Complete Guide to Event Streaming: Process, Components, Use Cases

What is event streaming?

Event streaming is a type of data processing where data is continuously generated, collected, and processed in real time. This approach contrasts with traditional batch processing, which handles data in discrete chunks at set intervals.

Event streaming captures events—such as transactions, log entries, or user interactions—as they occur, enabling immediate insights and actions. Technologies like Apache Kafka, Apache Pulsar, and Amazon Kinesis are commonly used for implementing event streaming architectures.

Unlike traditional batch processing, event streaming allows organizations to react to events as they occur, which enhances decision-making and operational efficiency. This makes it essential in a modern software architecture, especially for applications that demand low latency and high responsiveness, such as fraud detection, dynamic pricing, and real-time analytics.

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

Benefits of event streaming

Event streaming offers several advantages:

• Real-time processing: Enables immediate response to events, which is critical for applications requiring low latency, such as fraud detection or real-time analytics.
• Scalability: Event streaming platforms are designed to handle massive volumes of data, allowing organizations to scale seamlessly as their data grows.
• Fault tolerance: Advanced event streaming systems provide mechanisms for ensuring data reliability and consistency, even in the face of failures.
• Decoupling systems: By using an intermediary broker, event streaming decouples data producers and consumers, simplifying system architecture and enhancing flexibility.

Event streaming vs. batch processing

While both event streaming and batch processing are used for data processing, they serve different purposes and have distinct characteristics:

• Latency and timing: Batch processing operates on fixed schedules, processing data in bulk at predefined intervals. This means there is an inherent delay between data generation and processing. Event streaming processes data as events occur, providing immediate insights and actions. This makes it suitable for applications requiring constant, real-time data flows.
• Data handling: Batch processing is suitable for tasks that can tolerate delays, such as end-of-day reporting or monthly data aggregation. It handles large volumes of data at once, making it efficient for non-time-sensitive tasks. Event streaming is more suitable for applications requiring low latency, such as real-time monitoring, fraud detection, or dynamic pricing.
• System architecture: Batch processing systems are typically simpler, often relying on traditional databases and file systems to store and process data. Event streaming systems are more complex, involving brokers, topics, partitions, and offsets to manage the high throughputs and continuous flow of data.
• Scalability and flexibility: Batch processing systems are scalable but they do not provide the same level of flexibility or real-time capabilities as event streaming systems. Event streaming platforms are inherently scalable, built to handle vast amounts of data from multiple sources simultaneously. They decouple data producers and consumers, enhancing system flexibility and allowing independent scaling of each component.
• Use cases: Batch processing has several common use cases, including large-scale ETL (Extract, Transform, Load) jobs, data warehousing, and offline analytics. Event streaming is commonly used for real-time analytics, live dashboards, IoT data processing, and microservices communication.

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

The event streaming process

Event streaming involves continuously collecting and processing data from various sources as events occur. Here’s a typical workflow:

• Event production: Various sources generate events. These could be application logs, user activity, sensor data, etc.
• Event ingestion: Events are ingested into a broker (e.g., Kafka), which acts as an intermediary between producers and consumers.
• Event storage: Events are stored in a distributed, durable log within the broker, ensuring fault tolerance and high availability.
• Event processing: Consumers (e.g., analytics engines, databases) read and process events as the data arrives. This could involve filtering, aggregating, or transforming data.
• Event delivery: Processed events are delivered to their final destination, such as dashboards, alerting systems, or downstream applications.

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. Implement event sourcing for auditability: Use event sourcing to capture changes to the application state as a sequence of events. This not only improves auditability but also allows for easier debugging and the ability to rebuild the state from historical events.
2. Enable exactly-once semantics where critical: Configure your event streaming platform, like Kafka, to use exactly-once semantics for critical processing pipelines. This eliminates duplicates and ensures data consistency, which is crucial for financial transactions and similar applications.
3. Implement stateful stream processing where needed: Use stateful stream processing frameworks like Apache Flink or Kafka Streams to maintain state across events. This is beneficial for complex event processing scenarios that require stateful operations like joins and aggregations.
4. Use compacted topics for latest state access: Employ compacted topics in Kafka for scenarios where you need to access the latest state of an entity. This reduces storage overhead and ensures quick access to the most recent data.
5. Incorporate CQRS (Command Query Responsibility Segregation): Use CQRS to separate read and write operations in your event streaming architecture. This improves system performance and allows for more optimized data models for reading and writing.

Key components of event streaming

Event streaming platforms usually include the following components.

