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Top 20 Big Data Analytics Software SaaS Companies in May 2026

As of May 2026, there are 20 SaaS companies in Big Data Analytics Software. They have combined revenues of $348.6M and employ 1.8K people. They have raised $1.1B and serve 500.1K customers combined.

Big Data Analytics Software encompasses tools and systems designed to collect, process, and analyze extensive and rapidly changing data sets. These platforms enable organizations to derive meaningful insights from large volumes of structured and unstructured data, facilitating decision-making and strategic planning. Typical use cases include predictive analytics, customer behavior analysis, operational optimization, and trend identification. Key features of Big Data Analytics Software often include data integration, real-time analytics, machine learning capabilities, and advanced visualization tools. Users typically range across various sectors, including IT, marketing, finance, and operations, with individuals like data scientists, business analysts, and decision-makers engaging with these systems to enhance their organizational intelligence. The ability to analyze data at scale not only informs business strategies but also drives innovation across industries.

Companies
20
Revenue
$348.6M
Funding
$1.1B
Employees
1.8K

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Top Big Data Analytics Software Companies

Showing 10 of 1 companies ranked by annual revenue.

1
SambaNova Systems

Palo Alto, California, United States

SambaNova is the leading Enterprise AI company that delivers a full-stack infrastructure from silicon to software, specializing in machine learning and big data analytics platforms.

Revenue
$100M
Customers
-
Year founded
2017
Funding
$982M
Team size
417
Growth
-

Inclusion Criteria

- Must provide capabilities for analyzing large and complex data sets - Must support real-time data processing and analytics - Must include data visualization tools to present insights effectively - Must offer integration with other data sources and tools - Not just traditional reporting; must also enable predictive and prescriptive analytics - Should allow for machine learning model deployment and management - Must cater to multiple industries and use cases, including operational, customer, and predictive analytics