Real-time data streaming: What it is and how it works

The ability to process and analyze data in real time is no longer a luxury but a necessity for many businesses. Real-time data streaming allows organizations to harness the power of continuous data flows, enabling immediate insights and responsive decision-making. 

Whether it's monitoring financial transactions for fraud detection, optimizing user experiences on a website, or ensuring high availability in mission-critical applications, real-time data streaming is increasingly central to many business functions. Read on to learn how it works, how it differs from batch data processing, its benefits, real-world examples, and how you can get started.

What is real-time data streaming?

Real-time data streaming involves collecting, processing, and analyzing continuous streams of data in real time. These data streams could include server activity logs, user engagement events in an app or website, stock market fluctuations, or any other context where data is generated continuously at a high speed with no set beginning or end. 

Streaming data is time-stamped and often highly time-sensitive, making continuous, real-time analysis key for a variety of business applications. This requires specialized software architectures that reduce processing times to a matter of microseconds.

How does real-time data streaming work?

The process of real-time data streaming starts with a data source, like a website or app, an IoT sensor, or any other device or endpoint generating a stream of data. From there, the stream passes through data streaming architecture with a few key software components:

• Producers are software programs that collect raw streaming data and publish it to topics. A topic is the basic unit of organization for streaming data. It’s roughly the equivalent of a table in a relational database.
• Brokers parse the topics and feed them to the correct consumers, essentially “brokering” a transaction between producer(s) and consumer. A group of Redpanda brokers is known as a cluster.
• Consumers are software programs with analytics capabilities (e.g., aggregation, machine learning) that read topics from the brokers and deliver various outputs. One consumer can consume multiple topics.

While this simplified explanation implies a simple one-directional flow, actual streaming data architectures can be considerably more complex. For example, some topics may feed back into the systems that originated the data, turning data pipelines into loops. Your organization’s streaming data architecture will ultimately depend on your specific use cases, existing legacy infrastructure, and other factors.

Real-time data processing vs. batch data processing

Streaming data processing and batch data processing are two distinct approaches to handling and analyzing data, each suited to different types of use cases. 

Think of the difference between streaming a video to your computer vs. downloading a video file. In the first case, your computer receives a continuous stream of data you use in real time (to watch the video). In the other, you wait to download all the data you need in one chunk.

Here are a few key differences between these approaches:

What are the benefits of real-time data streaming?

One of the primary benefits of real-time data streaming is the ability to gain immediate insights from data as it is generated. This enables organizations to make faster, more informed decisions, which is crucial in environments like financial markets, where rapid responses to data can mean the difference between profit and loss, or in cybersecurity, where suspicious activity within a system must be addressed immediately to counter potential cyberattacks. Real-time data streaming allows businesses to react swiftly to changing conditions, such as market fluctuations, customer behavior, or system anomalies, enhancing overall agility and responsiveness.

In addition, in customer-centric industries, real-time data streaming can significantly improve the user experience by enabling personalized interactions and services. For example, in e-commerce, real-time analytics can be used to recommend products based on a customer's current browsing behavior, increasing the likelihood of a purchase. This level of immediacy and personalization helps build stronger relationships with customers and drives customer loyalty.

What are the challenges of real-time data streaming?

For use cases where real-time data streaming makes sense, the benefits almost always outweigh the potential downsides. If you design your data streaming architecture properly, you can mitigate common challenges including:

‍Managing traffic spikes

For some types of data streams, traffic can be highly variable, with sudden bursts or spikes. For example, a web publisher might see a sudden spike in user engagement activity when a piece of content goes viral, potentially overwhelming its systems. You need infrastructure that can scale up and down rapidly without delays, downtime, or data loss.

Minimizing costs

Real-time data streaming does require you to invest in dedicated infrastructure and in some cases additional cloud storage or compute capacity. But you can control costs with solutions that maximize operational efficiency. For example, Redpanda utilizes tiered cloud storage and Cloud Topics that batch writes directly to cost-efficient object storage and replicate only metadata to save on cross-AZ networking costs.

‍Maintaining high availability

Streaming data applications need to pull from the stream continuously, which means any downtime can cause significant issues. Mitigate the risks of stream interruption due to broker downtime by distributing your data pipelines and underlying data across multiple availability zones (AZs). 

Be mindful, however, that this can drive up network bandwidth costs, since traffic that crosses AZs may incur additional fees from your cloud provider. A streaming data platform with features like follower fetching can provide the high availability you need while optimizing your network traffic to minimize added costs.

‍Ensuring data safety

Backing up data stored in the cloud or on-prem is a basic element of security and disaster recovery protocols at most enterprises. However, it’s less common to back up data in motion. If real-time streaming data is lost or corrupted in transit, it can be difficult to recover — unless you’re using a highly fault-tolerant, durable, and enterprise-grade solution.

Besides distributing data across availability zones, Redpanda also guarantees a high level of data safety through our Raft consensus algorithm, which governs data replication across clusters. Both data and cluster metadata are regularly backed up to cloud object storage, allowing entire clusters to be efficiently restored directly from object storage.

What are examples of real-time data streaming?

Because real-time data streaming enables the generation of immediate, actionable insights from data, it’s important for a number of use cases across industries that require fast, accurate decision-making at scale. 

• Security monitoring: Monitoring complex IT infrastructure requires ingesting and analyzing activity logs and other data streams from endpoints across a network. Streaming this data in real time enables security applications to immediately flag anomalous behavior and respond swiftly to potential attacks.
• Event stream processing: Users continually engage with a website or app, yielding streams of information that can be analyzed in real time and used to optimize website or app content.
• Fraud detection: To identify potential fraud before it hurts their bottom line, financial institutions must analyze streams of transaction data to flag suspicious behavior in real time.
• Algorithmic stock trading: An algorithmic trading firm might need to process millions of data points a second, including market data and system information. Real-time data streaming is necessary as a split second can make a difference on a trade. For example, Redpanda’s performance-engineered architecture delivered latencies as low as 50 milliseconds for our p95 and 150 milliseconds for p99 for one trading firm.

Why Redpanda is the data streaming platform for developers

You need a data streaming platform powerful enough to manage all the real-time data streams you need, but simple to deploy and manage.

Redpanda’s enterprise-grade streaming solution delivers 10x lower tail latencies and 6x faster transactions without the technical complexity of Apache Kafka. With Redpanda, you manage all your clusters through a single tool, and gain visibility into all your streams with the industry’s most complete web UI. Best of all, there’s no need for external dependencies like JVM that make deploying in modern environments a headache.

Ready to accelerate your data pipelines? Check out Redpanda’s platform capabilities.

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