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

Airbyte + dbt represents the modern data stack approach: extract and load with Airbyte, transform with dbt, orchestrate with Airflow. Improvado bundles the entire marketing data lifecycle—extraction, transformation, governance, and intelligence—into a single, marketing-native platform. Both handle data movement and transformation, but they target fundamentally different personas and operational models. This comparison breaks down where each solution wins, what they cost, and how to decide which architecture fits your team.

Airbyte + dbt vs Improvado: The Core Difference

Airbyte + dbt is an engineering-led stack that gives data teams full control over the ELT pipeline. Improvado is a purpose-built marketing analytics platform that eliminates the need for data engineering in the marketing data workflow. If your data team wants to own transformation logic in SQL and Python, Airbyte + dbt is the natural choice. If your marketing team needs self-serve access to clean, governed data without filing engineering tickets, Improvado is built for that.

Full disclosure: we're Improvado, and this page is written from our perspective. We've tried to represent Airbyte and dbt's 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

Feature Comparison: Improvado vs Airbyte + dbt

Feature comparison: Improvado vs Airbyte + dbt (updated February 2026)

Where Improvado Differentiates from Airbyte + dbt

Marketing Teams Operate the Pipeline—No Engineering Tickets

Airbyte + dbt is powerful, but it's built for data engineers. Setting up a connector in Airbyte is straightforward if you're comfortable with Docker and API documentation. Writing a dbt model requires SQL fluency and an understanding of incremental materialization strategies. Most marketing teams don't have those skills in-house, which means every new data source, every schema change, and every transformation request becomes an engineering ticket.

Improvado inverts that dependency. Marketers configure connectors through a no-code interface, map fields using dropdowns, and access pre-built transformations for common marketing workflows (deduplication, UTM parsing, currency conversion, cross-channel attribution). When a new ad platform launches or a schema changes, Improvado's team handles the update—marketers never touch a dbt YAML file or debug a failed incremental run.

The result: marketing teams move faster. Adding a new connector in Improvado takes 10 minutes of configuration, not a two-week sprint. Schema drift doesn't break dashboards because Improvado preserves two years of historical mappings. And when something does break, marketers call their dedicated CSM, not Slack the data engineering channel and hope someone has bandwidth.

Improvado review

“Everything’s just set up and streamlined, and it all just works. The dashboards update automatically, and I don’t even have to touch them most of the time.”

Shayna Tyler

Analyst

Signal Theory's analytics lead runs reporting for 30+ clients without growing her team—Improvado automated the pipeline work that would otherwise require three full-time data engineers.

Marketing Data Governance That Actually Prevents Budget Errors

DBT supports data quality testing: you can write tests to check for null values, duplicate primary keys, or referential integrity. That's useful for validating that your ELT pipeline didn't corrupt the data. It doesn't tell you whether your Facebook campaign just exceeded its monthly budget by 40% because someone fat-fingered the daily spend cap, or whether your attribution model is crediting the same conversion to three different channels.

Improvado's Marketing Data Governance layer includes 250+ pre-built rules designed specifically for marketing workflows. Before a campaign launches, the platform validates that budget allocations across channels don't exceed the approved total. During the campaign, it monitors for anomalies: sudden CPC spikes, conversion rate drops, impression share losses. When something drifts outside expected ranges, alerts route to the responsible team with enough context to diagnose the issue—not just a Slack notification that says "dbt test failed."

You can build similar governance in dbt—write custom tests, set up Airflow to monitor metric thresholds, configure PagerDuty alerts. But that's another project for the data engineering backlog, and it won't include the marketing-specific logic (budget pacing curves, seasonality adjustments for retail, cross-channel attribution validation) that Improvado ships out of the box.

Improvado review

“If we don't trust the data, the agency won’t trust the reports and won't give them to the client. They’ll start pulling data manually to Excel and spend a lot of time comparing platform numbers to reports. With Improvado, we now trust the data. If anything is wrong, it’s how someone on the team is viewing it, not the data itself. It’s 99.9% accurate.”

