List of Keboola Platform Customers
Prague, 170 00,
Czech Republic
Since 2010, our global team of researchers has been studying Keboola Platform customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Keboola Platform for Analytics and BI from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Keboola Platform for Analytics and BI include: Czech Savings Bank, a Czech Republic based Banking and Financial Services organisation with 9738 employees and revenues of $984.0 million, Mall.cz, a Czech Republic based Retail organisation with 2000 employees and revenues of $230.0 million, Rohlik, a Czech Republic based Retail organisation with 1000 employees and revenues of $75.0 million and many others.
Contact us if you need a completed and verified list of companies using Keboola Platform, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Czech Savings Bank | Banking and Financial Services | 9738 | $984M | Czech Republic | Keboola | Keboola Platform | Analytics and BI | 2019 | n/a |
In 2019, Czech Savings Bank implemented Keboola Platform to build a self service data management environment across around 70 teams. Czech Savings Bank, known locally as Česká spořitelna, configured Keboola Platform to support unified product datamarts, CRM automation, branch analytics, and People and Culture reporting within an Analytics and BI context.
The Keboola Platform deployment focused on pipeline orchestration, data ingestion, transformation, and provisioning workflows typical for Analytics and BI use cases. The environment was used to assemble and operationalize datamarts and CRM automation workflows, with BI, ETL and CRM connector patterns inferred from the described architecture to standardize data flow and enable self service analytics.
Integrations documented in the case study include Oracle DWH as a source, Salesforce CRM for customer master and activity data, internal HR systems for People and Culture reporting, and outbound feeds to Salesforce Marketing Cloud for marketing activation. These integrations indicate a hub style data architecture where Keboola Platform consolidated cross functional data and fed downstream analytics and marketing systems.
Governance and rollout emphasized improving data governance and accelerating time to insight, while enabling around 70 teams to access managed data products for product, CRM, branch operations, marketing, and HR reporting. The implementation narrative centers on Keboola Platform as the central Analytics and BI layer that standardized data ingestion, transformation, and distribution for enterprise reporting workflows.
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Mall.cz | Retail | 2000 | $230M | Czech Republic | Keboola | Keboola Platform | Analytics and BI | 2020 | n/a |
In 2020 Mall.cz implemented Keboola Platform to centralize cloud data workflows and support its Analytics and BI needs. The Keboola Platform deployment emphasized cloud ETL, orchestration, and self-service BI capabilities to power pricing and product analytics across the organization.
The implementation used Keboola Platform ETL pipelines and orchestration layers together with self-service BI to enable dynamic pricing workflows across approximately 300,000 products. Configuration centered on reusable data pipelines and automated orchestration schedules, enabling over 100 engineers to own and iterate on data pipelines and feature releases.
Deployment followed a cloud-first architecture with Keboola Platform as the central data orchestration layer, instrumenting data ingestion, transformation, and downstream BI consumption. Operational scope covered Mall Group ecommerce and pricing functions within Central Europe, with the platform supporting product analytics, pricing decisioning, and faster feature delivery.
Governance shifted toward decentralized pipeline ownership and self-service analytics, reducing data-engineering bottlenecks and accelerating developer-driven feature deployment according to the customer account of Keboola. The Keboola Platform served as the primary Analytics and BI toolchain for Mall.cz, enabling controlled autonomy for engineering teams and operationalizing dynamic pricing at scale.
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Rohlik | Retail | 1000 | $75M | Czech Republic | Keboola | Keboola Platform | Analytics and BI | 2014 | n/a |
In 2014, Rohlik implemented Keboola Platform as its central data operations hub for retail and e‑commerce analytics. The Keboola Platform is classified in the Analytics and BI category and was chosen from day one of Rohlik’s data stack.
Rohlik uses Keboola Platform to centralize BI, ETL, and ML orchestration capabilities, running thousands of daily pipelines to support logistics and supply‑chain optimization, personalized recommendations, and dynamic pricing. Implementation emphasis is on pipeline orchestration, data transformation, scheduling, and operational analytics, with the Keboola Platform handling extraction, normalization, and delivery of analytical datasets.
The deployment is organized as a central data operations layer that feeds analytics and downstream decisioning across logistics, merchandising, pricing, and data science teams. Governance is embedded in data ops practices, with pipeline monitoring, versioned transformations, and orchestration workflows used to ensure repeatable ingestion and model operationalization across Rohlik’s retail systems.
The Keboola Platform has been in production since Rohlik’s early years and is used to run the company’s BI and ML pipelines that aim to improve average order value and reduce food waste, while supporting ongoing analytics for inventory and pricing decisions.
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