Data Streaming: 5 Key Characteristics, Use Cases and Best Practices

What is data streaming

Data streaming refers to the continuous transfer of data at a high velocity, enabling real-time processing and insights. Unlike traditional batch processing, which deals with data in large, discrete chunks, data streaming focuses on a constant data flow. This method is useful for applications requiring instant data consumption and processing, such as real-time analytics, machine learning, and monitoring systems.

Real-time analytics and immediate responses are critical for industries such as finance, healthcare, and the internet of things (IoT). Data streaming enables organizations to act on data as it is generated, offering an advantage over batch processing, which introduces lag time and additional costs for storing and managing data over its lifecycle.

This is part of a series of articles about real time streaming

5 characteristics of streaming data

The benefits and challenges of using streaming data derive from the following characteristics.

1. Chronological Significance

Streaming data retains its value based on the order of events. Timeliness is important for making accurate decisions, as the sequence in which data arrives can impact the resulting analysis. For example, in monitoring systems, the sequence of error logs can determine the cause and effect relationships between different events.

Ensuring chronological accuracy in data streams often involves time-stamping data as it is generated. This allows systems to process events in the correct order, preserving the temporal integrity of the data.

2. Continuous Flow

Data streaming involves a constant flow of data rather than periodic updates. This continuous nature supports real-time analytics, where data is immediately processed as it arrives. Streaming applications can handle high-velocity data streams, enabling timely reactions to events and trends.

Applications like financial trading systems or live social media feeds rely on continuous data flow. These systems must process vast amounts of incoming data without delays to provide accurate, up-to-date information.

3. Uniqueness

Streaming data often features unique, non-repetitive events. Each data point may represent a distinct incident, such as a sensor reading or a user action. This uniqueness requires systems to handle each incoming data piece individually, ensuring no critical information is overlooked.

Systems must be designed to accommodate this uniqueness, processing data points effectively while maintaining overall performance and accuracy. Data deduplication strategies may be necessary to handle occasional repetitive data, but the primary focus remains on treating each piece of streamed data as an individual event.

4. Nonhomogeneity

Streaming data typically comes in various formats and from different sources, challenging systems to process diverse data types efficiently. This nonhomogeneity demands robust, flexible data processing architectures capable of handling mixed data streams without compromising performance.

Processing nonhomogeneous data often involves normalizing and integrating data from multiple sources to provide a cohesive, comprehensive view. Systems must adapt to different data structures, ensuring seamless integration and consistent processing across various data types, crucial for accurate and meaningful analytics.

5. Imperfection

Streaming data is often imperfect, containing errors, missing values, or inconsistencies. Systems that handle streaming data must implement mechanisms for real-time data validation, cleansing, and correction to ensure data quality and reliability.

Addressing data imperfections in real-time can involve filtering out erroneous data, filling in gaps, and reconciling conflicting information. Effective handling of imperfect data ensures that insights derived from real-time analytics are dependable and actionable.

How stream processing works

Stream processing involves continuously ingesting, analyzing, and acting on data in motion. Here’s how it works:

• Data ingestion: Data is ingested from various sources such as IoT devices, social media feeds, financial transactions, and logs. Tools like Apache Kafka and Amazon Kinesis are commonly used for this purpose. These tools act as buffers that can handle high-throughput data streams, ensuring reliable data ingestion.
• Data processing: Once ingested, the data is processed in real-time using stream processing frameworks such as Apache Flink, Apache Storm, or Spark Streaming. These frameworks support complex event processing, enabling operations like filtering, aggregating, joining, and correlating data on-the-fly.
• Event stream management: Stream processors manage event streams by defining processing logic through directed acyclic graphs (DAGs) of operators. Each operator performs specific tasks on the data, such as transformations, computations, or enrichment, ensuring that each piece of data is processed as soon as it arrives.
• State management: Maintaining the state is crucial in stream processing, especially for operations that require context, like aggregations over time windows. Stream processing frameworks provide built-in state management mechanisms to keep track of intermediate states and ensure accurate results.
• Output and action: Processed data is then pushed to various destinations such as databases, data warehouses, dashboards, or alerting systems. This enables real-time analytics, monitoring, and automated decision-making, facilitating immediate responses to events and trends.
• Scalability and fault tolerance: Stream processing systems scale horizontally by adding more processing nodes. They also incorporate fault tolerance mechanisms to recover from failures without data loss, ensuring uninterrupted data flow and processing.

Related content: Read our guide to event streaming (coming soon)

Tips from the expert

Andrew Mills

Senior Solution Architect

Andrew Mills is an industry leader with extensive experience in open source data solutions and a proven track record in integrating and managing Apache Kafka and other event-driven architectures.

