Top 10: Data Platforms

Data sits at the heart of modern enterprise, powering everything from smarter decisions to the next wave of AI innovation. 

With this in mind – and as organisations race to connect, govern and activate their data – data platforms lead the charge to drive a new era of intelligent, data‑driven transformation. 

From cloud analytics to Gen AI, these 10 platforms are helping businesses turn information into intelligence and insights into real impact.

10: MongoDB

Revenue: US$2bn  (fiscal year 2025)
Employees: 5,500+ 
President and CEO: Chirantan “CJ” Desai
​​​​​​​Founded: 2007

MongoDB has grown from its origins as a niche NoSQL database to become a comprehensive developer data platform. 

Its evolution has been fuelled by MongoDB Atlas, which offers integrated vector search and stream processing capabilities. This allows engineering teams at some of the world’s largest and most successful companies to build sophisticated, AI-enriched applications without the complexity of managing multiple disjointed databases. 

By providing a unified document-based model that scales effortlessly across public clouds, MongoDB is a solid foundation for modern, data-intensive software architectures.

9: Teradata

Revenue: US$1.7bn (full year 2025)
Employees: 9,900+
President and CEO: Steve McMillan
​​​​​​​Founded: 1979

A particularly strong data platform for the telco and financial sectors, Teradata handles some of the world’s most massive data workloads. 

With the successful rollout of VantageCloud Lake, the company has modernised its legacy of high-performance analytics into a flexible, cloud-native architecture. 

Teradata’s strength lies in its ability to handle complex, multi-statement queries at a scale, enabling enterprises to reliably harmonise their data fabric across hybrid and multi-cloud environments.

8: Cloudera

Revenue: N/A
Employees: 3,200+
President and CEO: Charles Sansbury
​​​​​​​Founded: 2008

Cloudera is a leader in hybrid data management, providing a unified platform for organisations that cannot move entirely to the public cloud. 

Its Cloudera Data Platform (CDP) offers consistent security and governance across on-premises, private cloud and multiple public cloud deployments. 

Cloudera is a strong data platform for highly-regulated firms, offering the Shared Data Experience (SDX) layer that ensures data remains compliant and discoverable – regardless of where it geographically resides.

7: Informatica

Revenue: US$1.7bn (TTM as of September 2025)
Employees: 5,200+
President and CEO: Amit Walia
​​​​​​​Founded: 1993

6: IBM watsonx

Revenue: US$67.5bn (full-year 2025)
Employees: ~300,000
President and CEO: Arvind Krishna
​​​​​​​Founded: 1911

IBM’s data strategy centres on watsonx.data, an open-source-based lakehouse designed to scale AI workloads while significantly reducing storage costs. 

By leveraging open formats like Apache Iceberg and Presto, IBM allows enterprises to access their data across hybrid environments without being locked into proprietary silos. 

With its massive global consulting arm, IBM is a data powerhouse and supports those looking to implement a governed, open-standard data architecture – one that supports both traditional analytics and Gen AI.

5: AWS

Revenue: US$128.7bn (full year 2025)
Employees: 143,000
President and CEO: Matt Garman
​​​​​​​Founded: 2002

Amazon Redshift by AWS removes the friction of moving data between databases and warehouses, allowing enterprises to gain real-time insights with minimal engineering overhead. 

The deep integration between Redshift, S3 and the Bedrock AI platform provides a seamless pipeline for training foundation models, making it a high-performance choice for organisations already anchored in the world’s largest cloud ecosystem. 

In addition, its serverless scaling capabilities ensure businesses can manage unpredictable workloads while maintaining strict cost governance and high-speed analytical performance.

4: Google Cloud

Revenue: US$71bn (annual revenue run rate as of early 2026)
Employees: N/A
President and CEO: Thomas Kurian
​​​​​​​Founded: 2008

3: Microsoft Fabric

Revenue: US$281.7bn (fiscal year to June 2025)
Employees: 228,000
Chairman and CEO: Satya Nadella
​​​​​​​Founded: 1975

Microsoft Fabric is a unified analytics platform that brings together Data Factory, Synapse and Power BI in one place. 

Its OneLake concept acts as a single, virtualised data lake for the entire organisation, eliminating data silos once and for all. 

Fabric’s deep integration with the Microsoft 365 Copilot ecosystem has placed it among the fastest-growing data platform used by Global 2000s, enabling business users to query complex data using natural language.

2: Snowflake

Revenue: US$4.7bn (fiscal year ending January 2026)
Employees: 7,800+
Chairman and CEO: Sridhar Ramaswamy
​​​​​​​Founded: 2012

Snowflake has greatly expanded from its origins as a cloud data warehouse. 

Now a data cloud for the majority of the world’s largest enterprises, one of its most valuable data offerings is Snowflake Cortex. The fully-managed AI service allows enterprises to build and deploy Gen AI applications directly within their secure data perimeter. 

By eliminating the need to move sensitive information to external AI providers, Snowflake ensures that large-scale organisations can leverage LLMs while maintaining strict data sovereignty and compliance. 

What’s more, the platform’s industry-leading Data Marketplace has evolved into a strategic business network, enabling seamless, secure data sharing across entire supply chains without traditional ETL overhead. 

Another strength lies in its ability to ensure global firms can maintain a unified governance layer regardless of their underlying infrastructure, making Snowflake a strong partner for data-driven strategic growth.

1: Databricks

Revenue: US$5.4bn (annual revenue run rate as of February 2026)
Employees: 9,000
Co-Founder and CEO: Ali Ghodsi
​​​​​​​Founded: 2013

Clinching top spot is Databricks thanks to its ability to successfully unify data engineering, data science and warehousing through its pioneering Lakehouse architecture. 

One key strength is the full integration of Mosaic AI, which empowers enterprises to train, fine-tune and deploy their own proprietary foundation models using their unique internal data.

Unlike generic AI solutions, Databricks provides a data intelligence layer that understands the specific semantics of a company’s data – leading to higher accuracy and lower costs for compound AI systems. Central to this ecosystem is Unity Catalog, which offers robust governance for both data and AI assets across a unified fabric. 

By championing open standards like Apache Iceberg and Delta Lake, Databricks prevents vendor lock-in while providing the performance of a traditional warehouse. This makes it a firm foundation for the next generation of autonomous enterprise applications.