Data Streaming Technologies Overview

6 min read Darryn Campbell on Jul 8, 2024
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The ability to process high volumes of data (big data) in real-time has become crucial for many organizations, and this is where data streaming technologies come into the picture. These technologies allow large amounts of data to be processed in real-time or near real-time as it is generated, enabling businesses to gain immediate insights and make time-sensitive data-driven decisions.

At the heart of these technologies is the concept of data streams, also known as event streams. Data streams are sequences produced by various sources, such as social media feeds, Internet of Things (IoT) devices, log files, scientific data sets, and more. These streams of data are then ingested and processed by data streaming technologies.

Another important aspect is data stream scalability. As the volume of data grows, the technologies can scale to handle the increased load, ensuring that businesses can harvest real-time analytics. This means businesses can analyze their data as it is generated, enabling them to make quick decisions, especially useful in scenarios where timing is important, such as fraud detection or optimizing customer experiences.

Data streaming technologies support various formats, from structured data like SQL databases to unstructured data like live events or social media feeds; this ensures that businesses can process and analyze all types of data, regardless of their source or format. It is important to note that while the technologies offer many advantages, they also come with challenges; for example, they require sophisticated data engineering skills to implement and manage, requiring low latency and high throughput, especially when handling large volumes of data.

Basic Concepts of Data Streaming Technologies

Data streaming technologies are built on several fundamental concepts. Understanding these concepts is crucial to fully leveraging the power of real-time data processing:

Data Streams

Data streams are continuous data flows from various sources, such as IoT devices, log files, stock markets, etc. These data sources produce data at a high velocity, often in real-time or near real-time and the data produced is typically time-sensitive, meaning its relevance decreases with time.

Stream Processing

Stream processing is the real-time processing of data streams. Unlike batch processing, which processes data at scheduled intervals, stream processing handles data as soon as it arrives. This provides low latency, which is essential for time-sensitive applications such as user position tracking or commodity prices and decision-making based on those values.

Batch Processing vs Stream Processing

Batch processing and stream processing represent two different approaches to data processing. Batch processing handles large volumes of data at once, at scheduled intervals and is suitable for non-time sensitive data analysis tasks. On the other hand, stream processing handles data as soon as it is generated, providing real-time insights.

You might also see the term “micro-batch” used when talking about data stream processing, this approach sits between batch and stream processing, when very fresh data is needed, but not necessarily real-time.

Data Streaming Architecture

The typical architecture of data streaming technologies involves data sources, data ingestion systems, stream processing systems, and data storage systems.

  1. Data sources generate streams of data. 

  2. Data ingestion systems, like Apache Kafka or Amazon Kinesis, capture these data streams for processing.

  3. A Stream processor, such as Apache Flink or Apache Spark Streaming, processes the ingested data in real-time.

  4. The processed data is then stored in data lakes or data warehouses for further analysis or visualization dashboards.

  5. Data can be streamed to the edge of your network directly using systems such as the PubNub Kafka Bridge

Data flows through the architecture from the source to the destination in data pipelines. In essence, data pipelines represent the journey of data from its point of origin through ingestion, processing, and finally, storage or visualization.

Data Consistency

Data consistency is a significant concern in data streaming. Data streaming technologies use various techniques such as event ordering, exactly-once processing, and fault tolerance to ensure consistency. These techniques ensure that the data is processed in the correct order, no data is lost or processed multiple times, and the system can recover from failures without data loss.

For example, PubNub offers several ways to guarantee message delivery, such as read receipts, message ordering, and queuing.

Tools for Data Streaming Technologies

There are various open-source and commercial tools available for implementing data streaming technologies. These include Apache Kafka, Apache Flink, AWS Kinesis, and Microsoft Azure Stream Analytics. Each tool has its own strengths and use cases, and the choice of tool depends on the specific requirements of the data streaming application.

Next Steps with PubNub Data Streaming

After understanding the basic concepts and architecture of data streaming technologies, the next step is implementing these technologies into your own systems. PubNub provides a robust and scalable real-time data streaming platform that can be easily integrated into your existing architecture.

