What is streaming data?

Organizations today generate high volumes of data on everything from Internet of Things (IoT) devices to e-commerce transactions. Streaming data, also known as "data streaming" or "real-time data streaming", helps organizations process these continuous data flows as they come in.

Examples of streaming data include:

• Financial market data that tracks stock prices and trading activity
• IoT sensor readings monitoring equipment performance
• Social media activity streams capturing user engagement
  
• Website clickstream data showing visitor behavior patterns

Organizations often use streaming data to support business initiatives that rely on real-time data for rapid, data-driven decision-making, such as data analysis and business intelligence (BI).

Streaming data is frequently part of big data collection and processing efforts. For instance, organizations can analyze continuous data streams by using big data analytics to gain insight into operational efficiency, consumer trends and changing market dynamics.

Because it flows continuously, streaming data requires different processing methods than traditional batch processing. These often include scalable streaming architectures and stream processors that manage data ingestion, processing and analysis while maintaining optimal performance.

In recent years, the rise of artificial intelligence (AI) and machine learning has further increased the focus on streaming data capabilities. These technologies often rely on streaming data processing to generate real-time insights and predictions.

According to Gartner, 61% of organizations report having to evolve or rethink their data and analytics operating model because of the impact of AI technologies.¹

Organizations can process data in two primary ways: batch processing or streaming data.

While both methods handle large volumes of data, they serve different use cases and require different architectures.

Key differences include:

• Processing model: Batch processing aggregates and analyzes datasets in batches at fixed intervals, whereas streaming data uses real-time data processing tools to process data as it arrives. This means streaming systems can yield insights and take action immediately, while batch systems operate on a periodic schedule.
  
  
• Infrastructure needs: Batch systems often use traditional data storage and analytics tools such as data warehouses, whereas streaming requires specialized frameworks and data streaming platforms built to handle real-time data flows.
  
  
• Performance requirements: Batch systems can optimize resource use during scheduled runs, whereas stream processing needs fault-tolerant systems with low latency. In other words, streaming systems must process data in real-time without delays, even when data volumes are high or issues occur.

Organizations typically choose between batch and stream processing based on data volumes, latency needs and business objectives. Many use both approaches within a unified data fabric to handle different types of data tasks.

For example, an e-commerce organization might use batch processing to generate daily sales reports while using streaming data and real-time analytics systems to monitor key website metrics.  

At a high level, streaming data works by continuously capturing, processing and analyzing real-time data flows from various sources. This process consists of four key stages:

• Data ingestion
• Stream processing
• Data analysis
• Data storage

The first stage involves capturing incoming data streams from diverse sources. Modern data ingestion tools such as Apache Kafka buffer and standardize these streams as they arrive, which helps ensure both scalability and data consistency.

Organizations typically integrate data ingestion tools with other components to create unified workflows. Data integration tools can also further align disparate data types into a standardized format for processing to help ensure that data from multiple sources can be combined and analyzed effectively.

In the processing stage, stream processing frameworks such as Apache Flink analyze and transform data while it is in motion. These frameworks enable organizations to:

• Process complex events in real-time
  
  
• Perform data aggregation at scale, such as calculating averages, counting events or adding up transaction values
  
  
• Apply transformations—such as filtering, enriching or formatting data—as data flows through the data pipeline

At this stage, organizations derive actionable business insights from streaming data flows through data visualization and other analytical tools.

Key applications include:

• Real-time dashboards that deliver critical metrics and KPIs
  
  
• Operational applications that automate workflows and optimize processes
  
  
• Machine learning models that analyze patterns to predict outcomes

When storing streaming data, organizations must balance the need to quickly access data for real-time use with long-term data storage, cost-efficiency and data compliance concerns.

Many organizations use data lakes and data lakehouses to store streaming data because these solutions offer low-cost, flexible storage environments for large amounts of data. After streaming data is captured, it might be sent to a data warehouse, where it can be cleaned and prepared for use.  

Organizations often implement multiple data storage solutions together in a unified data fabric. For example, financial institutions might use data lakes to store raw transaction streams while using warehouses for analytics and reporting.

Organizations can use many types of streaming data to support real-time analytics and decision-making. Some of the most common streaming data flows include:

Streaming data enables organizations to process high volumes of real-time information for immediate insights and actions.

Common applications include:

Financial institutions frequently use streaming analytics to process market data, transactions and customer interactions.

