What is streaming analytics?

Also known as stream analytics, streaming analytics is the data analytics layer of event-driven architecture, which is the software design model that enables real-time responses to changes in a system. It helps enterprises pull insights from large-scale, real-time data flows to support time-sensitive data-driven decision-making, such as fraud detection and personalized marketing.

Streaming analytics is also key to faster, more performant AI, which benefits from up-to-date, context-rich data and insights. In fact, according to 2026 data from IDC, 96% of enterprises are currently using or planning to use streaming data for AI and analytics.¹

Streaming data stems from a variety of data sources, such as financial transactions, social media and Internet of Things (IoT) ecosystems. Although implementing streaming analytics to glean data insights can be more challenging than using traditional batch analytics, the right data engineering tools—including data streaming platforms such as Confluent—can help enterprises leverage streaming analytics successfully, delivering desired outcomes quickly and efficiently.

If data, as the adage goes, is the new oil, then streaming data is the high-octane ethanol fueling race cars: It’s delivered quickly to support fast, competitive performance.

In the not-so-distant past, data analysis primarily entailed batch processing of datasets. Tasks were gathered during certain intervals and eventually run in groups at specific times. While batch processing can help enterprises optimize resource use—overnight processing, for instance, may be preferable to processing during busy daytime periods—it can’t deliver the most up-to-date insights because the data is already hours or days old at the time of analysis.

As sources of real-time data proliferate—consider the growing array of social media feeds, expanding networks of IoT sensors, and rising number of SaaS applications—the inability to quickly process and derive intelligence from them can hamstring enterprises’ competitiveness and growth. In fact, research from IDC finds that 63% of use cases must process data within minutes to be useful.

Enter streaming analytics.

The speed of streaming analytics—and the insights it yields—allows businesses to move faster on key functions. It accelerates fraud detection, makes customer personalization immediate, allows more agility in supply chain management and enables inventory optimization. As such, industries such as retail, manufacturing, healthcare, financial services and more have incorporated streaming analytics into everyday business practices.

End-to-end streaming analytics can be broken down into four components:

• Data ingestion
• Data processing and analysis
• Governance
• Consumption

A data streaming platform (DSP) captures real-time events by using connectors to ingest large, continuous data streams from various sources, including:

• Clickstreams
• Financial transactions
• IoT devices
• Logs
• Microservices
• SaaS applications
• Social media feeds

In addition, DSPs can ingest database changes through change data capture.

Apache Kafka is the most widely used open source platform for the ingestion of streaming data.

In preparation for streaming analytics, ingested dataflows can be written to open table formats, such as Apache Iceberg or Delta Lake.

In stream processing (sometimes referred to as real-time data processing or event stream processing), large volumes of data are filtered, enriched, transformed, joined, aggregated and analyzed as they arrive in continuous flows. Analysis methods can include complex event processing (CEP), which can detect meaningful patterns and relationships across multiple event streams.

As data in motion is processed and analyzed, it can also be governed. For instance, lineage tracking can record data provenance while access controls can mask sensitive fields to support compliance with data privacy regulations.

Real-time analysis of streaming data can be used for real-time data visualization and business intelligence dashboards that support immediate action on time-sensitive issues. It can also be ported to data warehouses and combined with historical data to support additional analysis, including pattern detection powered by machine learning models, for decision-making in the longer term.

While streaming data is critical to many AI use cases, streaming analytics and AI have what could be described as a symbiotic relationship. AI and real-time AI inferencing are often integrated into streaming analytics processes. In turn, the resulting real-time data insights and contextualized data can be fed into AI models for time-sensitive forecasting and decision-making.

Many AI agents rely on streaming analytics to inform autonomous decision-making, while generative AI models can use streaming analytics to produce more accurate, up-to-date outputs. For example, streaming analytics help make it possible for customer service chatbots to provide instant, relevant answers to users based on the latest information about the user and other important, time-sensitive context.

While streaming analytics is often used interchangeably with the term real-time analytics, there is a subtle, semantic difference between the two terms.

Streaming analytics refers to the process of deriving value from data sources through the continuous ingestion, transformation and analysis of streaming data. Real-time analytics focuses more on the result: insights delivered with minimal latency, sometimes within milliseconds.

