Top 10: Data Platforms

Data is the foundation of the AI-powered enterprise, enabling organisations to transform information into actionable intelligence and competitive advantage.

As businesses accelerate their AI ambitions, the ability to connect, govern and activate data at scale has become a top priority. Leading data platforms are driving this shift, providing the infrastructure for advanced analytics, Gen AI and real-time decision-making.

Here, AI Magazine takes a look at the platforms helping enterprises unlock greater value from their data and turn insight into measurable business impact.

10. MongoDB

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

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

Founded: 1979
Employees: 9,900+
President and CEO: Steve McMillan
Revenue: US$1.7bn

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

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

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

Founded: 1993
Employees: 5,200+
President and CEO: Amit Walia
Revenue: US$1.7bn​​​​​​​

Informatica manages more than 110 trillion transactions monthly through its Intelligent Data Management Cloud (IDMC). 

It has evolved from an ETL tool to become a critical AI-powered governance engine. 

By automating data quality, metadata management and master data synchronisation, Informatica ensures that the massive datasets utilised by world-leading firms are AI-ready, clean and fully compliant with evolving global privacy regulations.

6. IBM watsonx

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

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

Founded: 2002
​​​​​​​Employees: 143,000
President and CEO: Matt Garman
Revenue: US$128.7bn

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

Founded: 2008
Employees: ~54,000
President and CEO: Thomas Kurian
Revenue: US$71bn

A leader in serverless, planet-scale analytics via its BigQuery data warehouse, Google Cloud is a leader in the data space.

BigQuery ML and native integration with Vertex AI, for example, allow data scientists to run complex machine learning models directly on their data without any movement. 

This data-to-model efficiency is a major draw for companies that prioritise speed and innovation. Its ability to handle petabytes of data with zero infrastructure management makes it a desirable tool for the modern, AI-driven enterprise.

3. Microsoft Fabric

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

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

Founded: 2012
Employees: 7,800+
Chairman and CEO: Sridhar Ramaswamy
​​​​​​​Revenue: US$4.7bn

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

Founded: 2013
Employees: 9,000
Co-Founder and CEO: Ali Ghodsi
Revenue: US$6.9bn

Databricks takes top spot 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.