, not run time)
- more intelligent (aware of dependencies, costs, and expected impact)
- more scalable (built for large, multi-team pipelines)
2. A Hybrid Development Environment (Local + Cloud)
dbt Fusion gives developers the flexibility to work however they prefer:
- locally, through the CLI or a local editor
- in the cloud, through a browser-based IDE using dbt Studio
- or switch between both instantly
3. A Centralized Semantic Layer
One of dbt Cloud’s most valuable additions is its universal semantic layer powered by the Fusion Engine, which lets teams define:
- metrics
- dimensions
- business logic
…once, centrally.
These definitions can then be used across BI tools like:
- Looker
- Sigma
- Hex
- Mode
- Tableau
- Power BI
This eliminates metric drift — the common problem where the same KPI shows inconsistent values across different dashboards—by ensuring a single source of truth for all business definitions. Create semantic models in YAML to define your fact tables (entities, dimensions, measures), then define metrics that reference those models.
4. Git-Native Workflows
dbt Cloud previously supported Git, but Fusion makes Git the primary system of record—every model, metric, doc, test, and deployment now runs directly from Git, enabling real CI/CD, branch-based environments, and fully Git-native development workflows.
dbt Fusion integrates Git deeply into the platform:
- branch creation
- commits
- pull requests
- reviews
- approvals
- deployment gates
—all happen within the dbt Cloud interface, without needing to switch tools.
For teams, this means:
- cleaner collaboration
- structured code reviews
- safer deployments
- better visibility into changes
This is especially useful in companies with multiple analytics and engineering teams working in the same project.
5. Metadata-Driven Orchestration
Traditional orchestration tools (Airflow, Prefect, Dagster) rely mostly on DAG structure and schedule definitions.
dbt Cloud’s metadata-aware scheduler uses Fusion engine’s metadata to:
- run only what changed
- avoid unnecessary rebuilds
- detect model dependencies
- skip downstream models when inputs are unchanged
This approach can help save cloud compute costs and dramatically reduce pipeline failures.
6. Built-In Governance (RBAC, Audit Logs, Model Ownership)
dbt Cloud provides:
- role-based access control (RBAC)
- audit trails
- ownership assignment for models
- structured approval processes
- environment isolation (dev/staging/prod)
This makes Fusion enterprise-friendly by ensuring:
- clearer accountability
- compliance support
- fewer accidental changes to critical models
dbt Fusion vs dbt Core
Below is an expanded comparison table with clearer explanations to help both beginners and technical readers understand the differences.
|
Feature
|
dbt Core
|
dbt Fusion
|
Why It Matters
|
|---|---|---|---|
|
SQL Compilation Engine
|
Python-based
|
Rust-based Fusion Engine
|
Fusion is faster, safer, and handles large DAGs more efficiently.
|
|
Semantic Layer
|
Limited (metrics deprecated)
|
Fully integrated, universal semantic layer
|
Prevents metric drift and standardizes KPIs across BI tools.
|
|
IDE
|
Local only (CLI + editor)
|
Hybrid (local + cloud IDE)
|
Faster onboarding, consistent setups, cloud previews, fewer environment issues.
|
|
Governance
|
Manual, scattered across tools
|
Built-in RBAC, audit logs, model ownership
|
Critical for enterprises, compliance-heavy teams, and multi-team collaboration.
|
|
Orchestration
|
Requires external tools (Airflow, Prefect)
|
Native metadata-aware scheduler
|
dbt Cloud includes an integrated scheduler that understands dbt metadata.
|
|
Collaboration
|
Git-only, no environment isolation
|
Staging environments, approvals, per-user sandboxes
|
Prevents developers from stepping on each other’s changes.
|
|
Impact Analysis
|
Limited
|
Full dependency and downstream impact analysis before deployment
|
Prevents breaking dashboards or downstream models.
|
|
Complex CI/CD setup
|
Complex CI/CD setup
|
Simple, built-in deployment workflows with guardrails
|
Clean, predictable releases.
|
|
BI Integrations
|
None
|
Deep integrations with Looker, Sigma, Hex, Mode, Tableau
|
dbt Cloud exposes semantic metrics to BI tools. (Availability may vary by partner)
|
|
Lineage
|
Manual docs + dbt docs site
|
Real-time, automated, metadata-driven lineage
|
Essential for debugging and stakeholder visibility.
|
|
Compliance Support
|
Minimal
|
Enterprise-ready logs and role separation
|
dbt Cloud adds auditability and access controls suitable for regulated industries.
|
dbt Fusion Architecture Overview
Cloud’s architecture, combined with Fusion Engine, is designed to solve a core problem of the modern data stack:
data teams use too many disconnected tools that don’t share the same logic, governance, or metadata.
