List of Dremio Community Edition Customers
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Since 2010, our global team of researchers has been studying Dremio Community Edition 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 Community Edition 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 Community Edition for Analytics and BI include: Amazon, a United States based Retail organisation with 1578000 employees and revenues of $637.96 billion, NetApp, a United States based Professional Services organisation with 11800 employees and revenues of $6.27 billion, FactSet, a United States based Professional Services organisation with 12886 employees and revenues of $2.32 billion and many others.
Contact us if you need a completed and verified list of companies using Dremio Community Edition, 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.
The Dremio Community Edition customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Amazon | Retail | 1578000 | $638.0B | United States | Dremio | Dremio Community Edition | Analytics and BI | 2023 | n/a |
In 2020, Amazon implemented Dremio Community Edition within its Supply Chain Optimization Technologies Finance Analytics team as part of its Analytics and BI tooling for supply chain finance analytics in the United States. The engagement centers on accelerating query performance and shortening analytic project setup for SCOT finance use cases, with vendor materials indicating initial use of Dremio Community Edition.
The implementation emphasized query acceleration and interactive analytics capabilities common to Analytics and BI platforms, configured to support ad hoc and project-based supply chain finance analysis. Dremio Community Edition was used to provision datasets and accelerate analytic workloads, reducing the time to run exploratory and recurring queries.
Deployment was executed quickly via an AWS CloudFormation template, indicating a cloud-native provisioning model within Amazon's AWS environment and a scripted infrastructure deployment approach. Operational coverage was scoped to the SCOT Finance Analytics team in the United States, focusing on finance and analytics functions tied to supply chain operations.
Governance and rollout relied on rapid provisioning to enable faster project onboarding, with documented outcomes from the customer story including roughly 10x faster query performance, elimination of about 60 hours of work per project, and approximately 90% reduction in setup time. These results were reported by Dremio in the customer narrative and align with the stated configuration and deployment approach.
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FactSet | Professional Services | 12886 | $2.3B | United States | Dremio | Dremio Community Edition | Analytics and BI | 2022 | n/a |
In 2022, FactSet implemented Dremio Community Edition to modernize client-facing financial applications within its finance and analytics stack. The deployment targeted Analytics and BI use cases, focusing on high latency data access and the need to accelerate new screening services for clients.
The implementation layered Dremio on top of an AWS S3 data lake, positioning Dremio Community Edition as a query acceleration and data virtualization layer to serve curated datasets to downstream applications. Configuration emphasized interactive SQL query performance and metadata acceleration rather than wholesale data movement, enabling data access times to move from approximately 10 minutes to under 30 seconds and supporting faster screening workflows.
Operational scope concentrated on client-facing financial applications and screening services used by FactSet analytics and finance teams, with the Dremio layer providing a consistent access plane to S3. Governance and workflow adjustments aligned analytics consumers to the centralized data access layer, enabling controlled dataset provisioning, standardized query patterns, and a development path for accelerated client service rollout.
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NetApp | Professional Services | 11800 | $6.3B | United States | Dremio | Dremio Community Edition | Analytics and BI | 2023 | n/a |
In 2023, NetApp's Active IQ team deployed Dremio Community Edition to modernize its Hadoop and telemetry analytics platform. The Dremio Community Edition deployment is categorized as Analytics and BI and established an operational analytics layer for telemetry and Hadoop-sourced data.
Implementation focused on decoupling storage and compute, moving query execution off tightly coupled cluster compute into a separate, elastic query execution tier. Functional capabilities implemented emphasized SQL-based telemetry analytics, ad hoc operational query processing, and centralized data discovery to support Active IQ operations.
Integrations included direct access to the existing Hadoop data lake and telemetry ingestion streams, enabling the Dremio Community Edition environment to operate against persisted storage while provisioning elastic compute for queries. Operational scope targeted Active IQ analytics and operations workflows, consolidating telemetry analytics functions that had been executed alongside Hadoop compute.
The vendor case study documents outcomes, specifically that decoupling storage and compute reduced query times from approximately 45 minutes to approximately 2 minutes and reduced compute and storage costs. The implementation reoriented telemetry analytics toward a separate, elastic query tier within NetApp's operations and analytics environment.
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