Improvado vs Databricks: Choosing the Right Data Platform for Marketing Teams (2026)

Databricks is an enterprise data platform built for general-purpose analytics, ML, and distributed processing across all data types. Improvado is a marketing-focused ETL platform that automates integration from 500+ marketing sources with pre-built transformations and governance. This comparison helps you decide which architecture fits your team's actual workflow — whether you need a comprehensive data engineering platform or a specialized marketing data pipeline that feeds into your existing warehouse.

Improvado vs Databricks: Marketing Data Pipeline vs General Data Platform

Databricks excels at building custom data infrastructure for teams with engineering resources. Improvado eliminates the need to build that infrastructure for marketing data specifically — it's the difference between hiring a team to maintain 500 API connectors or getting them pre-built with a 2–4 week SLA for custom additions. Same data problems, entirely different solutions.

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

Feature comparison: Improvado vs Databricks (updated February 2026)

What Makes These Platforms Different

Your Marketing Team Operates the Pipeline — No Engineering Tickets

Databricks is built for data engineers who write Spark jobs and manage distributed compute clusters. Improvado is built for marketing operations teams who need data flowing without writing code. That's not a quality judgment — it's an architectural difference that determines who owns the pipeline.

With Databricks, adding a new marketing platform means your data team writes a connector, maps fields, handles API pagination, manages rate limits, and maintains the integration when the vendor changes their schema. With Improvado, that entire process is handled by the platform. Your marketing team clicks a button, authenticates, and data flows within hours. When Facebook changes their API (which happens quarterly), Improvado updates the connector. You don't touch anything.

This difference compounds. If your marketing team runs 30 paid channels, 5 analytics platforms, 3 CRMs, and pulls offline event data — that's 40+ integrations your engineering team either maintains or you pay Improvado to maintain. The real cost isn't the initial build; it's the ongoing maintenance burden that never appears on a project roadmap but consumes 20–30% of a data engineer's time.

Improvado review

"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

Marketing Data Governance: 250+ Rules That Catch Budget Errors Before Launch

Databricks offers Unity Catalog — a powerful governance layer for data lineage, access control, and compliance across your entire data estate. But it's a general framework. You still write the validation rules, define the quality checks, and build the alerting logic specific to marketing workflows.

Improvado ships with 250+ pre-built governance rules designed for marketing data specifically. Budget pacing alerts that catch overspend before it happens. Campaign naming validation that enforces your taxonomy. Cross-channel deduplication that prevents double-counting conversions. Attribution logic that handles multi-touch without custom SQL. These aren't features you configure — they're operational guardrails that work out of the box.

The impact shows up in two places: speed and trust. Teams using Improvado launch campaigns faster because validation happens automatically, not through manual QA. And finance teams trust the data because governance catches the errors that typically surface three weeks into a quarter when someone asks why the spend doesn't reconcile.

No-Code for Marketers + Full SQL for Engineers — Dual-Persona Design

Databricks is optimized for technical users. It assumes fluency in SQL, Python, or Scala. That's appropriate for a platform designed for data engineers and ML practitioners. Improvado takes a different approach: a no-code interface that marketers can operate independently, with full SQL access underneath for edge cases the visual builder can't handle.

This dual-persona design means your marketing ops team can build dashboards, create custom metrics, and adjust attribution windows without engineering support. When they hit a limitation — a complex join, a custom aggregation, a calculation the UI doesn't support — they escalate to your data team, who can write SQL directly against the same data models. Both personas work in the same platform without context-switching.

The alternative is the pattern most teams fall into: marketers request changes, engineering writes the transformation, marketing waits for the next sprint, the original request is now outdated. Improvado collapses that loop. Simple changes happen immediately. Complex changes still require engineering, but the volume of tickets drops by 60–70% because marketers self-serve the majority of requests.

Improvado review

“Teams don’t want to fight dashboards—they want it simple. Now, Improvado makes data accessible and pliable for use across a wide range of needs.”

Beau Payne

Dedicated CSM + Professional Services vs Ticket-Only Support

Databricks operates on a self-service model with enterprise support available through paid tiers. You submit tickets, consult documentation, and engage with account teams for strategic projects. That's standard for infrastructure platforms — it assumes you have internal expertise to operate the system.

Improvado includes a dedicated Customer Success Manager and professional services as part of the platform cost, not an add-on. Your CSM becomes an extension of your team — they know your data sources, your governance requirements, your reporting deadlines. When a connector breaks, you're not filing a ticket and waiting for a response; you're texting your CSM and getting real-time updates on the fix timeline.

This difference matters most during three scenarios: initial onboarding (where hands-on guidance cuts implementation time from months to weeks), connector customization (where Improvado's 2–4 week SLA for custom builds includes scoping, development, and testing), and production incidents (where a dedicated contact eliminates the escalation loop). For teams without a large data engineering org, this managed-service approach is the difference between adopting the platform or abandoning it after 90 days.