For example, credit card companies rely on streaming data for fraud detection. Streaming data platforms allow these companies to analyze thousands of transactions per second to detect unusual activity and flag or block suspicious transactions.

Modern manufacturing facilities often use IoT device sensors and real-time data processing to improve operational efficiency. 

For instance, an automotive plant might monitor thousands of assembly line sensors, tracking metrics such as temperature, vibration and performance. This data can help operators detect inefficiencies early and schedule preventive maintenance to avoid downtime.

Healthcare providers rely on streaming applications to process data from medical devices and patient monitoring systems.

In intensive care units, for instance, bedside monitors stream vital signs through data pipelines to central processors. These processors can then identify concerning patterns and automatically alert medical staff when intervention is needed.

Retailers and e-commerce companies use streaming data from point-of-sale systems, inventory sensors and online platforms to optimize operations.

For example, a large e-commerce platform can use Apache Kafka to process clickstreams from millions of shoppers to gauge demand and personalize customer experiences.

Transportation companies often use streaming analytics to process GPS data and IoT sensor readings for fleet optimization.

For instance, a logistics provider can integrate real-time data from thousands of vehicles with weather and traffic datasets. Stream processors can then enable automated route optimization with minimal latency to help drivers avoid delays. 

Streaming data helps support cybersecurity measures such as automated anomaly detection. AI and machine learning systems can analyze data flows from monitoring tools throughout the system to identify unusual patterns or suspicious behaviors, enabling immediate responses to potential issues. 

Streaming data also plays a vital role in AI and machine learning. For example, stream processing frameworks can support continuous AI model training so that machine learning algorithms can adapt to changing patterns in near real-time.

Machine learning systems can also learn incrementally from streaming data sources through a process called online learning, by using specialized algorithms to improve accuracy without requiring complete model retraining.

With the help of both open source and commercial streaming data solutions, organizations can build scalable data pipelines that are fault-tolerant, meaning they can recover from failures without data loss or downtime.

Two key types of technologies underpin most streaming data implementations: stream processing frameworks and streaming data platforms.

Stream processing frameworks provide the foundation for handling continuous data flows. These frameworks help organizations build high-performance data pipelines that consistently process large volumes of data quickly and reliably.

Three open source frameworks dominate the streaming landscape:

• Apache Kafka
• Apache Flink
• Apache Spark

A leading streaming platform, Kafka can handle massive data volumes with millisecond latency. Organizations often use Kafka to build pipelines for activity tracking, operational monitoring and log aggregation. 

Apache Flink specializes in complex event processing and stateful computations. It’s valuable for real-time analytics, fraud detection and predictive maintenance, where understanding the context of events over time is critical.

Known for its unified analytics capabilities, Spark can handle both batch and streaming data simultaneously. This ability makes it useful in scenarios where organizations need to analyze historical data alongside live data.

Streaming data platforms offer various tools and functions to help support the entire lifecycle of streaming data, from ingestion and processing to storage and integration.

Many major cloud providers offer managed streaming data solutions that make it easier for organizations to set up high-volume data streaming applications. Services such as Amazon Kinesis from Amazon Web Services (AWS), Microsoft Azure Stream Analytics, Google Cloud’s Dataflow and IBM Event Streams provide ready-to-use tools. Companies don’t have to build complex infrastructure from scratch.

These services can also integrate with on-premises streaming tools to create hybrid architectures that can help balance performance needs with data privacy requirements. 

Organizations can also use tools such as IBM StreamSets and Confluent to build streaming data pipelines tailored to their unique IT ecosystems.

While streaming data can offer many benefits, organizations can face challenges when building the data architectures necessary to support streaming applications.

Some common challenges include:

• Scaling data architecture: Streaming data processing often entails handling massive amounts of data from many sources. Organizations can struggle if their streaming architectures can't efficiently scale to process high volumes of data.
  
  
• Maintaining fault tolerance: Streaming systems must be fault tolerant while processing potentially millions of events per second. Otherwise, organizations risk losing data to system malfunctions and misbehaviors.
  
  
• Monitoring performance: Real-time applications require constant monitoring of metrics such as latency, throughput and resource utilization to maintain optimal performance, a demand that can break already-strained processing systems.
  
  
• Implementing data governance: Organizations must consider how they store and process streaming data that contains personally identifiable information (PII) or other sensitive information that falls under the jurisdiction of the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) or other data governance requirements.