As of August 2026, Latka tracks 19 big data software companies with $10M–$100M in annual revenue. They have combined revenues of $537.4M and employ 3.4K people. They have raised $1.5B 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 19 companies ranked by annual revenue.
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.
Bangalore, Karnataka, India
CoffeeBeans Consulting is a technology partner dedicated to driving business transformation. With deep expertise in Cloud, Data, MLOPs, AI, Infrastructure services, Application modernization services, Blockchain, and Big Data, we help organizations tackle complex challenges and seize growth opportunities in today’s fast-paced digital landscape. We’re more than just a tech service provider; we're a catalyst for meaningful change. What Makes CoffeeBeans Different? At CoffeeBeans, we believe that technology should empower—not complicate—businesses. We stand apart by offering holistic tech solutions that cover every phase of growth, from Product Development to DevOps, providing seamless integration and ongoing support. Our approach focuses on both innovation and practical impact, ensuring that our solutions are not only cutting-edge but also drive real, measurable results. With specialized expertise across industries such as BFSI, AgriTech, IT, eCommerce, and Supply Chain, we tailor our strategies to address specific challenges and needs. Additionally, our flexible engagement models allow clients to access on-demand tech expertise, ensuring they stay agile and competitive in a rapidly evolving market. Technology Capabilities Our team excels in: - Cloud and Data - Big Data and Analytics - Data Science and AI/ML Model Development - Blockchain Technology - Data Engineering Partner with CoffeeBeans Consulting to take your business forward with technology that truly makes a difference.
Hampton, New Hampshire, United States
QAT Global is an information technology company offering cloud computing, big data, analytics, and integration solutions.
San Ramon, California, United States
Tessell is an AI-native, multi-cloud DBaaS platform that unifies transactional and analytical database management into a single intelligent control plane, automating operations, reducing costs by up to 45%, and boosting performance by over 50%.
Latka tracks 19 big data software companies with $10M–$100M in annual revenue. Together they generate $537.4M in annual revenue and employ 3.4K people.
Rebellions is the largest, with $53.3M in annual revenue, founded in 2020.
The average big data software company in this list makes $28.3M a year, across 19 companies with reported revenue. They serve 20M customers combined.
Ranked by annual revenue, the leaders are Rebellions, Aiven, Aerospike, Dremio and CoffeeBeans Consulting.
The 19 big data software companies with $10M–$100M in annual revenue tracked here have raised $1.5B 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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