Keboola named a Leader on the G2 Mid-Market Europe Grid(R) for Data Extraction. Keboola has been recognized as a Leader on the G2 Mid-Market Europe Grid(R) for Data Extraction in the Winter 2026 report, based on reviews from real customers across Europe. 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. In the Europe Grid(R) Report for Data Extraction | Winter 2026, Keboola is a Leader on the Mid-Market Europe Regional Grid(R) for Data Extraction. * 70 reviews from European users * 97 / 100 Satisfaction score 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(R). This Mid-Market Europe Regional Grid(R) 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 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? * Pull structured, semi-structured, and unstructured data from many different sources * Collect data from databases, SaaS apps, APIs, files, and web sources * Export that data in usable formats so it can be cleaned, transformed, and analyzed 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. Why mid-market Europe specifically? This Grid(R) view is regional and segmented on purpose: * Only reviews from customers located in Europe are counted. * 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(R) 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. * 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. * 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. * 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 * | Explore the Keboola AI & Data Platform

Keboola, in cooperation with Capgemini and Snowflake, is releasing "AI Data Management For Dummies," a book written to be an easy introduction to the world of discriminative and generative AI.