What Is Data Streaming? Benefits, Examples, and How It Works in 2026

Data streaming enables organizations to process information continuously as it's generated, powering everything from fraud detection to personalized recommendations. Unlike batch processing that handles data in scheduled chunks, streaming delivers insights in seconds rather than hours. This article covers how streaming architecture works, common use cases across industries, and the key factors that determine whether streaming fits your needs.

Key takeaways

• Data streaming is the continuous flow of data from sources to your platform as it's created, enabling analysis in seconds rather than hours or days
• Unlike batch processing, streaming handles high-velocity data continuously, making it essential for time-sensitive decisions and AI-powered analytics
• Common use cases span e-commerce personalization, healthcare monitoring, fraud detection, IoT device management, and supply chain optimization
• Successful implementation requires attention to scalability, data accuracy, fault tolerance, security, and governance
• Modern streaming platforms integrate with AI and machine learning to turn continuous data flows into automated, intelligent actions

Fresh data wins. That's the realization driving organizations away from traditional batch processing and toward data streaming.

Data streaming allows you to access information in real time as it's generated from all your data systems. Gartner predicts that adoption of data streaming is expected to grow past 60 percent by 2028, driven by the disruptive pressure for real-time responsiveness and the rise of agentic AI. That growth rate signals a fundamental shift in how enterprises think about data freshness. Organizations that wait for nightly batch jobs increasingly find themselves a step behind competitors acting on current information.

Streaming data provides you with business and customer intelligence so you can enhance your daily operations, stay on top of emerging trends, or find new opportunities. Learn how it works, its benefits, and what aspects to consider when using a data streaming platform.

What is data streaming?

Data streaming is the continuous flow and transfer of data from all your sources into your data platform as it's created. It includes data generated at high speeds or volumes, simultaneously collecting data from all your applications, websites, social media channels, financial and customer transactions, networks and servers, Internet of Things (IoT) devices, business platforms, and more.

Your platform collects, stores, processes, and analyzes data in real time, so you constantly see the most up-to-date information. Cloud computing, internet-connected devices, and advanced analytics platforms make this possible.

Streaming data is replacing batch processing of business data, eliminating the need to download your data and reducing the time between data collection and analysis. It can handle numerous data types, changing volumes of data, and high-velocity data without affecting latency.

Terminology: data streaming vs streaming data vs event streaming

These terms often appear interchangeably, but each carries a slightly different emphasis. Data streaming refers to the overall process and architecture of moving data continuously from sources to destinations. Streaming data describes the data itself (the continuous flow of records or events being transmitted). Event streaming is an architectural pattern where data is treated as a sequence of immutable events, each representing something that happened at a specific point in time.

Most organizations use these terms loosely in practice. When evaluating tools or designing systems, the distinctions matter less than understanding that all three describe continuous data movement rather than periodic batch transfers.

Key characteristics of streaming data

Streaming data has distinct properties that set it apart from traditional data processing approaches. Understanding these characteristics helps you design systems that can handle continuous data flows effectively.

The following traits define streaming data:

• Continuous flow: Data arrives as an unending sequence of events rather than finite datasets, requiring systems that can process indefinitely without stopping
• Chronological significance: The order and timing of data points matter, as events often need to be processed in sequence to maintain accuracy
• Heterogeneous sources: Streams typically combine data from multiple systems, formats, and protocols into a unified flow
• High velocity: Data arrives rapidly, sometimes thousands or millions of events per second, demanding low-latency processing
• Imperfect and incomplete nature: Unlike batch data that can be cleaned before processing, streaming data may arrive out of order, contain duplicates, or have gaps that systems must handle gracefully

How data streaming works

Just about every action across your systems generates data. Digital marketing campaigns, IoT sensors, business servers, people using apps and visiting websites. The list keeps growing. For your business to make use of this data, you need the right streaming infrastructure that can ingest and process high-volume and high-velocity data at a continuous pace.

Modern data streaming architecture follows a producer-broker-consumer pattern that separates data generation from processing and delivery. This separation allows each component to scale independently and makes the overall system more resilient.

Data ingestion and stream producers

Producers are any systems or applications that generate data and send it into the stream. These include mobile apps tracking user interactions, IoT sensors monitoring equipment performance, web servers logging page visits, payment systems recording transactions, and social media platforms capturing engagement metrics.

Each producer publishes events to the streaming platform as they occur. The data might be structured (like database records), semi-structured (like JavaScript Object Notation (JSON) logs), or unstructured (like text from customer support chats). A well-designed streaming system can accept data from thousands of producers simultaneously without creating bottlenecks.