Fusion replaces this fragmentation with a unified, modular system built on top of a single metadata backbone.
Instead of treating SQL, metrics, documentation, governance, and scheduling as separate layers, dbt Fusion connects them into one coherent architecture.
Fusion is organized into three main layers, all powered by a central metadata engine:
1. Transformation Layer (The Data Modeling Engine)
This is the heart of dbt — where your SQL logic lives. In Fusion, the transformation layer includes everything you expect from dbt Core, but with more intelligence and more safety.
Components inside the Transformation Layer:
- Models (select statements, marts, dims, facts)
- Incremental logic (change-only updates, partition-aware)
- Tests (schema + data tests)
- Macros + Jinja templates
- Seed files
- Packages
- Model ownership & documentation
What Fusion adds on top of dbt Core:
- faster parsing via Rust engine
- compile-time validation (catching errors earlier)
- cost-awareness in behaviour
- lineage-driven decision-making
- impact analysis
- environment isolation
- automatic testing pipelines
Why this matters:
The transformation layer in Fusion isn’t just executing SQL — It interprets your project, understands dependencies and optimizes execution decisions.
This results in:
- fewer broken deployments
- fewer warehouse cost spikes
- faster iteration cycles
- higher developer velocity
The Transformation Layer is where your data modeling lives — but in Fusion, it becomes smarter and safer.
2. Semantic Layer (The Business Logic Brain)
The Semantic Layer is the part of dbt Fusion that standardizes business definitions across your entire organization.
In dbt Core, metrics often get duplicated across:
- Looker
- Tableau
- SQL dashboards
- Stakeholder notebooks
- Individual queries
Fusion solves this by centralizing business definitions.
What the Semantic Layer manages:
- Metrics (e.g., revenue, retention rate, ARPU)
- Dimensions (e.g., region, product category, customer type)
- Hierarchies
- Time-grain logic
- Business rules & KPI logic
- Metric versioning
- Semantic field definitions
Supported BI Tools:
- ✔ Looker
- ✔ Sigma
- ✔ Mode
- ✔ Hex
- ✔ Tableau
- ✔ Power BI (via connectors)
Why this matters:
This layer solves one of the oldest problems in analytics:
“Why does the same metric show different results in different dashboards?”
With dbt Fusion, all dashboards, reports, and ad-hoc queries finally match because they all reference the same semantic definitions.
Fusion provides one source of truth that aligns Finance, Marketing, Product, Sales, and Operations.
3. Orchestration Layer (The Workflow Control Center)
Traditional dbt Core requires additional tools like Airflow, Prefect, or GitHub Actions for scheduling and deployments.
dbt Fusion includes a native orchestration layer built on metadata awareness.
What the Orchestration Layer handles:
- Scheduling (hourly, daily, event-driven)
- Environment-specific runs (dev/stg/prod)
- Automated CI/CD checks
- Approval workflows
- Deferred execution
- Impact analysis based on lineage
- Selective model runs based on what changed
- Job monitoring & logging
Why metadata-aware orchestration is powerful:
Because it understands:
- dependencies between models
- lineage graphs
- semantic relationships
- warehouse costs
- environment context
- upstream data freshness
Fusion can decide:
- what needs to run
- what can be skipped
- what will break
- what will be expensive
- whether downstream dashboards will be affected
The result is fewer runs, lower costs, fewer failures, and cleaner deployments.
The Metadata Layer (The Core of Everything)
All three layers — Transformation, Semantic, Orchestration — rely on Fusion’s central metadata engine.
