What is real-time data streaming?

Real-time data streaming refers to the instant processing of data points as they arrive—often milliseconds after they’re generated. 

This low-latency approach is distinct from traditional batch processing, in which tasks are grouped together and executed during scheduled times. In contrast, through real-time data streaming, the immediate processing of “data in motion” means enterprises can access fresh, up-to-the-minute information. Real-time sources of data include:

• Internet of Things (IoT) devices and sensors
• Financial market data feeds
• Social media
• Point-of-sale and e-commerce systems
• Clickstreams (records of user behavior on a website)

Analysis of such information produces insights that can power timely decision-making and real-time applications, including agentic artificial intelligence (AI). Additional benefits of real-time data streaming include improved operational efficiency, data retention, risk management and customer personalization.

Real-time data streaming is made possible by an infrastructure consisting of an ingestion layer, a real-time processing engine and a storage and serving layer. Solutions such as open source frameworks and data streaming platforms support real-time streaming infrastructure and help enterprises efficiently manage millions of records across thousands of data pipelines.

Imagine a gushing water fountain. A thirsty passerby stops and attempts to take a few gulps, but the water flows so furiously that they can barely swallow anything at all. Most of the liquid splashes right out of their mouth, leaving puddles at their feet. To quench their thirst, they’ll need to stand there for a while—so long, in fact, that they might decide the effort isn’t worth it in the first place.

Such is the dilemma enterprises face when trying to harness the power of fast-moving streams of information—one of the most valuable sources of business intelligence today.

Attempting to capture and process that data using traditional methods is akin to the challenge facing the thirsty traveler at the out-of-control fountain: Reaching their goal, whether it’s actionable insights or adequate hydration, can be a messy process that takes a prohibitively long time.

Real-time data streaming offers enterprises a way to leverage real-time data fast, without the mess.

Through real-time data ingestion and processing, businesses can take fast-flowing, continuous data and feed it into real-time analytics systems—which then produce timely, actionable insights. Such real-time insights provide a competitive advantage in a range of industries and disciplines.

Retailers can dynamically adjust pricing based on immediate intelligence on consumer demand. Banks can analyze transaction data and perform fraud detection in real time. Manufacturers can detect machine failures and address them before significant downtime occurs.

The agility enabled by real-time data is amplified when paired with agentic AI. Agentic AI leverages real-time data to support fast, autonomous real-world decision-making, such as identifying and responding to cybersecurity threats or adjusting shipping routes during traffic delays.

Without real-time data streaming, businesses would be unable to realize these benefits. Instead, they would rely on traditional, slower forms of data ingestion and processing.

As a modern data processing solution, real-time data streaming—and managing streaming data overall—stands in contrast to the traditional data processing approach: batch processing. 

In real-time data streaming, each incoming, individual data point is processed as it enters the target system. In batch processing, organizations aggregate and analyze datasets in batches (batch data) at fixed intervals.

Batch processing can automate repeating workloads, such as the generation of routine reports. It also allows organizations to optimize resource use by slating batch jobs during convenient periods, such as overnight, when systems aren’t being heavily used otherwise.

But batch processing falls short when it comes to business needs that can’t wait for the next scheduled run. For faster turnarounds, enterprises turn to faster processes, including real-time data streaming.

Enterprises that use real-time data streaming experience many benefits, including:

Real-time data streaming is often used interchangeably with the term “event streaming” for good reason—the difference between the two is subtle.

Event streaming captures the flow of records called “events”—occurrences or changes in the system or environment—from various data sources such as applications and IoT devices, then transports it for immediate processing and then analytics or storage. Event streams typically consist of real-time data.

However, during event streaming, the filtering of data happens before its movement, significantly reducing demands on the target system. While this may prove a key benefit for some organizations, event streaming can also come with a downside: Time series analysis and signal processing (the manipulation of sensor data and other information to unlock value) are more challenging for event streaming than real-time data streaming.

This distinction notwithstanding, the solutions for real-time data streaming and event streaming are the same. The dominant data streaming platforms, such as Apache Kafka, Amazon Kinesis from Amazon Web Services (AWS) and Redpanda, are also known as event streaming platforms.

The data architecture that supports real-time data streaming is streaming architecture, with data engineering components designed to keep data moving and avoid staleness. The three basic components are:

• Ingestion
• Processing
• Destination

Various sources continuously produce and emit data points. This incoming data is often unbounded, meaning it is generated and continues flowing without a fixed endpoint. That information is captured by data ingestion tools with streaming connectors and then delivered to a processor. Application programming interfaces (APIs) can also help automate the transmission of real-time data from various sources.

In stream processing (sometimes referred to as real-time data processing), data is filtered, enriched, transformed or analyzed as it arrives. AI and machine learning can be deployed to power data analysis and discern patterns and other key insights.

The processed data is delivered to a destination for either immediate use (in an app or dashboard, for instance) or storage. Organizations often rely on data lakes and data lakehouses for the storage of streaming data because they can accommodate high volumes of data at relatively low costs. Streaming data can also be stored in data warehouses, which use ETL (extract, transform, load) processes for data transformation, organization and visualization. 

The right streaming tools and processing capabilities are critical for building real-time data streaming pipelines. These include open source streaming frameworks, cloud-based data streaming platforms and tools, and data integration solutions.

Apache Kafka, Apache Flink and Apache Spark Streaming are key open source frameworks and tools for real-time data streaming.

• Apache Kafka: A distributed platform that enables applications to publish or subscribe to data or event streams with high-throughput data delivery.

• Apache Flink: A distributed processing engine for stateful computations (maintaining data context across events) and complex event processing (detecting patterns and relationships in event streams).

• Apache Spark Streaming: Spark Streaming is an extension of the Spark data-processing engine. It provides for scalable, fault-tolerant processing of live data streams.

Open source streaming solutions can provide the foundation for real-time data streaming. However, enterprises often rely on cloud providers and specialized cloud-based platforms for additional support to manage streaming data, build streaming applications and ensure scalability.

Popular tools and platforms include Amazon Kinesis, Confluent, Microsoft Azure Stream Analytics, Google Cloud’s Dataflow and IBM Event Streams.

Different types of data processing require different types of data integration tools. Streaming data platforms include integration features, but the advent of a more-comprehensive type of integration solution can help businesses integrate real-time streaming data workflows and other types of processing workflows—batch and ETL, for instance—within the same solution. This capability can help reduce tool sprawl.

To successfully leverage real-time data streaming, it can be helpful to consider and plan for challenges inherent in its implementation.

On-demand data ingestion, processing and analytics—especially for massive, complex volumes of data, also known as big data—are expensive and resource-intensive endeavors. In determining whether to commit funding and resources to real-time data streaming, enterprises should balance its costs against the costs of stale data and slower decision-making.

Fault tolerance (the ability of a system to continue to function despite the failure of a component) is crucial for successful real-time data streaming. Disruptions and downtime in real-time data streaming systems could result in data loss while undermining the speed that distinguishes streaming from other processing methods.

Comprehensive views into streaming data pipelines are necessary to avoid pipeline failures and ensure optimal performance. Monitoring key data quality metrics and quickly identifying problems—such as schema changes and data drift—can help enterprises ensure data integrity and pipeline reliability.

Real-time data streaming can include the continuous flow of sensitive data or personally identifiable information (PII) subject to data privacy regulations. Measures to ensure secure pipelines, including data encryption and access controls, can help enterprises adhere to regulatory regimes and avoid data breaches.