dbt Labs vs Improvado: Which Is Right for Enterprise Marketing Teams? (2026)

dbt Labs and Improvado both appear in marketing data stacks, but they solve fundamentally different problems. dbt is a transformation-focused tool for analytics engineers who write SQL models in your data warehouse. Improvado is an end-to-end marketing intelligence platform that extracts, transforms, and delivers insights — no SQL required for marketers, full SQL access for engineers. This comparison breaks down when each tool fits, what they cost, and where the architectural divide matters most.

dbt Labs vs Improvado: Choosing the Right Marketing Data Platform

dbt transforms data that's already in your warehouse. Improvado extracts from 500+ marketing sources, transforms it with pre-built marketing models, and delivers governed insights. Same goal — unified marketing analytics — entirely different starting points and skill requirements.

Full disclosure: we're Improvado, and this page is written from our perspective. We've tried to represent dbt Labs' capabilities accurately — and where we've gotten it wrong, email us and we'll fix it. Our goal is to help you make the right call, even if that's not us.

Quick Verdict: dbt Labs vs Improvado

Feature Comparison: Improvado vs dbt Labs

This table compares core capabilities across the evaluation criteria that matter most when choosing between a transformation-focused tool and a full marketing data platform.

Feature comparison: Improvado vs dbt Labs (updated February 2026)

Where Improvado and dbt Labs Diverge: 4 Key Differences

Your Marketing Team Operates the Full Pipeline — No Engineering Tickets Required

dbt is a transformation tool. It doesn't extract data from Facebook Ads, Google Analytics, Salesforce, or any marketing platform. You need Fivetran, Stitch, Airbyte, or a custom-built extraction layer to get data into your warehouse before dbt can touch it. That's two vendors, two contracts, two support relationships — and when a connector breaks or a platform changes its API, your marketing team waits for engineering to triage.

Improvado owns extraction, transformation, and delivery. The platform maintains 500+ pre-built connectors covering ad platforms (Meta, Google, TikTok, LinkedIn), analytics tools (GA4, Adobe Analytics), CRM systems (Salesforce, HubSpot), and niche channels like podcast analytics and affiliate networks. When TikTok releases a new API version or Meta deprecates a field, Improvado's engineering team updates the connector — your dashboards don't break, and your marketing team never files a ticket.

The difference shows up in time to value. A marketing ops manager setting up cross-channel reporting in Improvado connects sources via UI, maps fields with visual recipes, and sees data flowing to Tableau in hours. The same workflow with dbt requires: (1) setting up Fivetran connectors, (2) writing dbt models to join and transform the raw tables Fivetran created, (3) orchestrating the pipeline, (4) connecting the BI tool. Minimum two-week sprint with SQL expertise required.

Improvado review

“Improvado’s connectors were huge for us in overcoming the limitations of our previous platform. I don't think we would have been able to get as far with data as we are now.”

Shayna Tyler

Analyst

Marketing Data Governance That Prevents Budget Errors Before Launch

dbt handles generic data quality: check for nulls, enforce unique keys, validate foreign key relationships. It's excellent at ensuring your warehouse schema is clean. It doesn't know that "cost per conversion" should exclude brand search spend, or that UTM parameters need consistent taxonomy across 30 campaigns, or that a $50,000 daily budget on a test campaign is probably a typo.

Improvado's Marketing Data Governance framework includes 250+ pre-built validation rules designed for marketing workflows. Budget anomaly detection catches when a campaign is set to spend 10× the historical average. Taxonomy parsers extract structured dimensions from messy campaign naming conventions (extracting region, product line, and audience segment from "EMEA_ProductA_Retargeting_Q1"). Timezone normalization ensures time-series analysis doesn't break when comparing data from ad platforms reporting in PST, CRM data in UTC, and Google Analytics in the user's local time.

One Improvado customer avoided a $120,000 budget error when the governance layer flagged an accidentally duplicated campaign before it went live. dbt would have successfully loaded that erroneous data into the warehouse — clean, tested, documented — because the data quality checks don't understand marketing intent.

