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Top 8 Big Data Processing And Distribution Systems SaaS Companies in May 2026

As of May 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.

Companies
8
Revenue
$254.6M
Funding
$727.5M
Employees
513

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Top Big Data Processing And Distribution Systems Companies

Showing 10 of 2 companies ranked by annual revenue.

1
Uni B Solutions

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.

Revenue
$7.8M
Customers
-
Year founded
2012
Funding
-
Team size
3
Growth
2777.09%
2
Arroyo

Berkeley, California, United States

Cloud-native stream processing

Revenue
$5M
Customers
-
Year founded
2022
Funding
-
Team size
2
Growth
-

Inclusion Criteria

- 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.