Additionally, depending on what source you consult, real-time analytics can encompass more than just the analysis of real-time streaming data. Some would consider it inclusive micro-batching, the process of ingesting data in small, frequent batches, providing near real-time updates.

Streaming analytics delivers a host of benefits to enterprises:

• Accelerated decision-making: Analyzing data as it’s generated means users have faster access to insights for informed decisions.

• Easier scalability: Stream processing and streaming analytics can handle large volumes of data, helping businesses—especially those in big data environments—accommodate growing workloads.

• Improved customer experiences: Real-time insights on customer behavior and preferences through streaming analytics can help enterprises provide tailored services and offerings.

• More performant AI: Streaming analytics can be the backbone of automation and AI initiatives, including AI systems for fraud detection, predictive maintenance and recommendation engines.

• Greater operational efficiency: Analysis of streaming data can enable real-time monitoring of various systems and supply chains, supporting proactive maintenance and process optimization.

• Better security: The real-time monitoring enabled by streaming analytics can also be applied in the detection and mitigation of cyberthreats and security breaches.

Enterprises rely on streaming analytics for business practices such as:

High-performance streaming analytics can be more complicated to implement in comparison to batch analytics. Common considerations include:

• Data integration: Different formats, different data sources—the diversity of streaming data can make it difficult to ingest and integrate.

• Data quality: Integrating diverse data is only half the battle—for data to be actionable, it should be clean, accurate and consistent.

• Governance: Weak or missing data governance can render streaming data difficult to trace and untrustworthy.

• Scalability: The large scale of incoming dataflows demands infrastructure that is elastic and scalable.

• Speed: Though streaming analytics is known for its speed, delivering insights in seconds or milliseconds is only possible with the right processing frameworks and resources.

Fortunately, with the right technology solutions, enterprises can overcome these challenges and implement streaming analytics in a variety of use cases.

Enterprises can choose among a variety of open source or commercial technologies for streaming data and streaming analytics.

• Apache Kafka: The open source data streaming platform Apache Kafka is known for its high-throughput, fault-tolerant infrastructure that can handle millions of incoming events per second.

• Confluent: Confluent, an IBM company, offers a cloud-native enterprise-grade solution that is built on Kafka and offers more than 120 pre-built connectors. Ingested dataflows are written to open table formats such as Apache Iceberg or Delta Lake to support analytics.

• Amazon Kinesis from Amazon Web Services (AWS): A cloud-native service for real-time data streaming and ingestion for workloads in the AWS stack.

Stream processing tools and streaming analytics platforms are integral to streaming analytics.

Analytics tools that integrate with Apache Kafka include:

• Apache Flink: A stream processing framework with in-memory management designed for stateful computations and CEP.

• Apache Spark Structured Streaming: A stream processing engine that supports real-time analytics alongside batch processing.

• Kafka Streams: A library for building stream processing applications directly on Apache Kafka.

• ksqlDB: A SQL-based stream processing engine from IBM Confluent that allows querying of streaming data using SQL syntax.

Cloud providers also offer analytics solutions, such as Microsoft’s Azure Stream Analytics, a serverless, SQL‑style engine for real-time insights that integrates with other Azure services.

Governance capabilities for streaming data are available in open source and proprietary solutions, including:

• Apache Atlas: An open source project for scalable metadata management and governance.

• DataHub: A metadata catalog that enables data discovery, lineage, observability and governance, available in open source and managed versions.

• Confluent Stream Governance: A suite of tools on the Confluent platform for managing schemas, tagging streams, tracking lineage and enforcing quality and access policies.

• IBM watsonx.data intelligence: An open, hybrid‑cloud platform that unifies data cataloging, governance, quality and lineage into a single metadata layer.

Processed streams ultimately need to be stored and queried to be useful. Open source solutions for storage and serving include:

• Apache Iceberg: A high-performance open source format for massive analytic tables that can be safely integrated with engines like Apache Spark and Flink.

• Delta Lake: An open source data storage format that combines Apache Parquet data files with a robust metadata log.

• Apache Hudi: An open source format designed for incremental data processing, in which small batches of data are processed frequently.