List of Dremio Enterprise Customers
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Since 2010, our global team of researchers has been studying Dremio Enterprise 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 Dremio Enterprise 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 Dremio Enterprise for Analytics and BI include: KION Group, a Germany based Manufacturing organisation with 41827 employees and revenues of $12.06 billion, Maersk, a Denmark based Transportation organisation with 100000 employees and revenues of $8.72 billion, TransUnion, a United States based Professional Services organisation with 13200 employees and revenues of $3.83 billion and many others.
Contact us if you need a completed and verified list of companies using Dremio Enterprise, 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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KION Group | Manufacturing | 41827 | $12.1B | Germany | Dremio | Dremio Enterprise | Analytics and BI | 2023 | n/a |
In 2023 KION Group deployed Dremio Enterprise to break down data silos and accelerate operational analytics across product analyses, warranty cases, and merchandise management. The Dremio Enterprise implementation was delivered as an Analytics and BI platform running on Microsoft Azure and targeted manufacturing and service operations across Europe.
The deployment emphasized real-time self-service analytics and query acceleration, providing operational teams with interactive access to consolidated datasets. Dremio Enterprise was applied to a dataset of roughly 200 million records, reducing query time from approximately 30 minutes to about 3 seconds, and enabling faster exploration of product and warranty data.
Operational coverage included manufacturing and field service functions, with use cases focused on product analysis, warranty case analytics, and merchandise management reporting. The architecture on Microsoft Azure centralized query processing and data access while exposing self-service capabilities to downstream operational teams to reduce reliance on centralized reporting.
Governance changes centered on enabling decentralized analytics workflows and establishing self-service access controls and data cataloging to support operational decision making. The implementation delivered real-time Analytics and BI capabilities to frontline teams, shorter analysis cycles, and a pronounced reduction in query latency on large operational datasets.
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Maersk | Transportation | 100000 | $8.7B | Denmark | Dremio | Dremio Enterprise | Analytics and BI | 2022 | n/a |
In 2022, Maersk implemented Dremio Enterprise to rebuild its analytics platform and provide Analytics and BI capabilities across logistics and operations. The deployment positioned Dremio Enterprise as the centralized query engine unifying logistics, shipping, warehousing and cargo datasets, supporting supply chain and operations use cases across global regions and scaling to millions of daily queries and thousands of users while achieving very high uptime.
The implementation configured Dremio Enterprise as the enterprise analytics layer, with the lakehouse serving as the consolidated data substrate for operational reporting. Module usage aligned with enterprise analytics for logistics and operations, and the platform was tuned for high concurrency and query performance to support operational reporting workloads.
Integrations included migrating SAP reporting workloads towards the lakehouse and surfacing those via Dremio Enterprise query acceleration, enabling analytical access without creating separate reporting silos. The operational coverage spanned supply chain, shipping and warehousing functions, with thousands of users across Maersk regions relying on the unified Analytics and BI environment.
Governance and workflow changes centralized query access and standardized analytics workflows, with Dremio Enterprise functioning as a governed query layer to reduce duplication of datasets. The project delivered stated reliability and performance improvements and sustained high uptime for mission critical operational analytics.
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TransUnion | Professional Services | 13200 | $3.8B | United States | Dremio | Dremio Enterprise | Analytics and BI | 2018 | n/a |
In 2018, TransUnion implemented Dremio Enterprise as a unified query engine to support global credit, fraud, and risk analytics across a hybrid multi-cloud environment. The deployment positioned Dremio Enterprise as the central Analytics and BI platform to enable secure, governed self-service analytics for data scientists and analysts working on credit scoring and consumer insight use cases across multiple regions.
The implementation emphasized enterprise analytics and risk analytics capabilities, including a governed data catalog, SQL-based query federation, and query acceleration to reduce time to insight for modeling teams. Configuration prioritized role based access controls and metadata-driven discoverability to allow analysts to explore datasets while preserving compliance and data lineage requirements.
Architecturally, Dremio Enterprise was embedded as a unified query layer over TransUnion’s hybrid multi-cloud data estate, providing a single logical access point to distributed data stores and cloud data lakes. The deployment supported cross region operational coverage for credit scoring and fraud detection workflows, and it served both analytic modeling pipelines and ad hoc analysis by business analysts.
Governance and rollout focused on operationalizing governed self-service, with policies for access control, catalog curation, and workflow handoffs between data engineering, analytics, and risk teams. The implementation supported improved credit models and expanded access to credit through more consistent, governed analytics outcomes for consumer insight and risk decisioning.
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