No-Code for Marketers, Full SQL for Engineers — Not an Either/Or

dbt's interface is a SQL editor. dbt Cloud adds a browser-based IDE, Git integration, and a catalog, but the core workflow is writing SELECT statements, defining Jinja macros, and managing YAML configuration files. If your marketing analyst doesn't know SQL, they're blocked. The tool is explicitly designed for analytics engineers, not business users.

Improvado offers two interfaces in the same platform. Marketing ops managers use visual data blending — drag sources together, define join keys with dropdowns, create calculated fields (like "cost per MQL") without touching code, preview results before activating the recipe. Data engineers who need to handle edge cases the visual builder can't express drop into the SQL editor, write custom transformations, and version-control them the same way dbt users do.

The dual-persona design means marketing teams ship reports faster (no SQL learning curve, no engineering dependency), while data engineers retain full control for complex use cases. A typical workflow: marketing ops builds 80% of transformations via the visual interface; engineering writes custom SQL for multi-touch attribution logic or anomaly detection algorithms. Both layers coexist in the same pipeline, governed by the same draft/active workflow.

Improvado review

“Improvado gives us the freedom to use our data how we want. It puts the control back in our hands. We get to choose the options instead of being limited to pre-set ones.”

Tyler Corcoran

Attribution Models and MMM Built In — Not a Side Project

Marketing teams need attribution. dbt can help you build it — by writing SQL that joins ad clicks to CRM conversions, applies time-decay weighting, and handles multi-touch journeys. But you're writing that logic from scratch. There's no pre-built attribution module in dbt Core or dbt Cloud. Every first-touch, last-touch, linear, or custom model is a custom SQL project. Marketing Mix Modeling? Same story: you're building it yourself or integrating a third-party tool.

Improvado includes configurable attribution models as a platform feature. Select first-touch, last-touch, linear, time-decay, or define custom weighting rules — the platform handles the join logic, deduplication, and lookback windows. For teams running incrementality tests or combining MTA (multi-touch attribution) and MMM (Marketing Mix Modeling), Improvado supports both methodologies in the same environment. You're not stitching together three vendors to answer "which channels drive incremental revenue."

The difference compounds at scale. An enterprise with 40+ paid channels and a 90-day customer journey needs attribution logic that handles cross-device tracking, offline conversions, and channel interaction effects. Building that in SQL is a six-month engineering project. Improvado's attribution engine is production-ready and customizable for your business model — whether you're B2B SaaS with long sales cycles or e-commerce with same-day conversions.

When to Choose dbt Labs

dbt Labs is the right choice in specific scenarios where its transformation-focused architecture aligns with your team structure and existing infrastructure.

• Your data engineering team owns the transformation layer. You have SQL-proficient analytics engineers who maintain modular, version-controlled data models as part of their core workflow.
• Extraction is already solved. You're using Fivetran, Stitch, Airbyte, or custom-built connectors to load data into Snowflake, BigQuery, or Redshift — and those pipelines are stable.
• You need deep transformation governance. Your use case requires column-level lineage, comprehensive testing coverage, and CI/CD for SQL models — and you have the team to maintain that infrastructure.
• Your analytics span beyond marketing. dbt excels when transformations cover product analytics, finance, operations, and marketing in one unified layer — it's data-warehouse-native, not marketing-specific.
• Budget prioritizes open-source flexibility. dbt Core is free; dbt Cloud's Starter tier is $100/user/month. If you're optimizing for low upfront cost and have engineering capacity to self-manage orchestration and lineage, dbt's pricing model is compelling.

What Customers Say: Improvado in Production

Teams using Improvado report measurable improvements in reporting speed, data trust, and cross-functional collaboration. Here's what the shift from fragmented tools to a unified platform looks like in practice.