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

Total Cost of Ownership: Predictable vs Usage-Based Pricing

Databricks charges based on compute consumption — DBUs (Databricks Units) that accrue as you run queries, process data, and maintain clusters. This usage-based model scales with workload but introduces cost unpredictability. A single inefficient query can spike your monthly bill. Optimizing costs requires ongoing tuning of cluster configurations, job schedules, and query patterns.

Improvado uses outcome-based pricing: a fixed annual cost determined by the number of data sources, data volume, and features you need. That cost doesn't fluctuate based on how many queries your team runs or how often dashboards refresh. You know the expense upfront, and finance doesn't receive surprise bills when your marketing team scales campaigns.

The hidden costs of Databricks show up in three areas: engineering time to maintain marketing connectors (which compounds as your stack grows), infrastructure tuning to control compute costs (a specialized skill most marketing teams don't have), and the opportunity cost of building features Improvado includes pre-built. When you calculate total cost of ownership over 36 months, the delta isn't just the platform subscription — it's the fully-loaded cost of the team maintaining it.

When to Choose Databricks

Databricks is the right choice in these scenarios:

• You're building a general-purpose data platform — Marketing data is one workload among many (product analytics, financial reporting, ML pipelines), and you need a unified infrastructure layer that handles all of them under one governance model.
• You have a dedicated data engineering team — Your team writes Spark jobs, optimizes distributed compute, and maintains custom integrations as part of their core responsibilities. Building and maintaining 500 marketing connectors isn't a burden; it's part of the job.
• Real-time streaming is a core requirement — Your use cases depend on sub-second data latency and event-driven architectures that require distributed stream processing beyond what batch ETL pipelines provide.
• You need ML model training and serving in the same platform — Your data science team is building predictive models, running experiments in MLflow, and deploying models to production endpoints — use cases Databricks is explicitly designed for.
• You already operate Databricks for other workloads — Adding marketing data to your existing Databricks deployment makes architectural sense if your team is already fluent in the platform and the incremental cost is lower than adopting a second tool.

What Customers Say About Improvado

Teams switch to Improvado when maintaining marketing integrations in-house becomes a bottleneck. Here's what that transition looks like in practice:

Case study

At AdCellerant, when the team calculated that each integration required over six months of development time and approximately $120,000 in costs, they realized it was a severe constraint on growth. Read full case study →

“It's very expensive for us to spend engineering time on these integrations. It’s not just the cost of paying engineers, but also the opportunity cost. Every hour spent building connectors is an hour we don’t spend deepening our data analysis or working on truly meaningful things in the market.”

Jonathan Hemnes

EVP of Engineering, AdCellerant

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 aren't isolated cases. Across Improvado's customer base — agencies managing 50+ client accounts, enterprise brands running global campaigns, and mid-market companies scaling their marketing operations — the pattern is consistent: eliminate engineering dependency for marketing data, reduce reporting time by 60–90%, and free technical teams to focus on higher-value work.

Pricing Comparison

Improvado Pricing

Improvado uses outcome-based pricing determined by three factors: number of data sources, data volume processed monthly, and which features you activate (governance, AI Agent, custom connectors). The cost is fixed annually, with no surprise charges based on query volume or compute consumption. Professional services and dedicated CSM support are included in all enterprise plans, not billed separately.

Most mid-market deployments (15–30 data sources, standard transformation needs, one data warehouse destination) fall in the $30K–$60K annual range. Enterprise deployments with custom connectors, advanced governance, and multi-region support scale from there. Pricing details are available on the Improvado pricing page.

Databricks Pricing

Databricks charges based on DBUs (Databricks Units) consumed during compute operations — running queries, processing data in pipelines, training ML models, and maintaining clusters. Pricing varies by cloud provider (AWS, Azure, GCP) and workload type (SQL, jobs, ML). Serverless compute and autoscaling features help control costs, but usage-based billing means monthly expenses fluctuate with workload intensity.

Public pricing is not disclosed; Databricks provides custom quotes based on anticipated usage. For marketing data workloads specifically, costs depend on pipeline frequency, data volume, transformation complexity, and how many analysts are querying dashboards. Teams report that optimizing Databricks costs requires dedicated engineering attention to avoid runaway compute expenses.

Total Cost of Ownership Considerations

When comparing Improvado and Databricks for marketing data specifically, factor in these hidden costs:

• Connector maintenance — Databricks requires engineering time to build and maintain API integrations. At 500+ connectors, this becomes a full-time role (or multiple roles). Improvado eliminates this entirely.
• Transformation development — Databricks transformations are written in SQL/Python/Spark from scratch. Improvado ships with pre-built marketing data models (MCDM) and no-code transformation UI, reducing development time by 70–80%.
• Governance implementation — Unity Catalog provides the framework; you still build the validation rules. Improvado's 250+ pre-built governance rules are included, saving months of development.
• Support model — Databricks enterprise support is an add-on cost; Improvado includes dedicated CSM and professional services in base pricing.
• Compute optimization — Databricks requires ongoing tuning to control costs. Improvado's fixed pricing removes this variable.