Brokers

Brokers act as intermediaries that enable communication between data producers and consumers. When an event is produced, it is sent to a broker, which then stores the event and ensures it is available for consumption. Brokers can handle high-throughput and low-latency data streams, making them crucial for real-time data processing.

Brokers provide features such as data persistence, fault tolerance, and load balancing. Apache Kafka is a widely used broker that supports distributed processing and ensures that data is replicated across multiple nodes for reliability and availability.

Topics

Topics serve as logical channels or categories within brokers that organize events. Each topic represents a specific stream of data, allowing producers to send events to designated topics and consumers to subscribe to the topics relevant to their needs. This categorization helps in managing and segregating different types of data streams.

For example, in an eCommerce platform, there could be separate topics for user activity, order transactions, and inventory updates. Topics help in reducing complexity and improving the efficiency of data processing by enabling focused consumption and processing of events.

Partitions

Partitions are subdivisions within topics that enable parallelism and scalability in event streaming systems. Each topic can have multiple partitions, which distribute events across different segments. This partitioning allows multiple consumers to read from a single topic concurrently, enhancing throughput and enabling horizontal scaling.

Each partition operates as an independent sequence of events, ensuring that events within a partition are processed in order. The distribution of partitions across multiple nodes in a cluster also ensures fault tolerance and high availability, as data can be replicated and balanced across the infrastructure.

Offsets

Offsets are unique numerical identifiers assigned to events within partitions. They mark the position of each event in the partition, allowing consumers to track and manage their progress in processing the events. When a consumer reads an event, it records the offset, which can be used to resume processing from the last known position in case of interruptions or failures.

This mechanism ensures reliable and consistent data processing, as consumers can avoid reprocessing events they have already handled. Offsets are important for maintaining data integrity and enabling fault-tolerant processing in event streaming architectures.

Event streaming use cases

Event streaming is useful for a range of applications in various industries. Here are some of the main use cases.

Banking and Financial Services

Financial institutions can process and analyze transaction data as it is generated, allowing for immediate identification of suspicious activities. This reduces the risk of fraud and enhances security.

Event streaming also enables real-time portfolio management, where stock prices and market data are continuously updated, allowing traders to make timely investment decisions. It can also support regulatory compliance by ensuring that all transactions are recorded and can be audited in real time.

Manufacturing

In manufacturing, event streaming is used to monitor and optimize production lines. Sensors and IoT devices on machinery and equipment generate continuous streams of data regarding operational status, performance metrics, and potential faults.

This data can be analyzed in real-time to detect anomalies, predict maintenance needs, and optimize production processes. Event streaming enables manufacturers to reduce downtime, improve product quality, and enhance overall efficiency by making immediate adjustments based on continuous insights.

Transportation and Logistics

Event streaming improves the efficiency and reliability of transportation and logistics operations through real-time tracking and analytics. For example, shipping companies can monitor the location and condition of goods in transit, ensuring timely deliveries and identifying potential issues such as delays or temperature excursions in perishable goods.

Fleet management systems use event streaming to track vehicle performance, optimize routes, and improve fuel efficiency. Real-time traffic data and predictive analytics help logistics companies to dynamically adjust delivery schedules, improving service levels and reducing costs.

Gaming and Entertainment

IOnline gaming platforms use event streaming to track player actions, game states, and system performance, ensuring smooth gameplay and immediate response to player inputs. Event streaming also supports real-time analytics for personalized content recommendations, targeted advertising, and dynamic in-game events.

What are event streaming platforms?

Event streaming platforms are systems that handle the continuous flow of data events in real time. These platforms provide the infrastructure and tools needed to ingest, store, process, and analyze event data as it occurs. They include features such as:

• Real-time data processing: Enable the processing of data as it is generated, allowing for immediate insights and actions.
• Scalability: Handle large volumes of data from numerous sources, scaling horizontally to accommodate growing data streams.
• Fault tolerance: Include built-in mechanisms to ensure data reliability and consistency, even in the event of hardware failures or network issues.
• Decoupling: Act as intermediaries to decouple data producers and consumers, simplifying the system architecture and increasing flexibility.

Examples of commonly used event streaming platforms include:

• Apache Kafka: An open-source platform widely used for building real-time data pipelines and streaming applications. Kafka is known for its scalability, durability, and high throughput.
• Apache Pulsar: A distributed messaging and streaming platform that provides low-latency data processing and strong consistency guarantees. It supports multi-tenancy and geo-replication.
• Amazon Kinesis: A fully managed service by AWS that makes it easy to collect, process, and analyze real-time data streams. Kinesis can handle large streams of data and integrate seamlessly with other AWS services.