Tyler Corcoran

Booyah Advertising processes millions in monthly ad spend across 40+ clients. Governance isn't optional—bad data means bad budget decisions, and bad budget decisions cost client relationships.

One Platform, One Vendor, One Compliance Audit

Airbyte + dbt + Airflow + Snowflake is a best-of-breed stack. Each tool does one thing well, and you can swap components as better options emerge. The downside: operational complexity. You're managing four separate vendor relationships (or more, if you count BI tools, reverse ETL, and observability platforms). Each has its own pricing model, support SLA, and compliance documentation.

When your security team asks for SOC 2 attestations, you're collecting reports from Airbyte Cloud, dbt Cloud, your orchestrator, and your warehouse provider. When a data pipeline breaks, you're debugging whether the issue is in Airbyte's connector, dbt's incremental logic, Airflow's scheduler, or Snowflake's warehouse scaling. When marketing requests a new capability—say, multi-touch attribution—you're scoping a project that touches all four layers of the stack.

Improvado consolidates that into a single platform with a single compliance audit, a single support contract, and a single point of accountability. SOC 2 Type II, HIPAA, and GDPR certifications cover the entire data lifecycle, not just one component. When something breaks, you call your CSM, and Improvado's team debugs whether the issue is in extraction, transformation, or loading—you don't triangulate across vendor support queues.

That simplicity has a cost: less flexibility. If you want to swap out Improvado's transformation layer for a custom dbt setup, you can't—Improvado's platform is integrated, not modular. But for teams that want to buy a solution rather than assemble one, the trade-off makes sense.

Improvado review

“Having a single point of contact simplifies everything. If we ever need assistance, we can reach out directly to Improvado instead of managing it internally. That's worth something.”

Jonathan Hemnes

EVP of Engineering

Pre-Built Marketing Data Models vs General-Purpose Transformation

DBT is extraordinary at transforming raw data into analytics-ready tables. You write a SQL SELECT statement, dbt materializes it as a table or view, and you can chain models together into a directed acyclic graph (DAG) that mirrors your business logic. The framework is general-purpose: it works equally well for product analytics, financial reporting, or marketing data.

Improvado ships with the Marketing Cloud Data Model (MCDM)—a pre-built set of transformations designed specifically for cross-channel marketing analytics. MCDM normalizes metrics and dimensions across 500+ data sources, so "cost per conversion" from Facebook, Google Ads, and LinkedIn all map to the same field with the same calculation logic. It handles UTM parameter parsing, currency conversion, timezone normalization, and deduplication—the transformations every marketing team needs but doesn't want to write from scratch.

With dbt, you'd build those transformations yourself. That's fine if you have the engineering capacity and want full control over the logic. It's a non-starter if your data team is underwater and marketing needs working dashboards in two weeks, not two quarters.

The MCDM also evolves. When Google Ads changes its API schema or Facebook deprecates a metric, Improvado updates the MCDM and preserves two years of historical mappings so existing dashboards don't break. With dbt, schema changes are your problem—you're maintaining the connector, the staging models, and the downstream dependencies.

AI Agent for Marketers vs SQL for Engineers

DBT doesn't include AI capabilities—it's a transformation framework, not an analytics layer. If you want to surface insights from your data, you write SQL queries, build dashboards in Looker or Tableau, or train ML models in Python. That workflow works brilliantly for technical teams. It doesn't help the CMO who wants to know "which campaigns are underperforming this month" without writing a 40-line SQL query.

Improvado's AI Agent lets marketers ask natural-language questions and receive structured answers. "Show me LinkedIn campaigns with CPA above $200 in Q1" returns a filtered table without SQL. The agent also surfaces anomalies automatically: if your Google Ads CPC spiked 35% last week, the platform flags it with context (competitive auction pressure, budget pacing, keyword bid changes) so you can diagnose the cause without digging through raw event logs.

This doesn't replace SQL—data teams still have full access to the underlying warehouse and can write custom queries when needed. But it shifts 80% of routine analytics tasks from the engineering backlog to self-serve, which means marketing teams get answers in minutes instead of waiting for the next sprint planning session.