For teams evaluating purely on platform cost, Databricks may appear less expensive. When you calculate the fully-loaded cost — engineering salaries, opportunity cost, and time to value — Improvado's managed-service model often delivers lower TCO over 36 months for marketing-specific workloads.

Frequently Asked Questions

What is the main difference between Improvado and Databricks?

Databricks is a general-purpose data intelligence platform designed for all data types, requiring engineering expertise to build and maintain pipelines. Improvado is a marketing-focused ETL platform with 500+ pre-built connectors, automated transformations, and governance rules specific to marketing workflows — designed for marketing operations teams to run independently. Databricks gives you infrastructure; Improvado gives you a managed marketing data pipeline.

Can Improvado integrate with Databricks?

Yes. Improvado can deliver transformed marketing data directly into Databricks as a destination warehouse. This architecture is common: Improvado handles marketing-specific extraction and transformation, then pushes clean data into Databricks where it joins with other enterprise datasets. You avoid maintaining 500 marketing API connectors while still using Databricks for broader analytics and ML workloads.

Does Improvado require engineering resources to operate?

No. Improvado is designed for marketing operations teams to run independently using the no-code interface. Engineering resources are optional — teams with SQL fluency can write custom transformations, but the majority of workflows (connector setup, metric definitions, dashboard creation) happen through the visual UI. Most customers operate Improvado without dedicated data engineering support.

How long does it take to migrate from Databricks to Improvado?

For marketing data specifically, 4–6 weeks from kickoff to production. Improvado's professional services team maps your existing Databricks transformations to equivalent recipes in the platform, migrates historical data, and validates output matches your current dashboards. The migration runs in parallel — your Databricks pipelines continue running until Improvado is fully validated, eliminating reporting gaps.

What happens when a marketing platform changes their API?

With Databricks, your engineering team receives an API deprecation notice and has to update the connector code before the deadline — often with limited notice. With Improvado, the platform team monitors API changes across all 500+ connectors and deploys updates automatically. Your pipelines keep running without intervention. Improvado also preserves 2 years of historical data during connector migrations, so schema changes don't break your year-over-year reporting.

Can Improvado handle the same data volume as Databricks?

Improvado processes billions of rows per month for enterprise customers running global campaigns across 100+ marketing channels. The platform scales horizontally to handle increasing volume without performance degradation. However, Databricks is built for petabyte-scale distributed processing across all data types — if your marketing data workload specifically requires that level of compute (rare), Databricks has a higher ceiling. For 99% of marketing use cases, Improvado's infrastructure handles volume without issue.

Does Improvado support real-time data streaming?

Improvado supports near-real-time data refresh (15-minute to hourly intervals depending on the source), which meets most marketing use cases. Databricks excels at true real-time streaming with sub-second latency using Spark Structured Streaming. If your marketing workflows require instant event processing (e.g., triggering campaign actions within seconds of a user behavior), Databricks is better suited. If you need daily/hourly refreshed dashboards and attribution reports, Improvado's batch processing is sufficient.

When does Databricks make more sense than Improvado?

Choose Databricks when marketing data is a small piece of a broader data platform strategy, your team has dedicated data engineering resources who maintain custom integrations as part of their core work, you need ML model training and real-time streaming beyond marketing analytics, or you already operate Databricks for other workloads and adding marketing data to the existing deployment makes architectural sense. Databricks is infrastructure; Improvado is a managed service. The right choice depends on whether you want to build or buy the marketing data pipeline.

The Bottom Line

Databricks and Improvado solve different problems. Databricks is the right platform when you're building a general-purpose data infrastructure and have engineering teams who maintain custom pipelines as part of their roadmap. Improvado is right when you need marketing data flowing reliably without engineering dependency — when the choice is between hiring a team to maintain 500 API connectors or paying Improvado to handle it as a managed service.

The platforms aren't mutually exclusive. Many teams use both: Improvado extracts and transforms marketing data, pushes it into Databricks, where it joins with product analytics, financial data, and ML models. That architecture lets each platform do what it does best — Improvado handles the marketing-specific pipeline complexity, Databricks provides the enterprise data warehouse and advanced analytics layer.

Your decision comes down to this: does your data engineering team want to own marketing data integration, or would they rather focus on broader platform initiatives while marketing data runs on autopilot? If the answer is the latter, Improvado eliminates the maintenance burden entirely. If your team thrives on building custom infrastructure and marketing is just one workload among many, Databricks gives you the control to build exactly what you need.