Great Expectations vs Improvado: Choosing the Right Data Quality Solution (2026)

Great Expectations is an open-source Python library for data validation and testing. Improvado is an end-to-end marketing analytics platform that extracts, transforms, governs, and delivers insights from 500+ sources. Both address data quality, but they operate at different layers of the stack and serve fundamentally different use cases. This comparison breaks down where each tool fits, what each does well, and which is right for your team.

Great Expectations vs Improvado: What You're Actually Comparing

Great Expectations (GX) is a developer-first validation framework. It checks data against expectations — rules you define in Python or YAML — and generates documentation showing what passed and what failed. It integrates into ETL pipelines, runs checks during ingestion or transformation, and alerts you when something breaks. It's open-source, highly customizable, and widely adopted by data engineering teams.

Improvado is a marketing data platform that handles the entire pipeline: extracting data from ad platforms, normalizing it into a unified schema, applying governance rules, and delivering clean data to your BI tool or warehouse. It's purpose-built for marketing teams who need cross-channel reporting without engineering bottlenecks.

The core difference: GX validates data you're already moving through pipelines you've already built. Improvado moves the data, validates it, transforms it, and delivers it — all in one managed service.

Feature Comparison: Improvado vs Great Expectations

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

Where Improvado and Great Expectations Diverge

Your Marketing Team Owns the Pipeline — No Engineering Tickets Required

Great Expectations assumes you have a pipeline. It validates data flowing through that pipeline — checking schemas, distributions, null rates, and custom rules — but it doesn't extract data, transform it, or deliver it to dashboards. You still need ETL tooling (Fivetran, Airbyte, custom scripts) to move data, DBT or similar to transform it, and orchestration (Airflow, Prefect) to tie it together. GX is a validation layer you add to infrastructure you've already built.

Improvado is the infrastructure. It extracts from 500+ marketing sources, normalizes the data into a unified schema, applies governance rules during transformation, and pushes clean data to your warehouse or BI tool. Marketing teams operate it directly — no engineering backlog, no JIRA tickets, no waiting three sprints to add a new connector.

This matters when campaign timelines don't wait for engineering capacity. A new channel launches, you need data flowing into reports by Monday, and your data team is three weeks out. With GX, you're blocked until someone builds the connector and writes the validation suite. With Improvado, you toggle on a pre-built connector and governance rules apply automatically.

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 Is Built In, Not Bolted On

Great Expectations lets you write expectations for any data quality rule you can code. You define what "good data" looks like — schemas must match, values must fall within ranges, distributions shouldn't shift unexpectedly — and GX flags violations. It's flexible, expressive, and works on any dataset. But it's also general-purpose. There are no pre-built rules for marketing-specific problems like campaign naming conventions, budget pacing, or cross-platform metric reconciliation.

Improvado ships with 250+ governance rules designed for marketing data. These aren't abstract schema checks — they're things like flagging when a campaign name doesn't match your taxonomy, alerting when CPC spikes 30% week-over-week, or validating that impression counts sync across DV360 and your attribution tool. You write rules in plain language, not Python, and they run automatically during transformation.

When data quality issues surface, GX tells you the validation failed. Improvado tells you the validation failed, which campaign caused it, what the expected value was, and gives you a drill-down link to investigate. The difference is context. Data engineers can debug schema mismatches from a stack trace. Marketers need to know which campaign to pause.

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

Transformation Happens Inside the Platform, Not in a Separate Tool

Great Expectations doesn't transform data. It checks data quality at various points in your pipeline — during ingestion, post-transformation, before consumption — but the transformation itself happens elsewhere. Most teams pair GX with DBT, writing SQL models to reshape raw data and expectations to validate the output. That's a powerful combination for data engineering teams, but it means marketers depend on someone else to write and maintain those transformations.

Improvado includes transformation. The platform normalizes data from 500+ sources into a unified schema (the Marketing Cloud Data Model), automatically mapping metrics and dimensions so "cost per conversion" from Google Ads and "CPA" from Facebook mean the same thing in your reports. You can use the no-code interface to build transformations — deduplication, calculated fields, cross-channel attribution — or write SQL if you need custom logic.

This eliminates the handoff. With GX + DBT, a marketer requests a new metric, a data engineer writes a DBT model, deploys it, writes expectations to validate it, and ships it a week later. With Improvado, the marketer builds the calculated field directly in the UI, governance rules apply automatically, and the metric appears in dashboards within minutes.

Dedicated Support vs. Community-Driven

Great Expectations is open-source. That means the core library is free, highly customizable, and backed by a strong community. When you hit an issue, you check the documentation, search GitHub discussions, or file an issue. GX Cloud (the SaaS offering) adds a support tier, but pricing and SLA details aren't publicly listed. For teams comfortable with self-service tooling and in-house Python expertise, this model works well.

Improvado is a managed service. Every customer gets a dedicated Customer Success Manager, a 99.99% uptime SLA, and professional services included in the subscription — not as an add-on. When you need a custom connector, it's built in 2–4 weeks under contract. When governance rules need tuning, your CSM works with you to configure them. When a campaign launches and data needs to flow by Monday, someone is accountable for making that happen.

This difference shows up most clearly during incidents. With GX, if a data source changes its schema and validations start failing, you debug it yourself or wait for a community response. With Improvado, the platform detects the schema change, the CSM is alerted, and the connector is updated before your dashboards break. You're buying insurance against the operational overhead of maintaining a data pipeline.