Improvado review

“I use Improvado AI Agent to get basic analytics and quick solves. I just enter the question, and it gives me the answer I need.”

Beau Payne

When to Choose Airbyte + dbt

Airbyte + dbt is the right choice in several clear scenarios:

• You have a dedicated data engineering team that wants full control over transformation logic, and maintaining dbt models is part of their core workflow—not a distraction from other priorities.
• You're building a broader data platform beyond marketing—product analytics, financial reporting, customer success metrics—and you need a general-purpose ELT stack that handles all use cases, not just marketing.
• Cost optimization at scale is a priority, and you have the engineering capacity to tune incremental materializations, optimize warehouse queries, and manage infrastructure to keep cloud compute costs predictable.
• Open-source flexibility matters—you want the option to self-host, fork the codebase, or switch vendors without migration lock-in.
• Your marketing team is comfortable filing engineering tickets for new connectors, schema changes, and dashboard updates, and that dependency doesn't create workflow bottlenecks.

If your data team already runs dbt in production and Airbyte covers your connector needs, adding a third platform for marketing data alone may not make sense. Stick with what you've built.

What Improvado Customers Say

Improvado serves 500+ marketing teams across agencies, e-commerce brands, and enterprise SaaS companies. Here's what they report after switching from build-it-yourself or multi-tool stacks:

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

Improvado holds High Performer badges across ETL Tools, Marketing Analytics, and Data Integration categories on G2, with consistent ratings above 4.5/5. Customers cite quality of support (9.4/10), ease of setup, and the elimination of manual data wrangling as top differentiators.

Pricing Comparison: Improvado vs Airbyte + dbt

Airbyte and dbt both offer flexible pricing that scales with usage. Improvado uses outcome-based pricing tied to data volume and connector count. Here's how the models compare:

Airbyte + dbt Pricing

Airbyte Cloud: Credits-based model at $2.50 per credit. Credits consumed based on data volume synced and connector type. A typical mid-market setup syncing 10 marketing sources to Snowflake costs approximately $500–$1,500/month depending on data volume. Self-hosted Airbyte is free (open-source), but requires infrastructure (minimum 2 vCPUs, 8GB RAM) and DevOps overhead.

DBT: dbt Core is free and open-source. dbt Cloud starts at $100/developer/month for the Team tier, with Enterprise pricing available on request. For a team of 3–5 analysts, expect $300–$500/month minimum for dbt Cloud, plus engineering time to maintain models.

Total cost of ownership: Airbyte + dbt together might run $800–$2,000/month in direct tool costs for a mid-market marketing team. Add engineering time (maintaining connectors, writing dbt models, debugging schema drift) and infrastructure costs (warehouse compute, orchestration), and the fully loaded cost often exceeds $5,000–$10,000/month when you account for headcount.

Improvado Pricing

Improvado pricing is custom and driven by data volume (rows synced per month), number of connectors, and deployment model (managed warehouse vs BYOW). Most mid-market customers fall in the $30,000–$60,000/year range, with enterprise deployments scaling higher based on data complexity and compliance requirements.

That fee includes: dedicated CSM, professional services for onboarding and custom connector builds, SOC 2/HIPAA compliance documentation, and ongoing support with a 99.99% SLA. There are no per-seat fees—unlimited users can access the platform.

The cost comparison isn't apples-to-apples. Airbyte + dbt optimizes for engineering flexibility and incremental cost control. Improvado optimizes for marketing team velocity and elimination of engineering dependency. Choose based on what your organization values more: control and cost transparency, or speed and simplicity.

For detailed Improvado pricing tailored to your data sources and volume, request a custom quote.

Frequently Asked Questions: Improvado vs Airbyte + dbt

What is the main difference between Improvado and Airbyte + dbt?

Airbyte + dbt is a modular ELT stack where Airbyte handles data extraction and loading, and dbt handles transformation within your warehouse. Improvado is an end-to-end marketing analytics platform that bundles extraction, transformation, governance, and insights into one managed solution. Airbyte + dbt requires data engineering to operate; Improvado is designed for marketing teams to use without engineering support.