This metadata layer includes:
- model structures
- SQL parsing results
- lineage graphs
- semantic definitions
- historical run data
- data tests
- documentation
- ownership ( by adding meta: { owner: … } in your dbt model metadata
- governance rules
Why the metadata layer matters:
Metadata enables dbt Fusion to:
- validate SQL before running
- prevent breaking changes
- optimize incremental logic
- track downstream impact
- generate lineage automatically
- enforce governance
- understand which dashboards rely on which models
- coordinate multi-environment deployments
This single source of truth transforms dbt from a modeling tool into an intelligent analytics engineering platform
Summary: Fusion’s Architecture at a Glance
|
Layer
|
Purpose
|
What Fusion Adds
|
|---|---|---|
|
Transformation Layer
|
Transform raw → modeled data
|
Rust engine, cost intelligence, compile-time validation
|
|
Semantic Layer
|
Standardize business metrics & logic
|
Universal metrics, BI integrations, versioning
|
|
Orchestration Layer
|
Schedule, deploy, validate runs
|
Metadata-aware execution, approvals & guardrails, impact analysis, selective model runs, cost-aware scheduling
|
|
Metadata Layer (Core)
|
Connect everything together
|
Lineage, governance, dependencies
|
Everything works together — seamlessly.
Fusion’s architecture is what allows:
- better governance
- faster development
- fewer errors
- consistent metrics
- enterprise-level reliability
No more duct-taping orchestration, semantic tooling, and governance across multiple platforms.
No more metric inconsistencies.
No more manual guesswork around dependencies, impacts, or cost.
dbt Cloud brings all core components of analytics engineering into a single, integrated architecture.
dbt Fusion Use Cases
dbt Fusion isn’t just “dbt Core plus extra features.”
It directly addresses real-world challenges that modern data teams struggle with as they scale.
Below are the most impactful use cases where dbt Fusion shines:
1. Enterprise-Level Data Governance
Large organizations deal with:
- multiple analytics teams
- hundreds of models
- strict compliance requirements
- conflicting definitions of KPIs
- massive pressure to avoid production failures
dbt Cloud introduces a unified system designed to address these enterprise-level needs:
✔ Consistent Metric Definitions
Using the semantic layer, metrics like Revenue, Retention, CAC, or Gross Margin are defined once — and reused across BI tools and SQL queries. So, instead of writing SQL’s, you can query using business terms:
dbt sl query –metrics order_count –group-by metric_time__day
✔ End-to-End Lineage
Fusion automatically maps every upstream and downstream relationship:
- raw → staging → marts
- models → dashboards
- metrics → BI queries
This provides instant visibility into how data flows through the organization.
✔ Role-Based Access (RBAC)
Limit who can:
- edit or change models
- deploy to production
- approve PRs
- edit semantic definitions
- modify environments
Perfect for teams with different roles (analysts, engineers, BI devs, auditors).
✔ Auditability
Fusion logs:
- who made a change
- what was changed
- When it happened
- where it was deployed
- who approved the deployment
This is essential for industries with compliance requirements (HIPAA, SOX, PCI, GDPR).
Best suited for:
Finance, healthcare, insurance, mobility, logistics, banking, and any org with strict compliance or large model surface area.
2. Real-Time & Near Real-Time Analytics
Traditional dbt workflows rely on time-based schedules (hourly, daily, cron-based). While many modern use cases feel event-driven, dbt Fusion does not natively trigger jobs on data arrival events. Instead, it enables near-real-time and incremental analytics through metadata-driven execution control.
dbt Cloud’s metadata-aware scheduler enables intelligent scheduling using Fusion-generated metadata.
Fusion knows:
- what’s changed (code, data, or dependencies)
- what actually needs to run
- what can be skipped
- what will break downstream
✔ Cloud warehouse compute alignment
Snowflake, BigQuery, and Databricks excel at handling micro-batch or incremental transformations.
Fusion plugs into that compute model seamlessly.
Best suited for:
Companies building operational analytics, real-time dashboards, product analytics, or ML feature pipelines.
3. Multi-Team Collaboration (Large Data Orgs)
As teams grow beyond 5–10 contributors, dbt Core workflows quickly break down:
- developers step on each other’s changes
- dev environments drift
- PR approvals become chaotic
- deployments impact multiple teams
- it becomes unclear who owns which model
- semantic drift between teams becomes common
dbt Fusion solves this through:
✔ Isolated Development Environments
Fusion supports branch-based, environment-isolated development, giving each developer or team a dedicated workspace.
✔ Structured Approval Workflows
Fusion allows organizations to define structured PR and deployment policies.