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

When to Choose Great Expectations

Great Expectations is the right choice in these scenarios:

• You have a data engineering team that owns your pipeline infrastructure and prefers building with best-of-breed open-source tools rather than adopting a managed platform.
• Your data sources aren't limited to marketing platforms — you're validating product data, operational data, and marketing data in the same framework.
• You need highly customizable validation logic that goes beyond pre-built rules, and your team is comfortable writing Python or SQL to define expectations.
• You're already using DBT for transformation and Airflow for orchestration, and you want a validation layer that integrates cleanly into that stack.
• Your budget prioritizes open-source tooling, and you have the internal capacity to operationalize and maintain validation infrastructure.

If any of these apply, GX is a strong fit. It's a mature, well-documented, widely adopted tool that does one thing exceptionally well: validating data quality in code-driven pipelines.

What Improvado Customers Say

Improvado serves marketing teams at agencies, brands, and enterprises who need the full pipeline — not just validation — handled in one platform. Here's what that looks like in practice:

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

At CV, More importantly, the team no longer faces the concern of escalating fees as they scale. Read full case study →

“Without Improvado, scaling to even half our current level would have meant spending all my time updating dashboards and realigning data with complex data workarounds. Now, I run a single query and save an hour's work.”

Beau Payne

CV

These outcomes — time saved, insights delivered, teams scaling without headcount — happen because Improvado eliminates the handoff between extraction, transformation, validation, and delivery. Marketing teams operate the pipeline directly. Data engineers get pulled in only when SQL-level customization is required. Everyone works faster.

Pricing Comparison

Great Expectations offers two pricing models. GX Core is free and open-source — you download the library, integrate it into your pipeline, and run it on your infrastructure. There's no licensing cost, but you bear the operational cost of maintaining the integration, writing expectations, and debugging issues. GX Cloud is a SaaS offering with a no-code UI, centralized collaboration, and enhanced support, but pricing isn't publicly listed. You contact their team for a quote based on your data volume and use case.

Improvado pricing follows a tiered subscription model (Growth, Advanced, Enterprise) based on the number of data sources, data volume, and feature access. Growth plans start in the mid-five figures annually and include dedicated support, professional services, and unlimited data destinations. Advanced and Enterprise tiers add features like Marketing Data Governance, AI-powered insights, and custom connector SLAs. Pricing is transparent during the sales process — you get a quote after a scoping call, not after weeks of evaluation.

The hidden cost in the GX model is engineering time. If your team spends 20 hours a month maintaining connectors, writing new expectations, debugging validation failures, and updating schemas, that's $10K–$15K/month in fully loaded engineering cost. Improvado's subscription includes all of that as a managed service. The sticker price is higher, but the total cost of ownership often favors the managed platform — especially as you scale to more sources and more complex governance requirements.

Full Improvado pricing details and tier breakdowns are available on the pricing page.

Frequently Asked Questions

What is the main difference between Improvado and Great Expectations?

Great Expectations is a validation library that checks data quality in pipelines you've already built. Improvado is an end-to-end platform that extracts, transforms, governs, and delivers marketing data — validation is one layer in a managed service. GX is a tool for data engineers; Improvado is a platform for marketing teams.

Can Great Expectations replace Improvado?

No. Great Expectations doesn't extract data from marketing platforms, doesn't transform it, and doesn't deliver it to dashboards. You'd need to combine GX with an ETL tool (Fivetran, Airbyte), a transformation tool (DBT), and orchestration (Airflow) to replicate what Improvado does in one platform. GX is a validation layer, not a pipeline.

Does Improvado support custom validation rules?

Yes. Improvado includes 250+ pre-built marketing governance rules (naming conventions, budget checks, anomaly detection) and lets you define custom rules in plain language or SQL. Rules run automatically during transformation and trigger alerts when violations occur. You don't need to write Python or deploy code to add a new rule.

How long does it take to migrate from a GX-based pipeline to Improvado?

Implementation timelines depend on the number of data sources and the complexity of your transformation logic. Most customers are live within 4–6 weeks. Improvado's professional services team handles connector setup, schema mapping, and governance rule configuration. You're not rebuilding the pipeline yourself — the CSM and engineering team do the heavy lifting.

When does Great Expectations make more sense than Improvado?

Great Expectations is the better choice if you have a data engineering team that owns pipeline infrastructure, you're validating data beyond marketing platforms (product, operational, financial data), and you prefer open-source tooling you can customize. It's also the right call if your budget can't support a managed platform and you have the internal capacity to maintain validation infrastructure.

Does Improvado integrate with DBT or other transformation tools?

Improvado handles transformation natively — you don't need DBT unless you want to use it for downstream modeling after Improvado delivers data to your warehouse. Some customers use Improvado for extraction and governance, then run DBT models in Snowflake for advanced analytics. The platform is compatible with any BI or transformation tool downstream.

What kind of support does Improvado provide compared to GX Cloud?

Improvado includes a dedicated Customer Success Manager, 99.99% uptime SLA, and professional services in every subscription tier. Custom connectors are built under a 2–4 week SLA. GX Cloud offers enhanced support over the open-source version, but SLA details and support tier breakdowns aren't publicly documented — you negotiate those during the sales process.

Can I use Great Expectations to validate data in Improvado?

Technically, yes — if Improvado delivers data to your warehouse, you could run GX expectations on that data as a secondary validation layer. But Improvado's governance engine already validates data during extraction and transformation, so adding GX on top is redundant for most use cases. Teams do this only when they have strict regulatory requirements that mandate multiple validation checkpoints.