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As of August 2026, there are 8 SaaS companies in Big Data Processing And Distribution Systems. They have combined revenues of $254.6M and employ 513 people. They have raised $727.5M and serve 1M customers combined.
Big Data Processing and Distribution Systems refer to technologies that facilitate the collection, storage, processing, and distribution of vast amounts of data across multiple platforms. These systems often enable real-time data insights and analytics, which can be critical for businesses seeking to make data-driven decisions. Common use cases include predictive analytics, customer experience enhancement, and operational efficiency improvements. Typically, these systems are utilized by IT professionals, data scientists, and business analysts who require robust data management capabilities. They often feature batch processing, stream processing, and various analytics tools. Integration with existing IT infrastructures and support for distributed computing are also crucial, allowing organizations to scale their operations and handle large datasets effectively.
Sorting: Highest -> Lowest
Showing 10 of 8 companies ranked by annual revenue.
Pudong New Area, Shanghai, China
Provider of enterprise cloud services intended to help companies meet their data management needs. The company's platform offers integrated storage, CDN integration and data processing capabilities, including data insights and accelerated transmission, enabling businesses across China to efficiently manage all their data needs.
- The software must facilitate the collection, processing, and analysis of large datasets. - It should support both batch and real-time data processing capabilities. - Systems must allow for data distribution across multiple environments or nodes. - It should provide analytics tools that enable users to extract meaningful insights from data. - Target users must include IT professionals, data analysts, or business analysts. - Not just data storage solutions; must also enable data processing and analytics.
Chicago, Illinois, United States
Provider of a big data platform intended to offer rapid analysis of the world's largest datasets. The company's big data platform works on large datasets that are measured in tens of terabytes, petabytes or exabytes with trillions or quadrillions of rows, as well as focuses on ingressing billions of rows per second, filtering and computing aggregate results at rates up to trillions of rows per second, enabling organizations to harness the untapped power of large datasets, at scale and speed.
New York, New York, United States
Provider of a cloud-based data processing software intended to simplify the deployment of real-time applications. The company's software eliminates infrastructure problems, facilitates rapid iteration and testing and is used to run real-time applications to process large volumes of data while being agnostic to infrastructure and scale, enabling data engineers, architects and providers of data infrastructure to improve outpatient monitoring, real-time bidding and risk profiling.
Chicago, Illinois, United States
Provider of a platform for data storage and processing. The company offers a cloud-based platform UNiTY that helps to access real-time insight to any information needed without storing client's data.
United States
Developer of a SaaS based data processing software designed to unleash the power of modern CPUs. The company's software uses open source databases to accelerate Generic Probe Database Browser (GPDB) for the data warehouse, enabling users to enhance their system's efficiency.
Bangalore, Karnataka, India
CloudInfra builds soft infrastructure services on the cloud. Our flagship product helps users use linux power tools (grep, sed, awk, ...) on their data stored on cloud. Imagine running a grep on Tbs of data stored on S3 with in few seconds. Not just that, we allow you to run map-reduce kind of operations all in your familiar linux commands, no java/pig scripts to learn. Cloudinfra is founded by Ex-Google guys, with immense experience handling big data and machine learning.
