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Top 2 Big Data Analytics Software Companies With $5M–$10M Revenue (September 2026)

As of September 2026, Latka tracks 2 big data analytics software companies with $5M–$10M in annual revenue. They have combined revenues of $17.1M and employ 81 people. They have raised $24M.

Every company below sells big data analytics 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.

What Big Data Analytics Software Companies do

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
2
Revenue
$17.1M
Funding
$24M
Employees
81

Filters

Sorting: Highest -> Lowest

Filters

Top Big Data Analytics Software Companies With $5M–$10M Revenue

Showing 2 of 2 companies ranked by annual revenue.

1ASOCS logo
ASOCS

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.

Revenue
$8.7M
Year founded
2003
Funding
$24M
Team size
81
2EGK logo
EGK

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.

Revenue
$8.4M
Year founded
2010

Frequently asked questions about Big Data Analytics Software Companies With $5M–$10M Revenue

How many big data analytics software companies with $5M–$10M in annual revenue are there?

Latka tracks 2 big data analytics software companies with $5M–$10M in annual revenue. Together they generate $17.1M in annual revenue and employ 81 people.

Which big data analytics software company with $5M–$10M in annual revenue is the largest?

ASOCS is the largest, with $8.7M in annual revenue, founded in 2003.

How much revenue does a typical big data analytics software company with $5M–$10M in annual revenue make?

The average big data analytics software company in this list makes $8.6M a year, across 2 companies with reported revenue.

Who are the leading Big Data Analytics software vendors with $5M–$10M in annual revenue?

Ranked by annual revenue, the leaders are ASOCS and EGK.

How much funding have big data analytics software companies with $5M–$10M in annual revenue raised?

The 2 big data analytics software companies with $5M–$10M in annual revenue tracked here have raised $24M in disclosed funding between them.

Related IT Infrastructure Software categories

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