Case study

At ASUS, Enhanced data availability not only enabled faster experimentation but also allowed teams across regions to "speak the same language." This increased transparency in global marketing efforts provides a strong foundation for innovative, data-driven strategies that maximize the impact of global marketing investments. Read full case study →

"Now, we don't have to involve our technical team in the reporting part at all. Improvado saves about 90 hours per week and allows us to focus on data analysis rather than routine data aggregation, normalization, and formatting."

Jeff Lee

ASUS

Case study

At Signal Theory, for most clients, the reporting process is now fully automated, taking 30 minutes to an hour at most. For clients with unique data sources requiring manual input, the system is flexible enough to accommodate specific needs without compromising overall efficiency. Read full case study →

“Reports that used to take hours now only take about 30 minutes. We're reporting for significantly more clients, even though it is only being handled by a single person. That's been huge for us.”

Shayna Tyler

Analyst, Signal Theory

Case study

Yodel Mobile relies on Improvado for daily budget pacing and campaign optimization. Having centralized, reliable data allows them to make quick adjustments to ensure campaigns stay on track. Read full case study →

“Improvado allows us to have all information in one place for quick action. We can see at a glance if we're on target with spending or if changes are needed—without having to dig into each platform individually.”

Kasia Pasich

Data Analyst, Yodel Mobile

These outcomes — 90% time savings, faster reporting, unified visibility — aren't edge cases. They're the result of eliminating the hand-offs between extraction, transformation, and governance that multi-tool stacks require.

Pricing Comparison: dbt Labs vs Improvado

dbt Labs Pricing

dbt offers tiered pricing based on developer seats, successful models built per month, and queried metrics. dbt Core remains free and open-source.

• Developer Plan: Free (1 seat, 3,000 successful models/month). Includes basic IDE, CLI, VSCode extension. 14-day trial available for paid tiers.
• Starter Plan: $100 per user/month (5 seats, 15,000 models/month, 5,000 metrics/month, 1 project). Adds dbt Catalog, Semantic Layer basics, and dbt Copilot (AI code generation).
• Enterprise Plan: Custom pricing, starting around $50,000/year. Includes 100,000 models/month, 20,000 metrics/month, 30 projects. Advanced features: dbt Canvas, Insights, Mesh (cross-project), cost optimization, premium support.
• Enterprise+ Plan: Custom pricing. Unlimited projects, PrivateLink, IP restrictions, rollback, hybrid deployments.

Hidden costs to factor: dbt pricing doesn't include warehouse compute (Snowflake, BigQuery, Redshift — budget $18,000+/year for mid-sized teams), job orchestration overages (~$3,600/year for growing teams), or premium support ($200–$400/hour). A 12-seat team building 95,000 models monthly can expect ~$2,000/month in dbt Cloud fees alone, before warehouse and tooling costs.

Improvado Pricing

Improvado uses outcome-based pricing tied to the number of data sources, transformation complexity, and deployment scale. Pricing includes:

• 500+ pre-built connectors with proactive API maintenance (no per-connector fees)
• Custom connector builds in 2–4 weeks (SLA-backed)
• Dedicated Customer Success Manager and professional services (not an add-on)
• Marketing Data Governance framework (250+ validation rules)
• No-code transformation recipes + full SQL access
• Enterprise compliance: SOC 2 Type II, HIPAA, GDPR certified

Pricing is customized based on your data sources, user count, and deployment model (cloud or on-premise). Contact Improvado for a detailed quote. See pricing details.

Total Cost of Ownership

When comparing TCO, factor in:

• dbt Labs: Seat licenses + warehouse compute + upstream ETL tool (Fivetran, Stitch) + engineering time to build/maintain models + BI tool licenses. A mid-sized team (10–15 users, 50+ sources) typically spends $60,000–$100,000/year across the stack.
• Improvado: Single platform fee covering extraction, transformation, governance, and support. No per-connector fees, no separate ETL vendor, CSM included. Mid-sized teams typically see 30–40% lower TCO vs. multi-tool stacks when factoring in engineering time saved.

