Data Streaming: The Past, Present, and Future of Stream Processing

The rate at which data is being generated has now become exponential with everybody connected to everything. Regardless of the industrial base, organizations need to process data in the quickest possible manner to increase its efficiency. Real-time fraud detection, individual-level recommendation, and operational performance tracking of organizations is something that the ability to process and analyse streaming data has done for all of us. 

Stream processing technology forms the bedrock of real-time data analytics, and its importance has accelerated in the last couple of years. In this blog, we take an all-encompassing look into the past, the present, and the future of stream processing, while exploring how this technology evolved, how it has been applied lately, and which direction it might take in the future. 

The Past of Stream Processing: Laying the Foundation 

The concept of stream processing began to emerge in early 2000, considering that the traditional batch processing systems had started to lose their ability to manage the substantially dynamic and voluminous nature of modern data. Batch processing was about processing data in large chunks or batches and had generally been well suited for many application scenarios until businesses demanded real-time insights in which case this approach became an old hat. 

Some of the very early adopters were in the telecommunication firms and financial firms to meet needs. In fact, results about first generation systems in real-time scale and the robustness of systems provided an early basis for the significant stream processing developments. 

Early Technology: The Birth of Stream Processing 

Dataflow, developed by Google, and Apache Storm, created by Twitter, were the first gateways for stream processing. Systems like those would enable organisations to process data on the fly without latency being introduced from batch processing, but these were far from scalable and fault-intolerant with no understanding of complex event processing. 

• Apache Storm, which was launched back in 2011, was the flag bearer at that time; it promised a distributed system that is fault tolerant on-stream processing, which is capable of dealing with high-volume data. However, at the time it was new and advanced enough; on the current release, it still lacks only that feature of sophistication that would help it support modern data analytics on any issue related to big data, cloud computing. 

• Apache Samza, by LinkedIn, continued feeding the stream processing stream by focusing on pipelines of real-time data. Samza used Apache Kafka for transporting data and provided a framework for building stream-processing applications

Both of these were weak in their early days, but they have paved the way for advanced systems and educated companies about real-time processing requirements. 

Fig - Streamz: Data Processing Core Flow

The Present of Stream Processing: Maturity and Adoption 

Fast forward to today, and stream processing is mainstream. Data is being generated faster than ever, and the demand for real-time analytics has spread across industries, from e-commerce to healthcare, to finance and beyond. Today's stream-processing systems are more sophisticated, scalable, and reliable, and they are integral to driving modern data architectures. 

The Rise of Apache Kafka 

The most influential development in stream processing has actually come in the form of open-source distributed event streaming that has been derived from Apache Kafka. This was originally developed by LinkedIn way back in 2010, and it later became the de facto standard for real-time data pipelines. 

Kafka is the core node for managing the streaming data that supports multiple systems reading and writing data in real time. Its purpose is high through put and low latency for the handling of a vast amount of data. 

Key Stream Processing Frameworks 

While Kafka deals with the job of transporting the data streams, other frameworks and systems have emerged to enable the users to obtain real-time analytics and processing capabilities. 

• Apache Flink is a distributed, in-memory stream-processing framework that, in practice, performs really well for real-time analytics and event-driven applications. High throughput with very low latency in processing means that Flink can be used in situations where it is absolutely necessary to provide instantaneous insights. More complex event processing functionalities are offered due to the support of stateful stream processing. 

• Amazon Kinesis is a fully managed service from AWS in the cloud. This provides one with a platform for streaming real-time data. Kinesis makes it easy to collect, process, and do analytics of streaming data; thus, giving companies a chance to spend more time building applications rather than thinking about infrastructure. 

Current Use Cases of Stream Processing 

Stream processing is applied to several areas each with unique challenges and opportunities: 

• Real-time fraud detection: Banks make use of stream processing to detect fraud in real time. In this manner, the organization can flag such activities in real time and hence losses are avoided, which otherwise would have occurred at the time of processing. 

• Personalized Recommendations: E-commerce sites are utilizing stream data for giving recommendations on the products related to the user. Stream processing helps an organization process the browsing and purchase behaviour of a user in real time to deliver more relevant content. 

• IoT Data Processing: Due to the increasing popularity of IoT it has become mandatory to use stream processing to address the huge amount of data collected from sensors, devices and machines. Some examples of implementing the technique can be found when it is used in several projects in an industrial setting such as the use of predictive maintenance and real-time monitoring. 

The Future of Stream Processing: The Next Frontier 

It has already transformed the field of data analytics. Soon, tremendous excitement will evolve through stream processing. With increased complexity and size in data, the stream-processing system needs to advance further and evolve with demands and opportunities arising out of such conditions. 

Edge Computing and Stream Processing 

There is also a key future for steam processing in the integration of the edge computing. As the number of connected devices increases, information processing at the network edge will help to cut latency and bandwidth expenses besides improving privacy and security. 

Edge-based stream processing is going to be applied near the source analysis for close to where the data is being created; this is especially so in the case of autonomous vehicles, smart cities, and industrial IoT. 

AI and Stream Processing 

The next exciting area of research has to do with the confluence of stream processing and artificial intelligence. The data is real-time and is used in real-time predicting, real-time anomaly detection, and generating insights based on the models by AI, and machine learning. 

The parameters of the system shall be automatically set, and it will optimize its data streams by the AI-driven stream processing that can locate a pattern in a way the human does not have to sit through an interlude and instead presents the chance for more intelligent decisions. 

Serverless and Autonomous Stream Processing 

This trend of serverless computing is disrupting how developers build stream-processing applications. Serverless platforms such as AWS Lambda and Google Cloud Functions offer totally managed environments for the execution of code against real-time events. Organizations using serverless stream processing can automatically scale applications without managing underlying infrastructure. 

Serverless stream processing would allow companies to automatically scale their applications without having to pay attention to the underlying infrastructure. It will even make it easier to build real-time data processing applications.