Part 2 of CRN’s Big Data 100 takes a look at the vendors solution providers should know in the big data system and platform space.
All Systems Go
Today’s big data technology stack includes databases, data management and integration software, and data analytics tools – all critical components of an effective operational or analytical data system. But all those technologies run on the foundational systems, including hardware servers and cloud platforms, provided by some of the IT industry’s biggest companies.
As part of the CRN 2026 Big Data 100, we’ve put together the following list of big data systems and cloud platform vendors that solution providers should be familiar with.
Many of these companies are well-known names in the channel, including Dell Technologies, HPE and IBM, that build the underlying hardware and software that power big data IT including analytics and data-intensive operational applications.
In the cloud, where an increasing number of businesses are launching big data projects, cloud service giants like Amazon Web Services, Microsoft Azure, Google Cloud, Databricks and Snowflake provide the platforms for those initiatives.
And many of the long-established software giants like Microsoft, Oracle and SAP provide foundational cloud systems, databases and other supporting software for big data initiatives, in addition to offering their own portfolios of data management and data analysis software.
This week CRN is running the 2026 Big Data 100 list in a series of slideshows, organized by technology category, spotlighting vendors of business analytics software, database systems, data warehouse and data lake systems, data management and integration software, data observability tools, and big data systems and cloud platforms.
Some vendors have big data product portfolios that span multiple technology categories. They appear in the slideshow for the technology segment in which they are most prominent.
Amazon Web Services
Top Executive: Matt Garman, CEO
AWS is a major player in the big data space with its extensive lineup of big data services across a range of technology categories. The cloud giant also provides data services and tools that are critical for AI initiatives.
The company, for example, offers a broad portfolio of database services that includes the Amazon Aurora and Amazon RDS managed relational databases, the Amazon Neptune graph database, and the Amazon DynamoDB managed NoSQL database.
On the data processing, integration and analytics side, the AWS portfolio includes Amazon Athena for SQL queries of S3 data, Amazon Redshift for data warehousing, Amazon Quick Sight for AI-powered business intelligence, Amazon Kinesis for real-time streaming data and video analysis, AWS Glue for data integration, and Amazon EMR for running big data workloads such as Apache Spark.
Beyond its own big data offerings, the AWS cloud platform has become the operational IT foundation for the big data systems and services run by many businesses, organizations, IT vendors and solution providers. And a huge number of companies use the cloud giant’s data storage services to manage their data and run their operational and analytical big data and AI workloads.
Cloudera
Top Executive: Charles Sansbury, CEO
The Cloudera flagship AI and data platform offers an extensive range of data management, analytics and AI functionality and services that reach across data centers, public clouds and edge systems.
The Cloudera system provides a foundation for building data warehouses and data lakes, data engineering tools for building data pipelines, collecting data and managing data flows from any source to any destination, and managing data lineage and metadata. The platform also provides critical centralized data security and governance capabilities.
In February, Cloudera expanded its Cloudera AI Inference and Cloudera Data Warehouse with Trino services to on-premises environments. The company also announced enhanced AI and analytics capabilities within Cloudera Data Visualization that streamline AI workflows across cloud, edge and data center environments.
Just last week, Cloudera announced that it has adopted Apache Polaris, the open-source data catalog service for Apache Iceberg tables, as a key component of its data lakehouse architecture.
Databricks
Top Executive: Ali Ghodsi, CEO
Fast-growing Databricks has become one of the heavy-hitters in the big data space with its Databricks Data Intelligence Platform, a unified, cloud-based system for big data processing, analytics and AI initiatives.
The platform is built on the Apache Spark SQL processing engine and a data lakehouse architecture. It provides a collaborative workspace for data teams, analysts and developers and offers a growing number of services and capabilities such as AI and machine learning tools, data engineering and data orchestration functionality, and data governance and data catalog features.
In March, Databricks expanded into the cybersecurity arena with Lakewatch, a new agentic SIEM product that taps into the data management and AI capabilities of the Databricks Data Intelligence Platform.
