Market Perspectives

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 Data: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.

Messaging and event processing enables enterprises to capture information related to business events and communicate it to any dependent application, device or system. Although messaging and event processing can trigger specific actions in response to events, the ability of an enterprise to make real-time business decisions is enhanced through the processing of streaming data as it is communicated.

Messaging generated by business events can be published sequentially as message queues or a continuous flow of event messages (or an event stream). ISG Research defines streaming data as the processing and management of continuously generated streams of event-based messages. By processing streaming data in real time, enterprises can refine and enhance streaming data and combine data from multiple event streams. Processing streaming data enables enterprises to act on the event data as it is communicated and forms the basis of streaming analytics.

Almost one-half (48%) of enterprises participating in the ISG Research Analytics and Data Benchmark Research currently use streaming data in operational processes, slightly ahead of the 44% that use streaming data in analytic processes.

Processing streaming data has been part of the data and analytics landscape for decades. Until recently, it has typically been adopted in industry segments with the most extreme high-performance requirements, such as financial services and telecommunications. In other industries, the processing of streaming data is primarily seen as a niche requirement, separate from the default focus on batch processing of data at rest.

The fundamental basis of processing streaming data is the ability to ingest a stream of events into a data processing engine. A variety of processing approaches are applied to the streaming data. Many of these processing approaches are the same as those applied to batch data processing, including data enrichment, data transformation and data filtering. The latter is particularly important for processing streaming data as it is used to sift the flow of events so that processing power is applied only to data outside of expected boundaries and, therefore, worthy of processing and analysis. Windowing can also be applied to the continuous flow of event data to divide the stream into time-based chunks to assist in identifying patterns and anomalies.

In addition to processing data from individual sources, streaming data may also involve unifying streams from multiple data sources. In its simplest form, this unification can result in data from multiple streams summarized in unison. More advanced cases involve data from various sources being joined and integrated into a combined stream.

Simple processing approaches such as filtering and basic transformations can be applied in a stateless stream processing environment where each event is processed independently of the others. More complex approaches, such as aggregations and joins, require stateful stream processing that retains the context of previously processed events, and stream processing guarantees that ensure messages are processed even if there are system or performance failures.

Processing and analyzing event data in isolation is valuable. Success with streaming data relies on the holistic management and governance of data in motion and data at rest. Integration with more traditional batch data processing technologies is, therefore, important to streaming data. This includes stream-table duality to maintain compatibility with database tables, the ability to materialize streaming data into an external database or data storage for long-term persistence and the analysis of real-time data streams alongside batches of historical event data.

Much like batch data processing pipelines, creating streaming data processing pipelines requires code-based development tools and low- or no-code visual development environments. Streaming data pipelines also need to be managed and monitored to ensure they are performing as intended.

Stream management involves monitoring stream processing performance metrics via reports, dashboards and alerts, as well as capabilities to manage the scalability and fault tolerance of streaming processor technologies. Integration with external monitoring and observability tools is also important to ensure that stream processing infrastructure is not monitored and managed in isolation.

Governance of streaming data is an important capability that can be seen as critical as enterprises become increasingly reliant on streaming data. As is the case with batch data, functionality for monitoring and managing data quality and data lineage is required to maintain trust in streaming data.

Data quality capabilities include the ability to define, monitor and enforce data quality rules. Data lineage functionality enables enterprises to keep track of where event data originated from, as well as when and how it has been transformed or changed and by whom.

The ability for users to discover data streams on a self-service basis encourages the use of event data and the development of event-driven applications. However, it depends on a catalog of event streams as well as access management capabilities and the tagging of both technical and business metadata. Additionally, schema management and a registry of streaming data schema are essential to verifying and keeping track of the rules that define the structure of streaming data.

Emerging requirements for processing streaming data include the ability to incorporate machine learning into streaming data pipelines and AI model inference. This includes the ability to make calls to external artificial intelligence services to coordinate data processing and AI workflows and ensure that AI models have access to streaming data as it is updated in real time. ISG asserts that through 2027, streaming and event software providers will accelerate integration with AI and GenAI services to facilitate the development of interactive real-time applications.

Processing streaming data also forms the basis of streaming analytics, which uses streaming compute engines to analyze streams of event data using SQL queries and real-time materialized views. This includes chart-based visualization of streaming data and additional support for native ML and GenAI model inferencing, as well as more advanced capabilities such as retrieval augmented generation.

The ISG Buyers Guide™ for Streaming Data evaluates products based on core capabilities such as event streaming, stream processing, stream management and stream governance. To be included in this Buyers Guide, products must offer functionality for event streaming, stream processing, stream management and stream governance. 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 data as we define it: Actian, Aiven, Alibaba Cloud, Altair, AWS, Cloud Software Group, Cloudera, Confluent, Databricks, Google Cloud, Cumulocity, GridGain, Hazelcast, Huawei Cloud, IBM, Informatica, Kurrent, Materialize, Microsoft, MongoDB, Palantir, Qubole, Redpanda, SAS, Solace, Striim and Tencent Cloud.

This research-based index evaluates the full business and information technology value of streaming data 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 data 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 Data in 2025 finds Databricks first on the list, followed by AWS 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.
• AWS.

The Leaders in Customer Experience are:

• Databricks.
• Informatica.
• Solace.

The Leaders across any of the seven categories are:

• Informatica, which has achieved this rating in six of the seven categories.
• Databricks in four categories.
• Google Cloud in three categories.
• Actian and Microsoft in two categories.
• Cloudera, Confluent, Cumulocity, Solace and Striim 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 data, 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 data 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.