✔ Model Ownership
Every model can have a defined owner.
Fusion can show:
- who is responsible
- who reviews changes
- who gets alerted when something breaks
✔ Production Guardrails
Fusion ensures only approved, reviewed code reaches production.
Best suited for:
Analytics teams with:
- 20–200+ active contributors
- multiple domains (marketing, product, finance, ops)
- complex DAGs
- distributed teams across time zones
4. Self-Service BI Enablement
One of dbt Fusion’s biggest value drivers is its semantic layer.
This centralizes business logic and ensures consistency across all teams and BI tools.
✔ Governed Metrics in BI Tools
Define once in dbt Cloud’s semantic layer → available everywhere:
- Looker
- Sigma
- Tableau
- Power BI
- Hex
- Mode
✔ No More Metric Drift
If Revenue = X in dbt, it will equal X across all dashboards.
No more:
- “Finance says revenue is $4.2M”
- “Marketing sees revenue is $3.9M”
- “BI dashboard shows a third number”
✔ Faster Reporting
Analysts no longer need to rewrite SQL for each question.
They simply reference the governed metrics.
✔ Trusted Dashboards
Once metrics are standardized, trust increases across leadership and stakeholders.
Best suited for:
Organizations with large BI teams, multiple dashboards, or inconsistent KPIs across tools.
Benefits of dbt Fusion for Organizations
dbt Fusion isn’t just an improved development experience — it fundamentally reshapes how data teams build, operate, and scale analytics pipelines.
Below are the key organization-wide benefits, explained in depth.
1. Faster Development & Shorter Iteration Cycles
One of the largest hidden costs in analytics engineering is slow development:
- running a model takes minutes
- debugging takes even longer
- waiting for full DAG rebuilds slows down productivity
- onboarding new contributors takes days
dbt Fusion solves this through:
✔ Lightning-Fast Rust Engine
The new Fusion engine parses models and builds the DAG much faster than Python.
✔ Immediate SQL Feedback
Developers get warnings and errors as they type, not after a run.
✔ Real-Time Previews
You can preview model output instantly, using real warehouse data.
✔ Hybrid IDE
Develop locally or use the cloud IDE with zero setup.
Result:
Developers ship changes in hours instead of days, and the entire development cycle becomes smoother and more enjoyable.
2. Fewer Production Failures (Safer Deployments)
In dbt Core, many issues only surface during execution, such as:
- broken SQL
- missing columns
- invalid references
- schema drift
- upstream changes
- unintentional full table scans
dbt Fusion dramatically reduces failures with:
✔ Compile-Time Validation
Fusion detects errors before the model even runs.
✔ Metadata-Driven Impact Analysis
Fusion shows exactly what will change, what dashboards will be affected.
✔ Isolated Environments
Developers can test safely without touching production.
Result:
Teams spend less time firefighting and more time building.
3. Consistent Metric Logic Across the Entire Organization
Most organizations suffer from “metric drift”.
dbt Fusion’s semantic layer solves this by centralizing:
- metrics
- dimensions
- definitions
- business rules
Once defined in dbt Fusion, a metric becomes reusable across:
- Looker
- Sigma
- Tableau
- Hex
- Power BI
- Mode
✔ Define your business metrics once
✔ Use them everywhere
✔ Guarantee consistency across dashboards
This is one of the biggest benefits of Fusion, especially for leadership, BI teams and organizations struggling with KPI drift across tools
4. Reduced Tooling Sprawl (Less Complexity, Lower Cost)
Without dbt Fusion, teams often rely on:
- a scheduler
- a separate orchestrator (Airflow/Prefect)
- a lineage platform (e.g., Monte Carlo, Atlan)
- a metric layer (LookML or proprietary logic)
- CI/CD tools
- semantic modeling tools
- documentation generators
dbt Fusion replaces some of these native capabilities with:
✔ Metadata-aware orchestrator
✔ Built-in lineage
✔ Universal semantic layer
✔ Version-controlled documentation
✔ Integrated testing
Result:
- fewer tools to maintain
- simpler architecture
- faster troubleshooting
- less training required for new hires
dbt Fusion becomes the central control plane for the modern data stack.
