As of August 2026, Latka tracks 69 big data software companies. They have combined revenues of $2.9B and employ 15.6K people. They have raised $4.4B and serve 20M customers combined.
Every company below sells big data software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
Big Data Software encompasses tools and platforms designed to store, manage, analyze, and visualize large volumes of data that traditional data processing software cannot handle effectively. These solutions enable organizations to derive insights from various data sources, helping in making data-driven decisions. Primary use cases include predictive analytics, operational intelligence, customer behavior analysis, and fraud detection, among others. Typical features of Big Data Software include data ingestion, storage, processing frameworks, and visualization capabilities. Users range from data scientists and business analysts to IT professionals who are responsible for managing the data lifecycle and ensuring data security. Common buyer personas include professionals from finance, marketing, operations, and research and development, all seeking to leverage big data for enhanced strategic decision-making.
Sorting: Highest -> Lowest
Showing 10 of 69 companies ranked by annual revenue.
San Jose, California, United States
Our mission at Cohesity is simple: to protect, secure, and provide insights into the world’s data. The largest organizations around the globe rely on us to strengthen their business resilience.
San Francisco, California, United States
Lambda is an AI infrastructure company providing cloud services, infrastructure, and software training and inferencing of AI models. It serves as a trusted AI Infrastructure advisor to the world's top AI Labs, Enterprises, and Hyperscalers.
New York, New York, United States
Accelerating time-to-insight for workload-intensive applications, the VAST Data Platform delivers scalable performance, radically simple data management and enhanced productivity for the AI-powered world. Launched in 2019, VAST is the fastest-selling data infrastructure startup in history.
Seattle, WA, United States
Modern enterprises have to manage exponentially-growing exabyte-scale data stores comprised mostly of unstructured data. Someone (often IT) has the difficult job of staying on top of managing these data stores, which becomes more difficult to do as enterprise datasets expand from the data center to the edge and cloud. And while scale and complexity are rising, budgets and staff are not. Existing solutions suffer from two crucial shortcomings: Complexity and platform lock-in. Legacy solutions are excruciatingly complex to deploy and manage. And nearly every solution restricts users to running in only the data center and on expensive, inflexible proprietary hardware platforms. Qumulo is the simple way to manage exabyte-scale data anywhere — edge, core, or cloud — on the platform of your choice. In a world with trillions of files and objects comprising 100+ zettabytes worldwide, companies need a solution that combines the ability to work anywhere with simplicity. This is precisely what Qumulo was founded to accomplish.
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.
Seongnam, Gyeonggi-do, South Korea
Rebellions is a South Korean AI accelerator startup specializing in the development of AI hardware for data centers, including chips and systems designed for efficient inference in large-scale applications.
Helsinki, Finland
Aiven is your AI-ready Open Source Data Platform. Aiven is an AI-ready open source data platform company, helping organizations gain more value from their data. Aiven’s cloud platform combines open choice services to stream, store, and serve data, simply, securely, and rapidly across major cloud providers to power innovation. Aiven is trusted by thousands of customers to create next-generation applications confidently and quickly. Aiven is headquartered in Helsinki and has hubs in Amsterdam, Berlin, Paris, London, Singapore, Sydney, Auckland, Austin and Toronto. To learn more about Aiven, visit https://aiven.io.
Mountain View, California, United States
Provides database server so companies can compile and analyze large amounts of data
Santa Clara, California, United States
Dremio is the intelligent lakehouse platform trusted by thousands of global enterprises, including Shell, TD Bank, Michelin, and Farmer’s Insurance. AI and analytics initiatives face significant delays due to the time-intensive process of dataset creation. Data engineering teams are overburdened, disconnected data sources require complex ETL processes, and prolonged iteration cycles with business stakeholders slow progress. Dremio eliminates these bottlenecks by unifying data sources without ETL, simplifying the creation of high-quality, governed datasets, and delivering autonomous performance optimization to accelerate AI. From the original co-creators of Apache Polaris and Apache Arrow, Dremio is the only lakehouse built natively on Apache Iceberg, Polaris, and Arrow - providing flexibility, preventing lock-in, and enabling community-driven innovation.
Latka tracks 69 big data software companies with reported revenue. Together they generate $2.9B in annual revenue and employ 15.6K people.
Cohesity is the largest, with $1.3B in annual revenue, founded in 2013.
The average big data software company in this list makes $42.4M a year, across 69 companies with reported revenue. They serve 20M customers combined.
Ranked by annual revenue, the leaders are Cohesity, Lambda AI, Denodo Technologies Inc, VAST Data and Qumulo.
The 69 big data software companies tracked here have raised $4.4B in disclosed funding between them.
- Product must be capable of handling and processing large volumes of structured and unstructured data. - Must provide advanced analytics features such as machine learning or predictive modeling. - Should include data visualization tools to present insights clearly and effectively. - Must support integration with various data sources and formats. - Not just data storage; must also offer actionable insights and analytics capabilities.
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