In February Databricks, which remains privately held but many anticipate will go public in the near future, said it surpassed an annual run rate of $5.4 billion in the company’s fiscal 2026 fourth quarter (ended Jan. 31) after recording 65 percent year-over-year growth. The company also said its market capitalization had reached a stunning $134 billion.
Dell Technologies
Top Executive: Michael Dell, CEO
Dell Technologies is a leading provider of the servers, storage systems and other IT infrastructure systems that many businesses and organizations rely on to run their operational and analytical big data and AI workloads.
The company’s Dell PowerEdge servers, for example, are used extensively for big data analytical workloads while its Dell PowerScale OneNFS network-attached storage systems supply huge volumes of data through its Hadoop Distributed File System protocol interface.
But Dell’s offerings go beyond hardware, thanks to a number of strategic alliances. The Dell Data Lakehouse turnkey system, for example, is powered by the Dell Data Analytics Engine which incorporates the powerful Starburst distributed data query engine. Dell also has a close relationship with Cloudera where Dell PowerEdge servers and PowerScale storage run data management workloads using the Cloudera Data Platform.
Dell has continued to upgrade its data infrastructure offerings: At Dell Technologies World in May the company launched the Dell PowerStore Elite platform, which the company said triples the data storage performance and density of its prior storage offerings.
Google Cloud
Top Executive: Thomas Kurian, CEO
Google Cloud offers an expansive portfolio of big data management and analytics services on the Google Cloud Platform (GCP).
On the data analytics side Google’s lineup includes the BigQuery data warehouse and analytics engine and the Looker platform for business intelligence, data applications and embedded analytics. Gemini in BigQuery is an AI-powered assistant to help data professionals manage, query and analyze data.
Google Cloud Dataflow is a serverless service for executing data movement, including continuous ETL, for both batch and real-time streaming data. Google Cloud Managed Service for Apache Spark (previously Google Dataproc) is a fully managed service for managing distributed data processing frameworks such as Apache Spark, Hadoop and Presto. And Google Cloud Data Fusion is a native data integration service for designing and managing data pipelines for data preparation and data migration tasks.
In the database arena Google Cloud offerings span relational, non-relational and vector databases including the company’s Cloud SQL relational database and AlloyDB for PostgreSQL.
At Google Cloud Next ’26 in April the company debuted its Agentic Data Cloud, an AI-native architecture that allows data to be utilized at the speed and scale required by agentic AI systems. Other recent big data developments include new interoperability features for Apache Iceberg in BigQuery Google Cloud.
Beyond Google’s own big data offerings, many businesses and IT vendors rely on the Google Cloud Platform for running their big data applications and workloads.
HPE
Top Executive: Antonio Neri, President and CEO
HPE offers a range of servers, data storage systems and other products that form the foundation for on-premises, private cloud and public cloud big data operations.
At the center of the company’s big data offerings is the HPE Data Platform, a unified data management and infrastructure architecture built on the Nvidia AI Data Platform reference design. The system includes HPE Data Fabric, the HPE GreenLake for Big Data platform, and infrastructure hardware including the HPE ProLiant Compute Gen12 and HPE Alletra Storage MP.
The company also provides HPE Ezmeral Unified Analytics, a software portfolio for analytical workloads and other data-driven initiatives, that includes analytics, data management, machine learning operations and container orchestration capabilities.
In May, HPE launched its fourth-generation private cloud with a unified data platform that the company said will accelerate enterprise modernization initiatives and AI data readiness. The expanded unified data layer includes new native file storage, additional scale-out block storage, and agentic AI management.
IBM
Top Executive: Arvind Krishna, CEO
IBM provides an extensive portfolio of hardware and software products that span the Big Data sector.
IBM watsonx.data, for example, is the company’s hybrid-cloud data lakehouse platform optimized for governed data, analytics and AI workloads. Db2 is IBM’s venerable relational database while Db2 Big SQL is a massively parallel processing database for querying disparate sources.