Best practices for event streaming

Organizations can implement the following practices to improve the effectiveness of event streaming systems.

Define Clear Event Schemas

Schemas define the structure, format, and data types of the events being transmitted. Using a well-defined schema helps ensure that all producers and consumers understand the structure of the data, reducing the likelihood of errors and inconsistencies.

Popular schema formats include JSON, Avro, and Protobuf, each offering varying degrees of flexibility and compactness. Schemas should be versioned and managed centrally to maintain compatibility across different services and to support smooth upgrades and changes.

Ensure Idempotency

Idempotency ensures that processing the same event multiple times does not produce different outcomes. This is particularly important for fault tolerance and retries. For example, if a consumer fails after processing an event but before acknowledging it, the event might be reprocessed.

Designing operations to be idempotent, such as using unique transaction IDs or employing upsert operations, helps maintain data integrity and consistency even when events are processed more than once.

Use Partitioning Strategically

Dividing topics into partitions enables parallel processing and helps distribute the load across multiple nodes. The partitioning key should be chosen carefully to ensure an even distribution of events, avoiding hotspots where some partitions are overwhelmed while others are underutilized.

For example, in an eCommerce application, using user ID or order ID as the partition key can help achieve balanced load distribution. Effective partitioning improves throughput and ensures that the system can scale horizontally.

Handle Backpressure Gracefully

Backpressure occurs when the rate of incoming events exceeds the processing capacity of consumers. Handling backpressure gracefully is essential to prevent system overload and ensures stable performance. Techniques include rate limiting, buffering, and load shedding.

Rate limiting controls the flow of events into the system, while buffering temporarily stores excess events until they can be processed. Load shedding involves discarding less critical events when the system is under heavy load. These strategies help maintain service quality and prevent system failures during peak loads.

Optimize Resource Utilization

Efficient resource utilization helps in maintaining performance and reducing costs in event streaming systems. This involves monitoring and adjusting the allocation of CPU, memory, and network resources based on the workload. Autoscaling features can automatically adjust resources in response to changes in event volume.

Additionally, optimizing the configuration of the event streaming platform, such as adjusting batch sizes and compression settings, can significantly improve throughput and reduce latency. Regular performance tuning and capacity planning help ensure that the system operates well under varying loads.

Event streaming with Instaclustr

In today’s data-driven world, event streaming has become a critical component for businesses seeking real-time insights and efficient data processing. Instaclustr, a leading managed service provider, offers a comprehensive solution for event streaming that empowers organizations to harness the power of streaming data.

At the core of Instaclustr’s event streaming offering lies Apache Kafka, a highly scalable and distributed streaming platform. Key features of Instaclustr’s managed Kafka service include:

• Manages the complexities of deploying, configuring, and maintaining Kafka clusters: Allows businesses to focus on leveraging the platform’s capabilities. Kafka provides a fault-tolerant and high-throughput infrastructure for handling real-time data streams efficiently.
• Ensures hassle-free event streaming operations: Takes care of the infrastructure setup, including provisioning and scaling of Kafka clusters, as well as monitoring and maintenance tasks.
• Provides proven expertise: With Instaclustr’s expertise, businesses can avoid the challenges associated with self-managing Kafka and instead benefit from a fully managed and reliable event streaming solution.
• Enables seamless integration with data sources and sinks: Makes it easy to ingest and process data from multiple systems. Whether it’s capturing events from web applications, IoT devices, or other sources, Instaclustr provides the necessary connectors and APIs to facilitate data ingestion into Kafka topics.
• Provides elastic scalability: As data volumes and streaming requirements grow, Instaclustr can dynamically scale Kafka clusters to handle the increased load.
• Ensures high availability and data durability: Ensures Leverages Kafka’s replication capabilities, ensuring data is automatically replicated across multiple Kafka brokers, providing fault tolerance and eliminating single points of failure.
• Regular backups and disaster recovery: Ensures data integrity and minimizing the risk of data loss.
  Comprehensive monitoring and support for event streaming: Businesses can leverage Instaclustr’s monitoring tools and dashboards to gain insights into the health and performance of their Kafka clusters.
• Dedicated support team: Available to address any issues or provide guidance, ensuring smooth operations and minimizing downtime.

Instaclustr offers a robust and managed event streaming solution powered by Apache Kafka. By leveraging Instaclustr’s expertise, businesses can streamline their event streaming operations, focus on extracting valuable insights from real-time data, and drive innovation.

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