ISG Research is happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Analytic Data Platforms: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.
As enterprises strive to automate business processes and decisions using artificial
ISG Research defines analytic data platforms as data platforms that run applications to analyze the business, including decision support, business intelligence, data science and AI and machine learning (ML). Complemented by data operations and data intelligence platforms and tools, analytic data platforms play a fundamental role in enabling enterprises to generate value from accumulated data, supporting BI and data science initiatives to evaluate the business to increase efficiency, as well as identifying and responding to new business challenges and opportunities.
Analytic data platforms encompass real-time analytics data engines, data warehouses and data lakehouses as well as the increasing convergence of data warehouse, data lake and data-streaming technologies. Without analytic data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes and huge libraries of physical files to analyze business information.
At the heart of any data platform is the storage and management of a collection of related data. This is typically provided by a database management system (more commonly referred to simply as a database) that provides the data persistence, data management, data processing and data query functionality that enables access to—and interaction with—the stored data. Since the 1980s, the market for analytic data platforms has been dominated by the relational data model and relational database management systems.
More recently, data processing frameworks, such as Apache Spark, have been used to form the basis of a data platform by providing data persistence, data management, data processing and data query functionality alongside object storage. Early data lake projects—designed to store and process large volumes of raw data economically—were primarily based on Hadoop. Today, adoption of cloud computing environments has also led to the widespread use of object stores as an underlying data persistence layer for data platforms, with query engines such as Apache Spark, Apache Presto and Trino adding the data processing functionality required of a data platform.
While cloud-based object storage provides a low-cost environment for storing large volumes of data, it lacks structured data management and processing functionality to support multiple BI projects as well as data science and operational applications. Accelerating the analysis of data in data lakehouse environments is a key trend driving the analytic data platform space. The data lakehouse concept is designed to incorporate data warehousing
Migration of analytic workloads to the cloud is a significant trend in the analytic data platform sector. Most analytic data platforms were traditionally deployed on-premises. Today, enterprises are increasingly deploying analytic data platforms on cloud infrastructure or using analytic data platform functionality delivered as managed cloud services. More than one-half (58%) of participants in ISG’s Market Lens Cloud Study use the cloud for the majority of their data platforms.
Another key trend in the data platform sector is the blurring of the lines between operational and analytic workloads. While there have always been general-purpose databases that were used for both analytic and operational workloads, traditional architectures have involved the extraction, transformation and loading of data from the operational data platform into an external analytic data platform. This enables operational and analytic workloads to run concurrently without adversely impacting each other, protecting the performance of both.
The development of intelligent applications infused with contextually relevant recommendations, predictions and forecasting driven by ML, generative AI (GenAI) and agentic AI provides workloads that span traditional requirements. While this impacts the requirements for operational data platforms to support real-time analytic functionality, it does not eradicate the need for analysis of data in a separate analytic data platform to support BI and data science projects, as well as the development, training and fine-tuning of AI models. There is an ongoing need for data platforms designed specifically to support analytic workloads, with dedicated functionality for data engineering, including the development, training and tuning of ML and GenAI models. The Analytic Data Platforms Buyers Guide reflects this requirement by assessing products positioned as analytic data platforms on the ability to serve specific requirements of analytic use cases.
Separately, we have also created the Operational Data Platforms Buyers Guide, which excludes dedicated analytic functionality and data platforms. Meanwhile, the Data Platforms Buyers Guide evaluates a software provider’s ability to serve a combination of both operational and analytic workloads, taking into account the analytic processing capabilities of operational data platforms, and vice versa. Our assessments also considered whether the functionality in question was available from a software provider in a single offering or as a suite of products or cloud services.The ISG Buyers Guide™ for Analytic Data Platforms evaluates software providers and products in key areas, including data persistence, data management, data processing and data query; database administrator functionality; developer functionality; data engineering functionality; and data architect functionality. To be considered for inclusion in the Analytic Data Platforms Buyers Guide, a product must be marketed as a general-purpose data platform, database, database management system, data warehouse, data lake or data lakehouse. The primary use case for the product should be to support worker- and customer-facing analytics workloads (business intelligence, artificial intelligence or data science).
This research report evaluates the following software providers which offer products that are considered analytic data platforms as we define it: Actian, Aiven, Alibaba Cloud, AWS, Broadcom, Cloudera, Couchbase, Databricks, Dremio, EDB, Google Cloud, Huawei Cloud, IBM, Incorta, InterSystems, KX, MariaDB, Microsoft, Neo4j, OpenText, Oracle, Percona, PingCAP, Progress Software, Salesforce, SAP, SingleStore, Snowflake, Starburst, Tencent Cloud, Teradata and VAST Data.
This research-based index evaluates the full business and information technology value of analytic data platforms software offerings. We encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool.
We urge organizations to do a thorough job of evaluating analytic data platforms offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI.
The Buyers Guide for Analytic Data Platforms in 2025 finds Databricks first on the list, followed by Oracle and Google Cloud.
Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.
The Leaders in Product Experience are:
The Leaders in Customer Experience are:
The Leaders across any of the seven categories are:
Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle analytic data platforms, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another.
ISG Research has made every effort to encompass in this Buyers Guide the overall product and customer experience from our analytic data platforms blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.
You can find more details on our community as well as on our expertise in the research for this Buyers Guide.