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.
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.
Lyzer is an AI-powered data analytics and decision intelligence platform that helps businesses analyze data, generate insights, and automate data-driven decisions without requiring deep technical expertise.
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.
GigaSpaces helps enterprises apply AI to real-time, structured operational data — enabling spontaneous, natural language conversations that accelerate decision-making, improve agility in unexpected situations, and surface opportunities and risks in everyday business.
With over two decades of experience powering mission-critical systems, GigaSpaces is a pioneer in real-time data technology and a trusted foundation for data-driven services across industries, including finance, travel, telecom, and insurance.
Headquartered in the US, with offices in Europe and Israel, GigaSpaces helps organizations turn operational data into decisions that are timely, secure, and scalable.
Operator of an infrastructure data capture and analytics platform intended to be used for commercial applications. The company's platform offers cloud-based content management, HD videos and photos and data analytics, enabling clients to create value by delivering data efficiently and effectively through our content management system.
Provider of on-premise edge cloud solutions designed to enable unlimited mobile network capacity and secure connectivity while collecting and analyzing mobile device and IoT data to allow enterprises to deliver and monetize new services and applications. ASOCS serves retail, real estate, corporate offices, hospitality, hospitals and sports and entertainment markets.
EGK merged by Carota in 2022 and provides SaaS, PaaS, IaaS system services in the fields of people, goods, and vehicle management, data analysis and supply chain solutions.
Revenue
$8.4M
Customers
-
Year founded
2010
Funding
-
Team size
-
Growth
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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
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