You don’t wake up caring about badges.
You care about data that’s actually extractable, pipelines that don’t fall over, and teams that aren’t stuck copy-pasting CSVs.

Still, it’s good when the market validates that.

In the Europe Grid® Report for Data Extraction | Winter 2026, Keboola is a Leader on the Mid-Market Europe Regional Grid® for Data Extraction.

That makes Keboola the highest-rated mid-market data extraction tool in Europe by customer satisfaction, with the most reviews on the grid.

See Keboola on the G2 Grid®

This Mid-Market Europe Regional Grid® shows how tools in the Data Extraction category compare, based on:

• Customer Satisfaction – ratings and feedback from users in Europe
• Regional Market Presence – things like market share, company size, and overall visibility
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Keboola appears in the Leader quadrant, based entirely on the voice of real mid-market customers in the region.

What does “Data Extraction” actually cover?

In G2's language, data extraction tools help you:

• Pull structured, semi-structured, and unstructured data from many different sources
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• Collect data from databases, SaaS apps, APIs, files, and web sources
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• Export that data in usable formats so it can be cleaned, transformed, and analyzed

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In other words, plug in your data from everywhere it lives today (mess of databases, SaaS tools, files, and streams), and get it into a shape your business and AI use cases can actually work with.

With Keboola, that’s the first step in a bigger story. From extraction to integration, transformation, orchestration, and data apps – all in one platform.

If you want the full story, you can deep-dive into the Keboola AI & Data Platform overview.

Why mid-market Europe specifically?

This Grid® view is regional and segmented on purpose:

• Only reviews from customers located in Europe are counted.
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• The Mid-Market segment means reviewers work at companies with 51 - 1,000 employees.

So if you’re a mid-sized European company, this Grid® reflects peers who look a lot like you - same region, similar scale, similar problems.

How teams use Keboola for data extraction

Data extraction is just the starting line. Here’s how teams typically use Keboola in this space:

• Connect everything in one place
  Use hundreds of ready-to-use integrations to pull data from SaaS tools, databases, files, and more – without writing and maintaining custom scripts.
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• Turn raw feeds into reliable pipelines
  Design and orchestrate flows that extract, standardize, and route data where it needs to go with Keboola Flows and Transformations – including AI-assisted SQL/Python.
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• Prepare data for analytics and AI
  Clean, join, and model data directly in Keboola so it’s ready for BI dashboards, reverse ETL, machine learning, or agentic workflows. Check Keboola Agents.
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• Put governed experiences in front of business users
  Build interactive data apps on top of extracted data – internal tools, workflows, and AI experiences that run inside the same governed platform.

So yes, the report is “about Data Extraction tools” – but the grid placement reflects teams who are using Keboola as the backbone for their broader data and AI stack, not just a scraper.

Where to go from here

If you’re evaluating platforms or refreshing your data stack, you’ve got two quick ways to go deeper:

• 👉 Read what customers say on Keboola’s G2 profile
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• 👉 Explore the Keboola AI & Data Platform