5. Improved Cross-Team Alignment
Organizations with multiple analytics teams often struggle with:
- different modeling standards
- inconsistent definitions
- conflicting SQL styles
- duplicated logic
- unclear ownership
- difficulty reviewing changes
Fusion addresses this with:
✔ Model Ownership
Every asset (model, metric, test) has a defined owner.
✔ Access Controls
Teams only modify what they are responsible for.
✔ Environment Isolation
Prevent accidental overwrites or cross-team interference.
✔ Standardized Development Workflow
Everyone follows the same Git-native approach.
✔ Shared Semantic Layer
BI, engineering, and analytics finally use the same definitions.
Result:
Clear alignment, predictable workflows, and fewer conflicts between teams.
Is dbt Fusion Right for You?
dbt Fusion is not just for large enterprises — it’s for any team that wants cleaner, faster, safer, and more scalable analytics development.
Below is a clear guide to help you understand whether Fusion is a good fit.
dbt Fusion is ideal for teams that:
1. Have multiple contributors or multiple analytics teams
If more than 3–5 people work on your dbt project, Fusion prevents:
- merge conflicts
- dev environment drift
- ownership confusion
- model overwrites
2. Struggle with inconsistent metrics
If different dashboards show different numbers for the same KPI, the semantic layer helps in fixing that issue.
3. Need strong data governance
Fusion’s built-in governance (RBAC, audit logs, approvals) is perfect for:
- finance
- healthcare
- banking
- insurance
- mobility
- logistics
- regulated industries
4. Manage complex DAGs or pipelines
If your DAG has dozens or hundreds of models, the Rust engine provides:
- faster parsing
- clearer lineage
- better incremental logic
- safer deployments
5. Support multiple BI tools
If you use Looker + Tableau + Sigma, Fusion ensures consistent logic across all tools.
Especially valuable for Snowflake and BigQuery users.
Even small teams benefit from dbt Fusion
Fusion is often seen as an enterprise platform, but even small teams gain:
- faster development
- simpler setup
- fewer broken pipelines
- less DevOps overhead
- universal metric definitions
dbt Fusion is a strong fit for teams that are scaling collaboration, complexity, and governance in dbt—and want safer deployments, consistent metrics, and lower operational overhead as their analytics footprint grows.
How DataPrism Helps You Implement dbt Fusion
dbt Fusion introduces a completely new way of modeling, governing, and orchestrating analytics—but adopting it successfully requires more than just switching tools.
It requires the right architecture, testing strategy, semantic definitions, and warehouse optimization patterns.
This is where DataPrism comes in.
DataPrism works with fast-growing startups and established enterprises to help them adopt dbt Fusion smoothly, safely, and strategically.
Our engineering-led implementation approach ensures that every Fusion rollout is:
- future-proof
- aligned with business goals
- optimized for analytics and BI teams
Here’s how we help:
Seamless Migration from dbt Core → dbt Fusion
Migrating an existing dbt Core project into Fusion requires:
- model validation
- semantic layer restructuring
- multi-environment setup
- Git workflow alignment
- dependency and lineage review
- adapter checks (Snowflake, BigQuery, etc.)
DataPrism handles the entire process:
- audit your existing dbt project
- resolving compatibility issues
- reorganize models into Fusion-ready structures
- set up dev → staging → production environments
- configure Fusion’s Rust engine and CI/CD
- validate runs and ensure zero-breaking-change deployment
You get a clean, optimized, Fusion-ready project without the migration headaches.
Semantic Layer Design & Metric Standardization
Most teams struggle with inconsistent KPI definitions across BI tools.
Fusion’s semantic layer solves this — but only if it’s designed correctly.
DataPrism helps you:
- define metrics, dimensions, and hierarchies
- map business logic to semantic entities
- align definitions across finance, product, marketing, and BI
- integrate metrics into Looker, Sigma, Hex, Tableau, and Mode
- establish governance around changes
This ensures one source of truth across the company.
CI/CD & Governance Setup for dbt Fusion
Fusion’s governance and orchestration features are powerful, but they must be configured correctly. DataPrism helps you implement:
- automated testing
- PR approvals
- environment isolation
- RBAC setup
- SSO & team permissions
- schedule & trigger design
- impact analysis gating
- semantic versioning
The result is a deployment pipeline that is safe, predictable, and enterprise-grade.