Stream processing and brokers

Message brokers sit at the center of the streaming architecture, receiving data from producers and making it available to consumers. They handle the complexity of managing data flow, ensuring messages are delivered reliably, and maintaining the order of events when necessary.

Stream processing happens either within the broker or in dedicated processing engines that consume data from the broker. Processing can include filtering irrelevant events, enriching data with additional context, aggregating metrics over time windows, detecting patterns or anomalies, and transforming data into formats needed by downstream systems.

Storage and delivery

The storage layer needs to quickly and consistently read and record large data streams in sequential order. Modern streaming platforms often maintain data in distributed logs that can be replayed if needed, providing durability without sacrificing speed.

Once processing completes, data flows to its final destinations. This might be a data warehouse for long-term storage, an analytics dashboard for visualization, an AI model for predictions, or an application that triggers automated actions. The same processed stream can feed multiple destinations simultaneously, allowing different teams to use the data for their specific needs.

Legacy systems and databases often can't keep up with modern, complex, fast-moving data streams. Instead, companies are turning to platforms that can consolidate all streaming data sources and help build fast, scalable processing layers. From there, you can query your data or feed it into real-time analytics and BI tools to create easy-to-understand data visualizations and actionable reports.

A concrete example: clickstream to personalization

Consider how an e-commerce site uses streaming to personalize product recommendations. When a visitor clicks on a product page, the web application (producer) emits an event containing the user ID, product ID, timestamp, and session context as a JSON message.

This event flows to a message broker like Apache Kafka, which partitions events by user ID so all activity from one visitor stays together. A stream processing engine consumes these events, joins them with the user's purchase history from a feature store, and computes a rolling window of recently viewed categories.

The enriched data feeds an inference service running a recommendation model, which scores relevant products in milliseconds. The results flow back to the web application, updating the "You might also like" section before the visitor scrolls down the page. The entire journey (from click to updated recommendation) happens in under 200 milliseconds.

Data streaming vs batch processing

What is data streaming in comparison to batch processing of data? Below, you'll see how the two processes differ and learn which works best for your business applications.

When to use each approach

The choice between streaming and batch processing depends on your specific requirements rather than one approach being universally better.

Streaming makes sense when decisions need to happen in seconds or minutes, when data loses value quickly, or when you need to detect and respond to events as they occur. Fraud detection, live personalization, operational monitoring, and IoT device management all benefit from streaming.

Batch processing remains the right choice for historical analysis, complex transformations that require seeing the complete dataset, regulatory reporting with fixed deadlines, and workloads where processing cost matters more than speed. Many organizations run nightly batch jobs to generate reports, train machine learning models, or reconcile data across systems.

Near-real-time processing offers a middle ground, processing data in micro-batches every few seconds or minutes. This approach works well when you need fresher data than daily batches provide but don't require true millisecond latency.

Most mature data architectures use both approaches.

Decision criteria for streaming vs batch

When evaluating whether streaming is worth the added complexity, consider these factors:

• Latency requirements: If decisions must happen in under a minute, streaming is likely necessary. If hourly or daily freshness suffices, batch may be simpler and cheaper.
• Data volatility: Rapidly changing data (stock prices, user sessions, sensor readings) benefits from streaming. Slowly changing data (monthly reports, annual forecasts) works fine in batch.
• Reprocessing needs: Batch makes it easier to rerun jobs when logic changes. Streaming requires more careful design for replay and correction.
• Cost sensitivity: Streaming infrastructure runs continuously, incurring steady costs. Batch jobs can run on spot instances or during off-peak hours.
• Complexity tolerance: Streaming introduces challenges like ordering, duplicates, and late arrivals that batch avoids by processing complete datasets.

Types of streaming data

Event streams capture actions people take, system events, and application logs (discrete occurrences with timestamps such as clicks, purchases, logins, or error messages). IoT and sensor data consists of continuous readings from connected devices including temperature sensors, GPS trackers, industrial equipment monitors, and wearable health devices. Transaction data covers financial transactions, order processing, inventory updates, and other business operations that require immediate recording and often trigger downstream actions. Log and telemetry data encompasses system performance metrics, application traces, and infrastructure monitoring data used for observability and troubleshooting. Social and engagement data includes real-time feeds from social platforms, customer interactions, and communication channels that inform marketing and customer experience decisions.

Understanding which types of streaming data your organization generates helps you design appropriate ingestion, processing, and storage strategies for each.

