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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 Streaming Analytics: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.
In theory, data-driven enterprises stand to gain a competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and 
personalized experiences. As all enterprises strive to be data-driven, however, making a higher proportion of decisions based on data is no longer enough to differentiate. The winners will be those that process and act upon data at the speed of business, analyzing and making decisions based on data generated by business events in real time.
ISG Research defines streaming analytics as the use of technology to analyze continuously generated streams of event-based messages to respond to opportunities or threats with timely actions. Despite the importance of data-driven decision-making, most enterprise analytics involve reports and dashboards created hours, days, weeks or even months after business events occur. Less than one-quarter (22%) of enterprises participating in the ISG Research Analytics and Data Benchmark Research currently analyze data in real time.
The processing and analysis of data in real time has long been seen as critical in industry segments with the most extreme high-performance requirements, such as financial services and telecommunications. In other industries, the historical reliance on batch data processing is so entrenched that processing data in real time has primarily been seen as a niche requirement. Despite the overwhelming dependence on batch data processing and analytics, it is an artificial construct driven by the historical limitations of computing capabilities to generate and process data at the same time without impacting performance.
Attitudes towards real-time analytics are changing as an increasing number of enterprises recognize that failing to process and analyze data in real time runs the risk of failing to operate at the pace of the real world. The pressure on enterprises to improve the ability to process and analyze data in real time is exacerbated by increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. AI-driven intelligent applications require a new approach to data processing that enables real-time performance of machine learning (ML) on operational data to deliver instant, relevant information for accelerated decision-making.
Enterprises can differentiate user experiences with real-time, AI-driven functionality. Doing so requires AI models with access to current data via streams of events generated in real time and the ability to incorporate model inferencing into streaming analytics pipelines. Enterprises with an over-reliance on batch data processing and analytics will not be able to match those that can act on real-time data as it is generated.
Messaging and event processing capabilities are a prerequisite for streaming analytics, alongside data processing engine functionality to apply various processing approaches to a continuous stream of event-based messages. Many of these processing approaches are the same as those applied to batch data processing, including data enrichment, data transformation and data filtering.
The analysis of streaming data is particularly reliant on data filtering as it can separate the signal from the noise—identifying data outside of expected boundaries and ensuring that processing power is applied only to the most important data. Windowing can also be applied to the continuous flow of event data to enable the stream to be divided into time-based chunks to assist in identifying patterns and anomalies.
The processing of streaming data may also involve the unification of streams from multiple data sources. In its simplest form, this unification results in data from various streams summarized in unison. More advanced cases involve data from multiple sources being joined and integrated into a combined stream.
The processing of streaming data forms the basis of streaming analytics, which uses streaming compute engines to analyze streams of event data. Key capabilities for streaming analytics include support for analytics functionality that is already prevalent in batch-based analytics, including standard SQL or “SQL-like” query languages, functions, materialized views, stored procedures and user-defined functions.
The importance of time as a factor in streaming analytics also accentuates the criticality of several capabilities, including timestamping, temporal joins and temporal analytics functions, as well as conditional rules, pattern matching and anomaly detection. Similarly, while geospatial and spatial visualization are by no means uniquely important to streaming analytics, their criticality is accentuated given the use of streaming analytics to support Internet of Things use cases that rely on real-time processing and analysis of location and environmental data.
More traditional, chart-based visualization of streaming data is also a key capability, including functionality to enable the creation of specialist streaming analytics dashboards as well as integration with widely adopted business intelligence and data science tools. 
While BI dashboard providers have traditionally focused more on the visualization of batch data, support for streaming data sources is becoming more prevalent. ISG asserts that by 2027, more than 3 in 5 enterprises will incorporate streaming analytics into business processes, enabling faster response to opportunities and threats.
Query management capabilities are equally essential for streaming analytics as batch-based analytics. This includes functionality for creating and testing individual queries and analytics pipelines, as well as monitoring the execution and performance of analytics jobs and the isolation, prioritization, optimization and scheduling of analytics workloads.
Support for AI is also increasingly essential for streaming analytics, given the widespread focus on developing AI-driven intelligent applications. Key capabilities include native functionality for ML scoring and ML predictions, as well as retrieval augmented generation, reinforcement learning and agentic AI. Streaming analytics products also need to deliver support for integration with external AI/ML models and services as well as MLOps tools and platforms to ensure compatibility with broader strategic AI initiatives.
The ISG Buyers Guide™ for Streaming Analytics evaluates products based on core capabilities such as stream processing, analytics, query management and AI. To be included in this Buyers Guide, products must include functionality for stream processing, analytics, query management and AI. Our assessment 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.
This research evaluates the following software providers that offer products that address key elements of streaming analytics as we define it: Actian, Aiven, Alibaba Cloud, Altair, AWS, Cloud Software Group, Cloudera, Confluent, Cumulocity, Databricks, Google Cloud, GridGain, Hazelcast, Huawei Cloud, IBM, Materialize, Microsoft, Oracle, Palantir, Qubole, SAS, and Striim.
This research-based index evaluates the full business and information technology value of streaming analytics 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 streaming analytics 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 Streaming Analytics in 2025 finds Databricks first on the list, followed by Oracle and Microsoft.
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:
- Databricks.
- Microsoft.
- Oracle.
The Leaders in Customer Experience are:
- Databricks.
- Oracle.
- Actian.
The Leaders across any of the seven categories are:
- Oracle, which has achieved this rating in six of the seven categories.
- Databricks in five categories.
- Google Cloud in three categories.
- Actian and Microsoft in two categories.
- Cloud Software Group, Cumulocity and Palantir in one category.
The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating.
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 streaming analytics, 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 streaming analytics 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.
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