Stream processing technology is increasingly prevalent because it provides superior solutions for many established use cases, such as data analytics, ETL, and transactional applications. It also enables novel applications, software architectures, and business opportunities. With traditional data infrastructures, data and data processing have been omnipresent in businesses for many decades.

Over the years, data collection and usage have grown consistently, and companies have designed and built infrastructures to manage that data. However, the traditional architecture that most businesses implement distinguishes two types of data processing: transactional processing and analytical processing.

Analytics and Data Handling are Changing.

– All this type of new device information enables valuable insights into what is happening on our planet, offering the ability to make accurate and quick decisions. However, analytics and data handling are challenging. Everything is now distributed to the edge, and new ways of handling data are emerging.

– To combat this, IoT uses emerging technologies such as stream data processing with in-stream analytics, predictive analytics, and machine learning techniques. In addition, IoT devices generate vast amounts of data, putting pressure on the internet infrastructure. This is where the role of cloud computing comes in useful. Cloud computing assists in storing, processing, and transferring data in the cloud instead of connected devices.

– Organizations can utilize various technologies and tools to implement event stream processing (ESP). Some popular ESP frameworks include Apache Kafka, Apache Flink, and Apache Storm. These frameworks provide the infrastructure and processing capabilities to handle high-speed data streams and perform real-time analytics. 

IoT Stream Processing: Distributed to the Edge

1: IoT represents a distributed architecture. Analytics are distributed from the IoT platform, either cloud or on-premise, to network edges, making analytics more complicated. A lot of the filtering and analysis is carried out on the gateways and the actual things themselves. These types of edge devices process sensor event data locally.

2: Some can execute immediate local responses without contacting the gateway or remote IoT platform. A device with sufficient memory and processing power can run a lightweight version of an Event Stream Processing ( ESP ) platform.

3: For example, Raspberry PI supports complex-event processing ( CEP ). Gateways ingest event streams from sensors and usually carry out more sophisticated steam processing than the actual thing. Some can send an immediate response via a control signal to actuators, causing a state change.

Technicality is only one part of the puzzle; data ownership and governance are the other. 

Time Series Data – Data in Motion

In specific IoT solutions, such as traffic light monitoring in intelligent cities, reaction time must be immediate without delay. This requires a different type of big data solution that processes data while it’s in motion. In some IoT solutions, there is too much data to store, so the analysis of data streams must be done on the fly while being transferred.

It’s not just about capturing and storing as much data as possible anymore. The essence of IoT is the ability to use the data while it is still in motion. Applying analytical models to data streams before they are forwarded enables accurate pattern and anomaly detection while they are occurring. This analysis offers immediate insight into events, enabling quicker reaction times and business decisions. 

Traditional analytical models are applied to stored data, offering analytics for historical events only. IoT requires the examination of patterns before data is stored, not after. The traditional store and process model does not have the characteristics to meet the real-time analysis of IoT data streams.

In response to new data handling requirements, new analytical architectures are emerging. The volume and handling of IoT traffic require a new type of platform known as Event Stream Processing ( ESP ) and Distributed Computing Platforms ( DCSP )

Event Stream Processing ( ESP ) 

ESP is an in-memory, real-time process technique that enables the analysis of continuously flowing events in data streams. Assessing events in motion is known as “event streams.” This reveals what is happening now and can be used with historical data to predict future events accurately. Predictive models are embedded into the data streams to predict future events.

This type of processing represents a shift in data processing. Data is no longer stored and processed; it is analyzed while still being transferred, and models are applied.

ESP & Predictive Analytics Models

ESP applies sophisticated predictive analytics models to data streams and then takes action based on those scores or business rules. It is becoming popular in IoT solutions for predictive asset maintenance and real-time fault detection.

For example, you can create models that signal a future unplanned condition. This can then be applied to ESP, quickly detecting upcoming failures and interruptions. ESP is also commonly used in network optimization of the power grid and traffic control systems.

ESP – All Data in RAM

ESP is in-memory, meaning all data is loaded into RAM. It does not use hard drives or substitutes, resulting in fast processing, enhanced scale, and analytics. In-memory can analyze terabytes of data in just a few seconds and can ingest from millions of sources in milliseconds. All the processing happens at the system’s edge before data is passed to storage.

How you define real-time depends on the context. Your time horizon will depict whether you need the full power of ESP. Events with ESP should happen close together in time and frequency. However, if your time horizon is over a relatively long period and events are not close together, your requirements might be fulfilled with Batch processing. 

**Batch vs Real-Time Processing**

With Batch processing, files are gathered over time and sent together as a batch. It is commonly used when fast response times are not critical and for non-real-time processing. Batch jobs can be stored for an extended period and then executed; for example, an end-of-day report is suited for batch processing as it does not need to be done in real-time.

They can scale, but the batch orientation limits real-time decision-making and IoT stream requirements. Real-time processing involves a continual input, process, and output of data. Data is processed in a relatively short period. When your solution requires immediate action, real-time is the one for you. Examples of batch and real-time solutions include Hadoop for batch and Apache Spark, focusing on real-time computation.

**Hadoop vs. Apache Spark** 

Hadoop is a distributed data infrastructure that distributes data collections across nodes in a cluster. It includes a storage component called Hadoop Distributed File System ( HDFS ) and a processing component called MapReduce. However, with the new requirements for IoT, MapReduce is not the answer for everything.

MapReduce is fine if your data operation requirements are static and you can wait for batch processing. But if your solution requires analytics from sensor streaming data, then you are better off using Apache Spark. Spark was created in response to MapReduce’s limitations.

Apache Spark does not have a file system and may be integrated with HDFS or a cloud-based data platform such as Amazon S3 or OpenStack Swift. It is much faster than MapReduce and operates in memory and real-time. In addition, it has machine learning libraries to gain insights from the data and identify patterns. Machine learning can be as simple as a Python event and anomaly detection script.

Closing Points on Event Stream Processing 

Event Stream Processing is a computing paradigm focused on the real-time analysis of data streams. Unlike traditional batch processing, which involves collecting data and analyzing it periodically, ESP allows for immediate insights as data flows in. This capability is crucial for applications that demand instantaneous action, such as fraud detection in financial transactions or monitoring network security.

The architecture of an Event Stream Processing system typically comprises several key components:

1. **Event Sources**: These are the origins of the data streams, which could be sensors, user actions, or any system generating continuous data.

2. **Stream Processor**: The core of ESP, where the actual computation and analysis occur. It processes the data in real-time, applying various transformations and detecting patterns.

3. **Data Sink**: This is where the processed data is delivered, often to databases, dashboards, or triggering subsequent actions.

4. **Messaging System**: A crucial part of ESP, it ensures that data flows seamlessly from the source to the processor and finally to the sink.

The versatility of ESP makes it applicable across numerous industries:

– **Finance**: ESP is used in algorithmic trading, risk management, and fraud detection, providing real-time insights that drive decisions.

– **Healthcare**: In monitoring patient vitals and managing hospital operations, ESP enables timely interventions and improved resource allocation.

– **Telecommunications**: ESP helps in network monitoring, ensuring optimal performance and quick resolution of issues.

– **E-commerce**: Companies use ESP to personalize user experiences by analyzing browsing and purchasing behaviors in real-time.

Implementing ESP can be challenging due to the need for reliable and scalable infrastructure. Key considerations include:

– **Latency**: Minimizing the delay between data generation and analysis is crucial for effectiveness.

– **Scalability**: As data volumes increase, the system must efficiently handle larger streams without performance degradation.

– **Data Quality**: Ensuring data accuracy and consistency is essential for meaningful analysis.