Full Modern Data Pipeline Redesign
If your data workflows need more than just Fusion, DataPrism also provides:
- ELT redesign with Fivetran/Airbyte
- custom ingestion pipelines
- event streaming integrations
- source-of-truth modeling
- BI enablement strategies
- operational analytics systems
- observability & quality checks
Whether you’re on Snowflake, BigQuery, Redshift, or Databricks, we build architecture that scales as your business grows.
Conclusion
dbt Fusion represents one of the biggest upgrades in modern analytics engineering.
By combining:
- high-performance transformations
- a shared semantic layer
- built-in governance
- metadata-aware orchestration
- hybrid developer workflows
……dbt Fusion brings engineering, BI, and business teams onto a single, consistent platform.
If your organization wants:
- cleaner, modular data pipelines
- consistent and governed metrics
- fewer production failures
- faster development cycles
- reliable cross-team collaboration
- standardized definitions across all BI tools
…then dbt Fusion is likely the future foundation of your data stack.
And with the right partner — the transition can be smooth, efficient, and fully optimized for long-term scalability.
DataPrism can help you design, migrate, optimize, and operationalize dbt Fusion the right way — with engineering excellence and best practices built in.
Frequently Asked Questions (FAQs)
dbt Fusion is the next-generation execution engine for dbt, built in rust that combines development, orchestration, governance, lineage, and semantic modeling into one unified analytics engineering system. It uses a high-performance Rust engine and adds enterprise-grade features that dbt Core doesn’t provide.
dbt Core is primarily a SQL transformation framework.
dbt Fusion is a full transformation platform with:
- a Rust-based compiler
- cloud + local development
- a universal semantic layer
- native orchestration
- governance & RBAC
- cost awareness
- integrated lineage
Fusion eliminates the need for separate tools like Airflow, Monte Carlo, or LookML for many workflows.
Not necessarily. Most dbt Core projects can be migrated with some restructuring.
DataPrism typically performs a:
- model audit
- dependency check
- semantic layer mapping
- environment setup
- CI/CD alignment
The migration usually takes days, not months.
No. Smaller teams benefit from:
- faster development
- fewer broken pipelines
- zero local setup
- built-in governance
- consistent metric logic
Fusion scales from 2-person analytics teams to 200-person data organizations.
For many teams — yes.
Fusion includes metadata-aware orchestration that can schedule jobs, run only what changed, detect impacts, and prevent expensive queries.
However, complex cross-system orchestration may still use external orchestrators.
Fusion natively integrates with:
- Looker
- Sigma
- Hex
- Tableau
- Mode
Your KPIs and definitions stay consistent across all dashboards.
dbt Fusion introduces:
- cost estimation before runs
- avoidance of unnecessary rebuilds
- metadata-aware selective execution
- better incremental logic
- warnings for expensive queries
Teams often see reduced compute usage immediately.
Fusion makes analytics engineering more accessible through:
- cloud IDE
- governed semantic metrics
- real-time previews
- standardized templates
- structured development workflows
Analysts can contribute safely without breaking core models.
Yes. Fusion can trigger runs based on metadata changes or new data arrivals.
The platform understands DAG dependencies and freshness signals, enabling micro-batch or near-real-time execution.
For a normal dbt project (40–150 models), the migration takes:
- 1–2 weeks for setup, validation, and environment configuration
- 2–3 weeks for semantic layer creation and orchestration alignment
DataPrism offers a structured migration plan to handle everything end-to-end.
Yes.
Fusion is fully Git-native and supports:
- GitHub
- GitLab
- Bitbucket
- Azure DevOps
All commits, PRs, approvals, gates, and deployments are managed through Git.
No.
dbt Core is open-source.
dbt Fusion is a commercial, enterprise edition built on top of Core with a fully integrated platform.
Yes — dbt Fusion is the evolution of dbt Cloud.
dbt Cloud → dbt Fusion (new architecture, new engine, new features).
dbt Fusion supports:
- Snowflake
- BigQuery
- Databricks
- Redshift
- Postgres
Cost intelligence is strongest on Snowflake & BigQuery.
Because DataPrism specializes in:
- Core → Fusion migrations
- semantic layer design
- cost optimization
- CI/CD and governance setup
- warehouse performance design
- BI alignment and KPI standardization
We don’t just move your project — we upgrade it.