Benefits of data streaming

The greatest benefit of streaming data is being able to understand business operations and customer activities at a deeper level and in real time. Unlike traditional data processing, where information has to be extracted, transformed, and loaded from data pipelines before it can be analyzed and acted upon, data streaming processes new data points as they are generated.

Data streaming platforms give you quicker access to information so your company can become more agile and respond to changes faster. You'll also see:

• Reduced costs: Data streaming infrastructure is often less expensive than legacy and batch processing systems since you don't need to utilize on-premise storage or data warehouses to store large volumes of data for processing purposes
• Improved visibility and operational efficiency: You can monitor operations across your business in real time, allowing you to find ways to improve marketing, sales, and financial performance for improved business outcomes
• Increased customer satisfaction: Streaming data enables you to respond to changing customer interests or market fluctuations immediately and provide a more personalized experience, improving overall customer satisfaction
• Competitive advantage: Organizations that act on fresh data can identify opportunities and threats before competitors relying on stale information, turning speed into a strategic differentiator

Data streaming use cases and examples

Organizations across industries use data streaming in numerous everyday applications. Here are just a few examples:

E-commerce and retail

E-commerce businesses of all sizes use data streaming platforms to analyze customer data and improve their marketing, sales, and operational strategies in real time. Customer demographics and behavioral data can help your business tailor product recommendations and marketing campaigns to better align with their interests, even if they change over time.

Data streaming can also be used to design personalized discounts or promotions based on what customers have most recently browsed on your website. Access to current data is critical for boosting engagement and converting more prospects into paying customers.

Many e-commerce companies also use data streaming to monitor and optimize their stock levels in real time. It can help you identify seasonal fluctuations and shifts in the market or consumer demand to proactively manage your inventory. That way, you can avoid being out of stock of popular items or having products you can't move. Data streaming also enables dynamic pricing, allowing you to adjust pricing to align with inventory levels, consumer demand, or the pricing and promotions of your closest competitors.

Healthcare and remote monitoring

A continuous flow of health data makes applications like telehealth and remote health monitoring possible. The streaming system combines, analyzes, and presents data from various sources, including a patient's electronic health record, research information, medical devices, and other wearables, in easy-to-understand visualizations so healthcare providers can track conditions and make informed care decisions without being in the same physical location as their patient.

Data streaming shows current trends and anomalies in health data, too, allowing physicians to detect problems earlier for better health outcomes. It's extremely useful for monitoring diabetes, heart disease, post-surgical recovery, and other chronic conditions with easily tracked metrics.

Data streaming also helps you transform your practice from a more traditional, service-centered model to personalized, value-based care. You can use patient data to tailor services or treatment plans based on an individual's needs or health goals, helping patients become more engaged with their health.

Financial services and fraud detection

Finance companies use streaming data in many applications to improve customer-facing and internal operations. Banks, lenders, and other finance organizations can monitor employee performance and discover insights from current data to reduce workflow bottlenecks and improve productivity.

Streaming platforms can also evaluate customer data, which can help you enhance your product or service offerings to better align with their interests and boost customer retention. Additionally, organizations use data streaming to assess risk and speed up credit card, loan, and mortgage application processing.

You can even use streaming data to prevent fraud in your financial institution. The platforms continuously analyze data and can alert you in real time when trends change or anomalies appear, which may indicate fraudulent transactions and help prevent losses. A streaming fraud detection system might evaluate hundreds of transaction attributes in under 50 milliseconds, blocking a suspicious payment before it completes. That speed matters because fraudulent transactions that clear become exponentially harder to reverse.

Access to real-time data streams also enables personalized investment portfolios. It uses factors such as a customer's individual risk tolerance and investment goals, along with insights from current market data, to suggest the most optimal investment and trading opportunities.

IoT and connected devices

Internet of Things (IoT) devices are programmed for specific applications and embedded in physical sensors, allowing you to collect and stream data from the source to your cloud or data processing center. They can help you track and optimize labor, machinery, inventory, transportation, and more from any location.

Retailers and supply chain management companies can use IoT devices to monitor and adjust inventory levels based on changing consumer demand or map out more efficient warehouse layouts. They're also ideal for managing equipment fleets and practicing predictive maintenance for manufacturers, logistics, and transportation companies.

You'll also see IoT data streaming in the health and wellness industry, as they enable remote patient monitoring and help practitioners and patients alike track and manage health conditions. With the number of connected devices currently estimated at nearly 20 billion globally and expected to more than double to over 40 billion by 2034, businesses across all industries will rely on data streaming to make the most of this technology. That trajectory means streaming infrastructure built today needs to handle order-of-magnitude increases in data volume within the next decade.