1. Incorporate event sourcing: Use event sourcing to record all changes as a sequence of immutable events. This approach helps in rebuilding state, provides a clear audit trail, and makes it easier to debug issues by replaying events to understand their impact.
2. Optimize serialization formats: Choose efficient serialization formats like Avro, Protobuf, or JSON for data interchange. Optimizing serialization can significantly reduce the size of data transmitted and improve processing speed.
3. Use schema evolution: Design data schemas that can evolve over time without breaking existing consumers. Tools like Apache Avro or Protobuf support schema evolution, allowing you to add new fields or change data types while maintaining backward compatibility.
4. Use backpressure mechanisms: Implement backpressure mechanisms to handle situations where producers generate data faster than consumers can process. This prevents data loss and ensures smooth operation. Apache Kafka and Apache Pulsar provide built-in support for backpressure.
5. Leverage edge computing: Implement edge computing to preprocess data at the source, reducing latency and bandwidth usage. This is especially useful for IoT applications where immediate local processing can provide faster insights and reduce the load on central systems.

Data streaming vs batch processing

Data streaming offers real-time, continuous data handling. Streaming processes data instantly as it arrives, making it suitable for applications that require immediate insights. Data streaming is more useful in scenarios where timeliness is crucial, such as fraud detection or live monitoring.

Batch processing deals with data in large, scheduled chunks. It collects data over a period before processing it, which can lead to delays. Batch processing is often used for tasks requiring extensive computation on accumulated data, like large-scale data migrations or comprehensive statistical analysis.

Use cases and examples of data streams

Here are some examples of scenarios that rely on streaming data.

Weather Data

Weather stations continuously generate data on temperature, humidity, and other atmospheric conditions. This streaming data is used for real-time weather forecasting, enabling accurate and timely predictions. Meteorologists and automated systems use this data to issue weather alerts and warnings, aiding in public safety and preparedness.

Real-time weather data streaming also supports various industries, from agriculture to aviation, helping them make informed decisions. Farmers can adjust irrigation schedules, and airlines can modify flight paths based on up-to-date weather information.

Data from Local or Remote Sensors

Sensors embedded in infrastructure, machinery, or vehicles generate continuous data streams. This data provides insights into operational efficiency, maintenance needs, and status monitoring. Industries such as manufacturing and transportation rely heavily on sensor data to optimize performance and prevent equipment failure.

An example of the use of remote sensors is environmental monitoring. Environmental sensors send data about pollution levels, soil moisture, and wildlife activity. This information supports conservation efforts, resource management, and regulatory compliance. Sensor data streaming ensures timely responses to changing conditions and potential issues.

Transaction Logs from Financial Systems

Financial systems generate a stream of transaction logs, capturing every detail of account activities, trades, and transfers. Real-time processing of this data is crucial for detecting fraud, ensuring compliance, and managing risk. Streaming data analysis helps financial institutions respond swiftly to suspicious activities.

Instant access to transaction data also supports real-time dashboards and analytics, providing insights into customer behavior and market trends. This enables financial institutions to offer personalized services.

Data from Health Monitoring Devices

Health monitoring devices, such as wearable fitness trackers and medical sensors, continuously collect data on vital signs, activity levels, and other health metrics. This streaming data can be used for real-time health monitoring, early detection of medical conditions, and personalized healthcare.

Hospitals and clinics use real-time data from medical devices to monitor patients’ conditions continuously. This enables timely interventions and improved patient outcomes. Health data streaming supports proactive healthcare, shifting the focus from reactive treatments to preventive measures.

Website Activity Logs

Websites generate a continuous stream of activity logs detailing user interactions, page visits, and transaction histories. Analyzing this data in real-time helps organizations understand user behavior, track engagement, and optimize website performance. Insights from streaming logs aid in personalizing user experiences and increasing customer satisfaction.

Real-time web activity monitoring is useful for detecting and mitigating security threats, such as DDoS attacks or unauthorized access attempts. Immediate analysis of activity logs enables rapid responses to security incidents, protecting data integrity and maintaining user trust.

Benefits of data streaming and processing

Here are some of the main benefits of streaming and processing data in real time.

• High returns: Stream processing offers immediate insights, enabling organizations to act quickly on data. This rapid reaction time can result in higher returns, as timely decisions often lead to better outcomes in scenarios such as financial trading, customer service, and marketing campaigns.
• Reduced infrastructure costs: Stream processing systems often require less hardware than batch processing systems since they handle data immediately, reducing the load on storage systems. This can lead to cost savings for organizations dealing with large volumes of data.
• Reduced preventable losses: Real-time data processing aids in identifying and mitigating potential issues before they escalate into significant problems. For example, continuous monitoring of financial transactions can detect fraudulent activities instantly, reducing the risk of substantial losses.
• Competitiveness and customer satisfaction: It allows organizations to respond swiftly to changing market conditions and customer needs. For example, immediate analysis of streaming data can inform personalized product recommendations to individual users.

Data streaming challenges

Here are some of the main challenges associated with data streaming:

• Data overload: As the volume and velocity of incoming data increase, systems can become overwhelmed, leading to potential bottlenecks. This can result in slower processing times and decreased performance.
• Data loss or corruption: Continuous data streams can suffer from network issues, hardware failures, or software bugs, leading to incomplete or inaccurate data being processed.
• Overhead: Implementing data streaming introduces overhead in terms of computing resources, network bandwidth, and system complexity.

