List of Qubole Platform Customers
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United States
Since 2010, our global team of researchers has been studying Qubole 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 Qubole Platform for ML and Data Science Platforms 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 Qubole Platform for ML and Data Science Platforms include: Gaia, Inc., a United States based Media organisation with 150 employees and revenues of $79.6 million, Oogst, a Netherlands based Healthcare organisation with 3 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using Qubole 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.
The Qubole Platform 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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Gaia, Inc. | Media | 150 | $80M | United States | Qubole | Qubole Platform | ML and Data Science Platforms | 2020 | n/a |
In 2020, Gaia, Inc. implemented Qubole Platform on AWS to deploy an ML-based recommendation engine for content personalization. The deployment is categorized as ML and Data Science Platforms and was focused on personalization for Gaia's media and streaming service in the United States.
The Qubole Platform implementation centralized Spark clusters and XGBoost model workflows to support both batch and near real time scoring, with pipelines for data preparation, model training, and scheduled model promotion. Configuration emphasized Spark job orchestration and XGBoost model execution as core functional capabilities, enabling automated training runs and repeatable model deployments within the Qubole environment.
Architecturally the solution ran on AWS infrastructure with Qubole managing compute elasticity and Spark orchestration, and models served into Gaia's content delivery and personalization stack to influence viewer recommendations. Operational scope included personalization and analytics workflows, and the implementation impacted engineering and content teams responsible for model iteration and content targeting.
Governance work introduced model orchestration and monitoring practices to manage production model versions and troubleshooting workflows. Within eight months of going live, Qubole Platform powered Spark/XGBoost models drove about a 50% lift in average minutes watched and materially reduced engineering troubleshooting time.
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Oogst | Healthcare | 3 | $1M | Netherlands | Qubole | Qubole Platform | ML and Data Science Platforms | 2018 | n/a |
In 2018, Merkle deployed the Qubole Platform to modernize its data science and machine learning workflows for marketing and analytics in the United States. The implementation positioned Qubole Platform within Merkle's ML and Data Science Platforms capability set to support model development, experimentation, and production delivery of client facing models.
The deployment emphasized Qubole's managed Spark and Presto runtimes and autoscaling compute, providing a managed execution environment for large scale data processing and iterative model runs. Merkle reported that model runtimes fell from about one day to five to six hours, while compute costs were reduced, enabling faster experimentation cycles. These capabilities were applied directly to marketing and analytics use cases and to the delivery pipeline for client facing machine learning models.
Operational scope focused on Merkle's marketing and analytics teams in the United States, consolidating data science workloads onto Qubole to improve scalability and repeatability of experiments. The deployment centralized cluster management, automated resource scaling, and job orchestration typical of ML and Data Science Platforms, allowing data scientists to iterate without manual infrastructure provisioning. Governance adjustments supported standardized runtime environments and experimentation workflows to accelerate model delivery.
Explicit outcomes from the implementation include shorter model runtimes, reduced compute costs, and faster delivery of client facing ML models.
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