The data platform landscape has undergone a dramatic transformation in recent years, becoming increasingly central to modern business operations. As organisations navigate through petabytes of data across distributed environments, these platforms serve as the critical infrastructure powering everything from customer analytics to automated decision-making systems.
Today’s leading data platforms represent a quantum leap from their predecessors, offering sophisticated capabilities that extend far beyond traditional data warehousing. They seamlessly integrate real-time processing, advanced analytics and machine learning operations while maintaining robust security and governance frameworks. Many have evolved to offer low-code interfaces alongside powerful programming environments, democratising data access across organisations.
This month we highlight the Top 10 data platforms.
10. Splunk
- Revenue: US$$4.216bn (FY2024)
- Employees:8,000
- GM: Gary Steele
- Founded: 2003
Splunk Inc specialises in machine-generated data analysis, processing information from computer systems, networks and Internet of Things devices. The platform indexes data for rapid searching through its Search Processing Language, designed for log analysis. Security teams use Splunk to detect network anomalies and coordinate incident response. The system processes real-time data streams, enabling immediate issue identification. Organisations deploy Splunk across corporate data centres and cloud environments for information technology monitoring, application performance tracking and security threat investigation. The platform generates visualisations for system performance and security metrics.
9. SAP HANA Cloud
- Revenue: €31.2bn (US$32.4bn) (FY2023)
- Employees:107,000
- CEO: Christian Klein
- Founded:1972
SAP's HANA Cloud provides in-memory database services, storing information in computer memory rather than on disk drives. The platform processes structured and unstructured data across SAP’s data centres and third-party cloud services, enabling organisations to meet data sovereignty requirements. HANA Cloud implements automated updates and demand-based resource scaling with usage-based pricing. The system connects with business applications across manufacturing, finance and supply chain operations. SAP integrates AI capabilities for pattern analysis and automated decision-making within applications.
8. Databricks
- Revenue: US$1.6bn (FY2023)
- Employees: 7,000
- CEO: Ali Ghodsi
- Founded: 2013
Former Apache Spark developers established Databricks Inc in 2013, introducing the ‘lakehouse’ architecture that combines data lake and warehouse functionality. The platform scales computing resources based on demand and integrates with existing data systems. Its MLflow component tracks machine learning experiments, while Delta Lake ensures database operation integrity. In 2024, Databricks launched DBRX, distributing artificial intelligence tasks across multiple neural networks. The system serves data engineers transforming information, scientists developing models and analysts examining business performance.
7. Microsoft Intelligent Data Platform
- Revenue: US$245bn (FY2024)
- Employees: 228,000
- CEO: Satya Nadella
- Founded:1975
Microsoft introduced its Intelligent Data Platform in 2022, integrating database management and analytics within Azure cloud infrastructure. The system processes data in real-time through three components: Azure Synapse Analytics for large-scale processing, Power BI for visualisation, and Azure Purview for compliance monitoring. The platform operates across multiple computing environments, enabling organisations to meet regional data storage requirements. It combines transaction processing with analytics capabilities, reducing the time between data collection and analysis while maintaining regulatory compliance through access tracking and usage monitoring.
6. Cloudera
- Revenue: US$1bn+
- Employees:3,000
- CEO: Charles Sansbury
- Founded: 2008
Cloudera is a leading technology company specializing in big data analytics and cloud-based solutions. Founded in 2008, it offers a comprehensive enterprise data platform that enables organisations to manage, process and analyse vast amounts of complex data. Cloudera’s flagship product, the Cloudera Data Platform (CDP), provides a unified suite of services for data warehousing, machine learning, and streaming analytics. The company serves various industries, including finance, healthcare and telecommunications. In 2021, Cloudera transitioned to a private company following acquisition by Clayton, Dubilier & Rice and KKR.
5. Apache Spark
- Creator & CTO: Matei Zaharia
- Founded: 2009
The University of California Berkeley developed Apache Spark in 2009 to address Hadoop MapReduce's data processing limitations. The Apache Software Foundation manages this open-source platform, which processes data in computer memory rather than on disk drives. Spark introduced Resilient Distributed Datasets for maintaining data integrity during hardware failures and supports multiple programming languages including Java, Python and R. Technology firms contribute to development: Adobe enhances image processing, Netflix develops streaming features, and AWS provides cloud integration. The platform includes SQL operations, machine learning and stream processing capabilities.
4. IBM Db2
- Revenue: US$62bn (FY2023)
- Employees: 282,000
- CEO: Arvind Krishna
- Founded: 1911
With roots tracing back to the 1970s, IBM's Db2 database platform manages structured and unstructured data, including JSON and XML formats. The software implements machine learning for query pattern analysis and automated optimisation. Db2 maintains redundant data copies across locations. Organisations deploy the platform across corporate data centres and cloud environments, enabling hybrid configurations where sensitive data remains on-premises while other information processes in cloud systems. The system serves use cases requiring immediate data access, including banking transactions and manufacturing operations.
3. Google BigQuery
- Revenue: US$305.63bn (FY2023)
- Employees: 182,502
- CEO: Sundar Pichai
- Founded: 1998
Google's BigQuery, launched by Alphabet Inc in 2010, processes datasets without dedicated servers through a separation of storage and computing functions. The system processes one petabyte - equivalent to one million gigabytes - of data within minutes through columnar storage and distributed computing.
Users interact with BigQuery through SQL (Structured Query Language), the standard database programming language. The platform incorporates machine learning capabilities for pattern identification in datasets and processes information in real-time, enabling immediate data analysis.
2. Snowflake
- Revenue: US$$2.806bn (FY2024)
- Employees: 6,780
- CEO: Sridhar Ramaswamy
- Founded: 2012
Snowflake Inc, a cloud computing company founded in 2012, offers database software that runs on cloud infrastructure from Amazon, Microsoft and Google. Its platform stores and processes data for corporate clients.
The software divides its operations into three segments. Storage systems hold the data. Computing systems process queries. Cloud services manage security and access.
Users do not manage the underlying infrastructure. The platform automates database maintenance tasks and query optimisation, and processes structured data, such as spreadsheets, and unstructured data, such as images.
The system adds or reduces computing power based on usage. When demand increases, it activates additional processing units. When demand decreases, it deactivates them. Users pay for actual consumption.
The company operates a digital marketplace where organisations purchase access to third-party datasets, where sellers determine pricing and usage terms and buyers connect purchased data directly to their existing databases.
1. Amazon Redshift
- Revenue: US$575bn (FY2023)
- Employees: 1,521,000
- CEO: Andy Jassy
- Founded: 1994
Amazon Redshift, a data storage system from Amazon Web Services (AWS), is a fast, fully managed cloud data warehouse that makes it simple and cost-effective for organisations to analyse all their data.
The service enables organisations to analyse massive datasets, ranging from gigabytes to petabytes, using standard SQL queries.
One feature of Redshift is its ability to deliver exceptional performance through parallel processing and columnar data storage, achieving up to 3x better price performance compared to other cloud data warehouses. The platform integrates with AWS' wider ecosystem, particularly with Amazon S3 data lakes and SageMaker Lakehouse.
Recent innovations include Redshift Serverless, which automatically handles infrastructure scaling, and zero-ETL integrations that enable real-time analytics without complex data pipelines. The service also leverages AI capabilities through Amazon Q for simplified SQL query writing and connects with Amazon Bedrock for enhanced generative AI applications.
Today, tens of thousands of customers rely on Redshift for their modern data analytics needs.
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