Logistics and supply chain

As mentioned above, logistics and supply chain businesses can use connected IoT devices and data to operate more efficiently, but the benefits of data streaming don't end there. Additionally, these companies can use customer data to see product and input demand and quickly take proactive steps to adapt to any shifts in demand by connecting with other distributors or increasing production, increasing order fulfillment and customer satisfaction.

You can also use data streaming to track traffic and fleet information to help find the most optimal routes, which in turn can reduce transportation costs and customer wait times. A holistic view of logistic and supply chain data also allows for greater transparency across the company. Data streaming can reveal new, formerly hidden opportunities across financial and operational metrics to become more agile to disruptions and changing markets.

Data streaming tools and technologies

Building a data streaming architecture requires selecting the right tools for ingestion, processing, and delivery. The ecosystem has matured significantly, with both open-source frameworks and managed cloud services available.

Stream processing frameworks

Several open-source frameworks have become industry standards for building streaming applications:

• Apache Kafka: The most widely adopted distributed streaming platform, Kafka excels at high-throughput data ingestion and serves as a durable message broker that can handle millions of events per second. Many organizations use Kafka as the backbone of their streaming architecture, though several Kafka alternatives have emerged for teams with different operational or cost requirements.
• Apache Flink: Apache Flink handles complex event processing, windowed aggregations, and exactly-once processing guarantees, but it often requires specialized expertise and operational overhead that Domo can reduce.
• Apache Spark Streaming: Apache Spark Streaming processes data in micro-batches and integrates well with Spark's machine learning and Structured Query Language (SQL) capabilities, but teams that need lower-latency streaming and less operational complexity may prefer Domo. It can work well when you need streaming alongside batch analytics, but teams that want simpler activation and distribution on governed data may prefer Domo.

Cloud streaming services

Major cloud providers offer managed streaming services that reduce operational complexity:

• Amazon Web Services (AWS) Kinesis: Amazon's suite of streaming services handles data ingestion, processing, and analytics with tight integration to other AWS services, but that same dependence on one cloud ecosystem can limit flexibility compared with Domo.
• Google Cloud Dataflow: Google Cloud Dataflow offers managed stream and batch processing with automatic scaling, but teams that want easier business-facing activation on governed data may prefer Domo.
• Azure Stream Analytics: Azure Stream Analytics connects easily to Azure IoT Hub, Event Hubs, and other Azure data services, but organizations that need broader cross-system activation and distribution may prefer Domo.

Choosing between open-source frameworks and managed services depends on your team's expertise, existing cloud investments, and how much operational overhead you're willing to manage.

Streaming data reliability and correctness

Building reliable streaming systems requires understanding the challenges that make continuous data processing harder than batch. These concepts matter because they determine whether your downstream analytics and AI models can trust the data they receive.

Delivery semantics

Streaming systems offer different guarantees about message delivery:

• At-most-once: Messages may be lost but are never duplicated. Fastest but least reliable, acceptable for metrics where occasional gaps don't matter.
• At-least-once: Messages are never lost but may be duplicated. Requires downstream systems to handle duplicates through idempotent operations.
• Exactly-once: Each message is processed once and only once. The strongest guarantee but requires coordination between producers, brokers, and consumers.

Most business-critical applications need at-least-once delivery with idempotent processing, or exactly-once when available. Fraud detection systems, for example, can't afford to miss transactions, but they also can't block the same card twice for one suspicious event. Many teams assume their streaming platform provides exactly-once semantics by default. Most require explicit configuration, and the guarantee often applies only within the broker, not end-to-end across your entire pipeline.

Handling late and out-of-order events

Events don't always arrive in the order they occurred. Network delays, system restarts, and mobile connectivity gaps mean a purchase event might arrive after the shipping event it triggered. Streaming systems address this through event time processing (using timestamps embedded in events rather than arrival time) and watermarks (signals that indicate when it's safe to assume all events up to a certain time have arrived).

Backpressure and flow control

When consumers can't keep up with producers, data backs up in the system. Without proper backpressure handling, this can cause memory exhaustion, dropped messages, or cascading failures. Well-designed streaming architectures include mechanisms for consumers to signal producers to slow down, or for brokers to buffer data temporarily during traffic spikes.

Data streaming and AI

The combination of data streaming and artificial intelligence creates opportunities that neither technology offers alone. Streaming provides the fresh data AI needs to make relevant predictions, while AI transforms raw streams into intelligent, automated actions.