Image showing real-time data streaming

Here are the steps to get started with PubNub Data Streaming:

  1. Explore Demos: PubNub provides a real-time data streaming demo to help you understand how our platform works. This demo applies to a wide range of use cases, from chat apps to IoT device control. 

  2. Understand the Basics: PubNub provides a comprehensive glossary that describes key terms and concepts, including an entry on data streaming.

  3. Understand PubNub Illuminate: With PubNub Illuminate, you can tailor monetization strategies on the fly, link user behavior to incentives, track every action with custom, real-time aggregate and device metrics, and instantly see results - all without burdening your dev team.

  4. Registration: Sign up for a PubNub account. You can do this on their registration page. The free tier of your PubNub account has generous limits and does not require a credit card until you are read to upgrade.   

  5. Start Building: Once you have mastered the basics, build your own data streaming applications. PubNub provides a host of tutorials that guide you through building various types of applications, including a tutorial on building a real-time data streaming application

  6. Explore APIs: PubNub provides a wide range of APIs and SDKs that you can use to build your applications. You can find more information on our SDK documentation page.

  7. Understand Pricing: Before you finish building, it's helpful to know how much it's going to cost. You can find more information about PubNub's pricing on their pricing page.

Looking deeper into use cases for Data Streaming Technologies

Real-Time Data Analytics

One of the primary use cases of data streaming technologies is real-time data analytics. By processing and analyzing data streams in real-time, businesses can gain immediate insights into their operations and make quick, informed decisions. This can be particularly useful in industries such as finance, where real-time data analytics can be used for fraud detection, market trend analysis, and more.

PubNub Illuminate is an example of a real-time analytics platform. However, PubNub Illuminate is more than just a platform for data management, it also allows you to define conditions based on your data metrics, which, when triggered, will perform dynamic actions based on that data.

Internet of Things (IoT)

Another significant application of data streaming technologies is the Internet of Things (IoT) where devices generate data streams that can be processed in real-time to provide valuable insights. For example, monitoring the performance of industrial equipment enables businesses to detect and address issues before they lead to equipment failure.

Social Media Analysis

Social media platforms generate massive volumes of data every second, and data streaming technologies can process this data in real time, enabling businesses to monitor trends, track customer sentiment, and respond to customer feedback immediately.


In the e-commerce industry, data streaming technologies can track customer behavior in real time, allowing businesses to provide personalized recommendations, improve customer experiences, and increase sales.

Future Trends in Data Streaming Technologies

Integration with Machine Learning and AI

One of the significant trends in data streaming technologies is the integration of machine learning and generative AI. Machine learning models can be provided with the real-time data needed to make accurate and timely predictions. This can be particularly useful for predictive maintenance, where machine learning models can predict part failures based on real-time data - for example the battery discharge cycles of a mobile device can be used to estimate the expected life of the battery.

Increased Use of Open-Source Frameworks

Open-source frameworks such as Apache Kafka, Apache Flink, and Spark Streaming have become popular tools for implementing data streaming technologies. These frameworks offer robust capabilities for processing large volumes of data in real-time, and their open-source nature makes them highly customizable and adaptable to different use cases. We expect to see an increased use of these and other open-source frameworks in the future.

Greater Emphasis on Data Security and Privacy

As businesses increasingly rely on data streaming technologies to process sensitive data, there will be a greater emphasis on data security and privacy. This will involve implementing robust security measures to protect data streams from unauthorized access and ensuring compliance with data privacy regulations.

More Advanced Data Engineering Techniques

We expect to see more advanced data engineering techniques as engineers become more familiar with the technologies, including more sophisticated algorithms for processing streams, optimizing data pipelines, and ensuring data consistency.


The future of data streaming technologies looks bright. By providing business with greater operational insights in real-time, they can take immediate actions without having to rely on historical data, increasing customer satisfaction, efficiency and profitability. Regardless of your industry, whether it's customer management, e-commerce, IoT, or social media analysis, data streaming technologies have the potential to transform the way businesses operate.

PubNub can help you transform your business with streaming data. Feel free to reach out to the DevRel team at or contact our Support team for help with any aspect of your PubNub development.