Frequently Asked Questions: Improvado vs dbt Labs

What is the main difference between Improvado and dbt Labs?

dbt Labs is a transformation-only tool for analytics engineers who write SQL models in a data warehouse. Improvado is an end-to-end marketing intelligence platform that extracts data from 500+ sources, transforms it with no-code and SQL tools, applies marketing-specific governance, and delivers insights — handling the full pipeline marketing teams need without requiring SQL proficiency or upstream ETL vendors.

Can I use dbt Labs without a separate ETL tool?

No. dbt transforms data that's already in your warehouse — it doesn't extract data from marketing platforms, CRMs, or analytics tools. You need Fivetran, Stitch, Airbyte, or custom-built connectors to load data before dbt can transform it. Improvado eliminates this dependency by owning extraction and transformation in one platform.

Does Improvado require SQL knowledge?

No for marketers, yes for engineers who want it. Improvado offers a no-code visual interface for blending data, creating calculated fields, and building cross-channel reports — marketing ops teams use it without writing SQL. Data engineers have full SQL access for custom transformations and edge cases the visual builder can't handle. Both interfaces coexist in the same platform.

How does Improvado's Marketing Data Governance compare to dbt's data quality tests?

dbt provides generic data quality checks (not null, unique, foreign key relationships) designed for warehouse schema validation. Improvado's governance includes 250+ marketing-specific rules: budget anomaly detection, UTM taxonomy parsing, timezone normalization, pre-launch validation for campaigns, and metric consistency checks (ensuring "cost per acquisition" is calculated the same way across all dashboards). dbt ensures clean data; Improvado ensures marketing-ready data.

Can Improvado and dbt Labs be used together?

Yes, but it's rarely necessary. Some teams use Improvado to extract and normalize marketing data, then push it to a warehouse where dbt handles cross-functional transformations (combining marketing, product, and finance data). However, most marketing teams find Improvado's transformation layer sufficient — eliminating the need for dbt and the engineering overhead it requires. The decision depends on whether your analytics scope extends significantly beyond marketing.

What kind of support does Improvado provide compared to dbt Labs?

Improvado includes a dedicated Customer Success Manager and professional services team with every deployment — proactive support, connector maintenance, and strategic guidance are part of the platform fee. dbt Labs offers community support and ticket-based assistance on Starter plans; Enterprise customers get SLAs and training, but premium support costs $200–$400/hour. Improvado's support model is white-glove by default; dbt's support scales with tier.

How long does it take to migrate from dbt Labs to Improvado?

Migration depends on the complexity of your existing dbt models and the number of data sources. A typical mid-sized deployment (20–40 sources, moderate transformation logic) migrates in 4–6 weeks. Improvado's professional services team maps your dbt transformation logic to Improvado recipes, connects sources via pre-built connectors, and validates output before cutting over. The process is phased — you can run both systems in parallel during validation.

When does dbt Labs make more sense than Improvado?

dbt makes more sense when you have a data engineering team that owns SQL transformations, extraction is already solved via Fivetran or custom pipelines, and your analytics scope extends across product, finance, and operations — not just marketing. dbt is data-warehouse-native and excels at cross-functional transformation layers. Improvado is purpose-built for marketing teams who need the full pipeline without engineering dependencies.

The Decision: Transformation Layer vs. Full Marketing Platform

dbt Labs and Improvado solve different parts of the marketing data problem. dbt is a best-in-class transformation tool for teams with SQL expertise and a stable extraction layer. Improvado is a complete marketing intelligence platform that removes the need for separate ETL vendors, eliminates SQL blockers for marketing teams, and delivers governed, attribution-ready insights out of the box.

If your data engineering team owns transformations and you're optimizing for flexibility across the entire business (not just marketing), dbt's architecture makes sense. If your marketing team needs to move fast, trust the data, and avoid engineering dependencies, Improvado's end-to-end approach delivers faster time to value and lower total cost of ownership.

The clearest signal: how much of your team's time goes to maintaining the pipeline vs. using the insights it produces. If you're spending more hours debugging connector failures and writing SQL than analyzing performance, the platform choice needs to shift.