IBM Netezza data warehouse and analytics appliances combine accelerated hardware with database analytics software. DataStage is IBM’s offering for ETL/ELT data integration and transformation. And for analytics at the top of the Big Data stack IBM offers IBM Cognos Analytics and IBM SPSS Statistics tools.
IBM recently completed its $11 billion purchase of real-time/streaming data technology developer Confluent. IBM said the combination of the Confluent platform and its own technology portfolio creates a “smart data platform” that “gives every AI model, agent and automated workflow the real-time, trusted data needed to operate across on-premises and hybrid cloud environments at scale.”
Confluent, for example, can stream live data into watsonx.data, IBM’s hybrid-cloud data lakehouse platform, to provide data for AI models, agents and workflows with data lineage, policy enforcement and quality controls.
Microsoft
Top Executive: Satya Nadella, CEO
Microsoft is one of the leading cloud hyperscalers with its Microsoft Azure cloud computing services, which serves as the big data platform for many customers, IT vendors and solution providers.
The software giant also offers a broad range of its own big data management and analytics software – both on Azure and as separate products. And Microsoft has been aggressively infusing AI capabilities into those products.
Microsoft Fabric is the company’s end-to-end, cloud-based analytics platform that unifies an organization’s entire data stack into a single environment including data engineering, data integration, data science, data warehousing, real-time intelligence and business intelligence. Fabric incorporated capabilities from earlier products including Azure Data Factory and Azure Synapse Analytics.
A key component of Microsoft Fabric is OneLake, a centralized data repository that allows data teams and business users to store, engineer, model and visualize data without the need to move it between different systems.
Two Microsoft products are long-time mainstays in the big data world: Microsoft SQL Server is one of the most widely used relational database systems and the PowerBI data visualization and interactive dashboard software remains a popular reporting tool.
Oracle
Top Executive: Clay Magouyrk and Mike Sicilia, Co-CEOs
Oracle’s flagship relational database is a core component of the operational and analytical IT systems within thousands of businesses and organizations around the world. The current edition, Oracle AI Database 26ai, was released in October 2025 with a focus on natively integrating AI into enterprise data operations and improving developer productivity.
In March the company debuted agentic AI capabilities for the Oracle AI Database that the company said help customers rapidly build, deploy and scale agentic AI applications that tap into business data across operational databases and analytic lakehouses.
The Oracle AI Data Platform is a unified cloud service with tools used to ingest, prepare and govern all types of data for AI, machine learning and business intelligence applications. Core components include the AI Data Platform Workbench, a collaborative space that brings together data scientists, engineers and analysts; and the AI Data Catalog that unifies private structured and unstructured data without moving it.
Other offerings in the Oracle portfolio include the Oracle Autonomous Database, a fully managed cloud database service, the MySQL open-source relational database and the Exadata enterprise database server. Oracle Fusion Data Intelligence is the company’s analytics and AI platform for analyzing data generated by the company’s Fusion enterprise applications.
Snowflake
Top Executive: Sridhar Ramaswamy, CEO
Snowflake has quickly become a powerhouse player in the cloud big data space with its Snowflake AI Data Cloud platform and expanding lineup of data and AI services for storing, managing, governing and analyzing huge volumes of data.
The Snowflake platform’s core capabilities include data engineering, data analytics, AI and machine learning, data sharing and collaboration, and transactional data workloads.
At the company’s recent Snowflake Summit 2026 the company showcased advancements in its CoWork personal work agent (previously Snowflake Intelligence) and its CoCo artificial intelligence coding agent (previously Cortex Code).
In May Snowflake inked a strategic collaboration agreement (SCA) with Amazon Web Services through which Snowflake is making a $6 billion, multi-year infrastructure commitment to AWS. Snowflake already runs its data and AI services on AWS, but the company said this is its largest commitment to date to AWS.
At the same time Snowflake said it had signed a definitive agreement to acquire Natoma, developer of an enterprise Model Context Protocol (MCP) platform for AI agents, in a bid to extend “Snowflake’s governance perimeter from data assets to AI actions and interactions across the enterprise.”