Does Improvado integrate with dbt?

Improvado delivers data to your warehouse (Snowflake, BigQuery, Redshift) in a clean, analytics-ready format, and data teams can run dbt models on top of that data if desired. However, most Improvado customers don't need dbt—the platform's Marketing Cloud Data Model handles the transformations that dbt would otherwise perform. If your data team already runs dbt for non-marketing use cases, Improvado's output tables can feed into your existing dbt DAGs.

Can Airbyte + dbt replace Improvado for marketing analytics?

Technically yes, but it requires significant engineering effort. You'd need to configure Airbyte connectors for each marketing platform, write dbt models to normalize metrics and dimensions across sources, build custom governance rules to validate budget pacing and detect anomalies, and maintain all of that as APIs and schemas change. Improvado automates those workflows out of the box. If you have the engineering capacity and want full control, Airbyte + dbt works. If you want to eliminate engineering dependency and move faster, Improvado is purpose-built for that.

How long does it take to migrate from Airbyte + dbt to Improvado?

Typical onboarding takes 2–4 weeks depending on the number of data sources and complexity of transformation requirements. Improvado's professional services team handles connector configuration, schema mapping, and historical data backfill. Most customers run Improvado in parallel with their existing stack for one billing cycle to validate data consistency before fully cutting over.

Which is more cost-effective: Airbyte + dbt or Improvado?

Direct tool costs for Airbyte + dbt are lower—$800–$2,000/month for mid-market setups. Improvado typically starts at $2,500–$5,000/month ($30K–$60K annually). However, total cost of ownership includes engineering time: maintaining connectors, writing dbt models, debugging schema drift, and building governance rules. When you factor in fully loaded headcount costs, Improvado often breaks even or costs less for teams without dedicated data engineering resources.

Does Improvado support the same connectors as Airbyte?

Airbyte has 600+ connectors covering general data sources (databases, SaaS apps, APIs). Improvado has 500+ connectors focused specifically on marketing platforms—ad networks, social media, analytics tools, e-commerce platforms, CRMs. If you need connectors for non-marketing data (Salesforce, Stripe, internal databases), Airbyte has broader coverage. If you need deep coverage of marketing-specific sources (DV360, The Trade Desk, TikTok Ads, Shopify), Improvado's connectors are more reliable and maintained by a dedicated team, not the community.

Can I use Airbyte for extraction and Improvado for transformation?

Not recommended. Improvado's value comes from managing the full pipeline—connectors, transformations, and governance—as an integrated system. If you only use Improvado for transformation, you lose the connector maintenance SLA, schema drift protection, and anomaly detection that make the platform valuable. If Airbyte already covers your extraction needs and dbt handles transformation well, adding Improvado on top creates redundancy without clear benefit.

When does Airbyte + dbt make more sense than Improvado?

Airbyte + dbt wins when you have a dedicated data engineering team that wants full control over transformation logic, you're building a data platform that serves use cases beyond marketing (product analytics, financial reporting), and cost optimization through custom infrastructure tuning is a priority. Improvado wins when marketing teams need self-serve access to governed data without filing engineering tickets, you want compliance handled by a single vendor, and speed-to-insight matters more than infrastructure flexibility.

Final Thoughts: Build vs Buy for Marketing Data Infrastructure

Airbyte + dbt represents the build path: assemble best-of-breed tools, own the infrastructure, optimize for flexibility. Improvado represents the buy path: outsource the undifferentiated heavy lifting, focus internal resources on insight generation instead of pipeline maintenance.

Neither approach is universally right. If your data team is already deep in the modern data stack and dbt is core to how you operate, Airbyte + dbt extends naturally into marketing use cases. If your marketing team outnumbers your data engineers 10:1 and every schema change creates a bottleneck, Improvado eliminates that dependency.

The question isn't which platform has more features. The question is: where does your organization want to spend engineering cycles? If the answer is "not on maintaining marketing data connectors," Improvado is purpose-built for that.