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.
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Showing 10 of 2 companies ranked by annual revenue.
Rosh Ha'Ayin, Hamerkaz, Israel
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.
Kaohsiung City, T'ai-wan, Taiwan
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.
- 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
Each Tuesday, we reverse-engineer a real SaaS company's revenue, profit, CAC, funnels, and its top growth tactic.
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