Data streaming best practices

Here are some of the ways that organizations can make the most of their data streaming systems.

Choose the Right Tools and Technologies

Selecting appropriate tools and technologies is critical for successful data streaming. Platforms like Apache Kafka, Apache Flink, and Spark Streaming offer capabilities for different use cases and requirements.

The evaluation should consider factors such as scalability, fault tolerance, and ease of integration. It’s also important to consider the resources required to operate the system. Many organizations cannot afford to process data streams in-house, so they opt for cloud-based services to outsource their data streaming.

Design for Scalability and Performance

Data streaming systems must be designed to handle increasing data volumes and maintain low latency. This involves leveraging distributed processing, efficient data partitioning, and load balancing.

Anticipating future growth and ensuring the infrastructure can scale accordingly is also important. Implementing performance monitoring and tuning mechanisms helps maintain optimal system performance, supporting continuous data streaming workflows.

Ensure Data Quality and Consistency

Maintaining data quality and consistency in real-time streams is important for ensuring accurate insights. This involves implementing data validation, cleansing, and consistency checks as data is ingested and processed. High-quality data ensures reliable analytics and decision-making.

Strategies like schema enforcement, anomaly detection, and real-time correction help address data quality issues. Consistent and accurate data streams enhance the value of real-time processing, providing actionable insights for various applications.

Optimize Data Processing

Optimizing data processing helps maintain high performance in streaming environments. This involves fine-tuning data ingestion pipelines to reduce latency and increase throughput. Techniques such as in-memory processing, parallel processing, and efficient serialization/deserialization of data can significantly improve processing speed.

Additionally, using stream processing frameworks that support dynamic scaling and load balancing can help manage changing data loads. Implementing strategies like micro-batching, where small, manageable batches of data are processed in near-real-time, can also balance the need for speed with processing efficiency. Regular performance monitoring and tuning based on workload characteristics help avoid bottlenecks.

Implement Proper Error Handling and Recovery

Effective error handling and recovery mechanisms are essential to maintain the reliability of data streaming systems. Implementing strategies for error detection, automatic retries, and failover support ensures continuous operation and minimizes downtime.

Systems must be built to handle various failure scenarios gracefully, ensuring data integrity and reliability. Proper error handling and recovery improve system resilience, providing a stable foundation for real-time data processing and analytics.

Instaclustr best practices for data streaming: Ensuring efficient and reliable operations

Data streaming has become a critical component for organizations seeking real-time insights and efficient data processing. Instaclustr, a leading managed service provider, offers a comprehensive solution for data streaming. Some of the best practices recommended by Instaclustr to ensure efficient and reliable data streaming operations include:

Designing with resilience in mind: Instaclustr recommends implementing fault-tolerant architectures by leveraging features such as Kafka replication and distributed processing. By replicating data across multiple Kafka brokers and distributing the workload across a cluster of machines, businesses can ensure high availability and minimize the risk of data loss or system failures.

• Efficient topic partitioning: Essential for achieving optimal performance in data streaming. Instaclustr advises to carefully consider the number of partitions for each Kafka topic based on the expected data volume and processing requirements. A well-distributed partitioning strategy ensures load balancing and parallel processing, enabling faster and more efficient data ingestion and consumption.
• Monitoring data streaming pipelines: Essential to identify bottlenecks, optimize performance, and ensure smooth operations. Leveraging Kafka monitoring tools and Instaclustr’s monitoring capabilities, businesses can track key metrics such as throughput, latency, and resource utilization. This data helps identify potential issues, optimize resource allocation, and fine-tune configurations for optimal performance.
• Emphasize security: Data streaming often involves sensitive information. Instaclustr recommends implementing robust security measures, including encryption, authentication, and access control mechanisms. By securing Kafka clusters and implementing proper user authentication and authorization, businesses can protect their data and ensure that only authorized users have access to the streaming infrastructure.
• Enable easy scalability: Instaclustr enables businesses to scale their data streaming infrastructure seamlessly. As data volumes and processing requirements grow, Instaclustr’s managed service allows for dynamic scaling of Kafka clusters. This elasticity ensures that businesses can handle increased workloads without compromising performance or incurring unnecessary costs.
• Disaster recovery and backup: Instaclustr advises implementing robust disaster recovery and backup strategies to protect against data loss and ensure business continuity. Regularly backing up Kafka data and implementing disaster recovery measures, such as replicating data across different availability zones or regions, helps minimize the impact of potential failures and ensures data integrity.

By following these guidelines, businesses can design resilient data pipelines, optimize performance, ensure data security, and scale their streaming infrastructure effectively. With Instaclustr’s managed service, organizations can focus on leveraging the power of data streaming to gain real-time insights and drive innovation, while Instaclustr takes care of the complexities of managing and maintaining the underlying infrastructure.

Check out our blog: Apache Flink® vs Apache Kafka® Streams: Comparing Features & Capabilities part 1 and part 2.