Real-time data feeds enable AI models to respond to current conditions rather than historical patterns. A recommendation engine using streaming data can adjust suggestions based on what a customer browsed 30 seconds ago, not just their purchase history from last month. Fraud detection models can evaluate transactions as they happen, blocking suspicious activity before money moves.

AI agents operating on streaming data can monitor business operations continuously and take action when conditions warrant. An agent might automatically adjust ad spend when campaign performance drops, reorder inventory when stock levels hit thresholds, or escalate customer issues when sentiment analysis detects frustration.

The key to making AI work on streaming data is governance. Models need access to fresh, accurate data, but that access must be controlled. Organizations should define what data AI can access, what actions it can take autonomously, and when human oversight is required. This bounded autonomy lets AI deliver speed and scale while humans maintain control over consequential decisions.

Challenges and considerations for data streaming

Developing data streaming applications can be more challenging due to their complex nature, large volume, and high velocity. Knowing the biggest potential issues and the proactive steps you can take is key to designing a powerful and adaptive data streaming process. Here are some top factors to consider:

Scalability and infrastructure

Data streaming is a continual process with data volumes that rapidly surge and decrease at any given time. Your ingestion, processing, computational, and storage infrastructure must be operating constantly and offer the scalability your data requires. Consider using cloud-based tools and data storage, which offer the required flexibility and scalability needed for streaming data.

Data accuracy and ordering

To ensure the accuracy and reliability of your data, you need to consider the order, consistency, and durability of your streaming data. Data that gets out of sequence during processing won't make sense or be as valuable. The same is true if you're using stale data or data that has been modified in any way.

Fault tolerance and reliability

Since data streaming relies on so many separate systems and moving parts, you need to be aware of how a single point of failure within one of the components has the potential to disrupt the entire process. Consider ways to make your system more reliable or redundant so you can continue to stream data of all formats, types, and locations, even if small failures occur.

Security, privacy, and governance

Streaming data contains sensitive information about your business and its customers or clients. You need to be able to protect the integrity and privacy of your data from tampering or unauthorized access as it moves through different systems. Being aware of threats and preventing data breaches is also crucial, as they can significantly impact your finances and reputation.

In addition to security, strong governance becomes essential as streaming data feeds more systems and powers more decisions. You need clear policies about data retention, access controls, and lineage tracking. When AI agents act on streaming data, governance ensures they operate within defined boundaries and that humans can audit their actions. A secure platform with built-in governance capabilities helps organizations move fast without compromising control.

Managing latency and data volume

While continuous flows of data offer fresh insights and can help you stay ahead of the competition, the sheer volume of data can be overwhelming. It may be hard to identify the most relevant data or develop meaningful insights without a plan in place ahead of time and the right tools to interpret your data.

Choosing the right data streaming platform

You'll need to use a powerful analytics platform to ingest and process data. While there are many options to choose from, consider the following elements to find the right platform for your organization:

• Integration capabilities: Top data streaming platforms will easily integrate with all your data sources, such as cloud databases, applications, and tools. Data moves across all systems and gets ingested, processed, and analyzed without coding or any extra steps.
• Scalability and flexibility: Make sure the platform can handle diverse data formats, rapidly changing data volumes, and high-velocity data without disruption.
• Data processing capabilities: Look for data streaming platforms that offer complex event processing and advanced analytics capabilities like AI, machine learning, or predictive analytics to make the most out of your data.
• Governance and security features: Safeguarding your data's privacy and integrity is a top priority. Ensure the platform offers strong governance and security features, including access management with multifactor authentication, secure architecture, encryption, auditing, and compliance with the Health Insurance Portability and Accountability Act (HIPAA), Service Organization Control 2 (SOC 2), the General Data Protection Regulation (GDPR), or other industry standards.
• Ease of use: Low- and no-code, user-friendly interfaces and drag-and-drop features make it easier for developers and business professionals to use the platform to its full potential.
• Reliability: Consider features like replication and checkpointing to reduce the risk of losing data or disruptions and ensure data reliability if a failure occurs within your data streaming systems.
• AI and automation readiness: As organizations increasingly use streaming data to power AI agents and automated workflows, evaluate whether the platform supports these use cases with appropriate governance controls.

Finally, consider your specific use cases, volume of data, existing tech stack, and processing requirements when evaluating data streaming platforms for your business. Too many teams choose a platform based on feature lists alone, only to discover it does not play well with their existing infrastructure. The right choice will meet your specific needs and be within your budget.