What is Stream Processing and Analytics?

Stream processing is a technology used to process large amounts of data in real-time as it is generated rather than storing it and processing it later.

Think of it like a conveyor belt in a factory. The conveyor belt constantly moves, bringing in new products that need to be processed. Similarly, stream processing deals with data that is constantly flowing, like a stream of water. Just like the factory worker needs to process each product as it moves along the conveyor belt, stream processing technology processes each piece of data as it arrives.

Stateful and stateless processing are two different approaches to stream processing, and the right choice depends on the specific requirements and needs of the application. 

Stateful processing is useful in scenarios where the processing of an event or data point depends on the state of previous events or data points. For example, it can be used to maintain a running total or average across multiple events or data points.

Stateless processing, on the other hand, is useful in scenarios where the processing of an event or data point does not depend on the state of previous events or data points. For example, in a simple data transformation application, stateless processing can be used to transform each event or data point independently without the need to maintain state.

Streaming analytics refers to the process of analyzing and processing data in real time as it is generated. Streaming analytics enable applications to react to events and make decisions in near real time.

Why Stream Processing and Analytics?

Stream processing is important because it allows organizations to make real-time decisions based on the data they are receiving. This is particularly useful in situations where timely information is critical, such as in financial transactions, network security, and real-time monitoring of industrial processes.

For example, in financial trading, stream processing can be used to analyze stock market data in real time and make split-second decisions to buy or sell stocks. In network security, it can be used to detect and respond to cyber-attacks in real time. And in industrial processes, it can be used to monitor production line efficiency and quickly identify and resolve any issues.

Stream processing is also important because it can process massive amounts of data, making it ideal for big data applications. With the growth of the Internet of Things (IoT), the amount of data being generated is growing rapidly, and stream processing provides a way to process this data in real time and derive valuable insights.

Collectively, stream processing provides organizations with the ability to make real-time decisions based on the data they are receiving, allowing them to respond quickly to changing conditions and improve their operations.

How is it different from Batch Processing?

Batch Data Processing:

Batch Data Processing is a method of processing where a group of transactions or data is collected over a period of time and is then processed all at once in a "batch". The process begins with the extraction of data from its sources, such as IoT devices or web/application logs. This data is then transformed and integrated into a data warehouse. The process is generally called the Extract, Transform, Load (ETL) process. The data warehouse is then used as the foundation for an analytical layer, which is where the data is analyzed, and insights are generated.

Stream/Real-time Data Processing:

Real-Time Data Streaming involves the continuous flow of data that is generated in real-time, typically from multiple sources such as IoT devices or web/application logs. A message broker is used to manage the flow of data between the stream processors, the analytical layer, and the data sink. The message broker ensures that the data is delivered in the correct order and that it is not lost. Stream processors used to perform data ingestion and processing. These processors take in the data streams and process them in real time. The processed data is then sent to an analytical layer, where it is analyzed, and insights are generated. 

Processes involved in Stream processing and Analytics:

The process of stream processing can be broken down into the following steps:

• Data Collection: The first step in stream processing is collecting data from various sources, such as sensors, social media, and transactional systems. The data is then fed into a stream processing system in real time.
• Data Ingestion: Once the data is collected, it needs to be ingested or taken into the stream processing system. This involves converting the data into a standard format that can be processed by the system.
• Data Processing: The next step is to process the data as it arrives. This involves applying various processing algorithms and rules to the data, such as filtering, aggregating, and transforming the data. The processing algorithms can be applied to individual events in the stream or to the entire stream of data.
• Data Storage: After the data has been processed, it is stored in a database or data warehouse for later analysis. The storage can be configured to retain the data for a specific amount of time or to retain all the data.
• Data Analysis: The final step is to analyze the processed data and derive insights from it. This can be done using data visualization tools or by running reports and queries on the stored data. The insights can be used to make informed decisions or to trigger actions, such as sending notifications or triggering alerts.

It's important to note that stream processing is an ongoing process, with data constantly being collected, processed, and analyzed in real time. The visual representation of this process can be represented as a continuous cycle of data flowing through the system, being processed and analyzed at each step along the way.

Stream Processing Platforms & Frameworks:

Stream Processing Platforms & Tools are software systems that enable the collection, processing, and analysis of real-time data streams.

Stream Processing Frameworks:

A stream processing framework is a software library or framework that provides a set of tools and APIs for developers to build custom stream processing applications. Frameworks typically require more development effort and configuration to set up and use. They provide more flexibility and control over the stream processing pipeline but also require more development and maintenance resources. 

Examples: Apache Spark Streaming, Apache Flink, Apache Beam, Apache Storm, Apache Samza

Let’s first look into the most commonly used stream processing frameworks: Apache Flink & Apache Spark Streaming.

Apache Flink : 

Flink is an open-source, unified stream-processing and batch-processing framework. Flink executes arbitrary dataflow programs in a data-parallel and pipelined manner, making it ideal for processing huge amounts of data in real-time.

• Flink provides out-of-the-box checkpointing and state management, two features that make it easy to manage enormous amounts of data with relative ease.
• The event processing function, the filter function, and the mapping function are other features that make handling a large amount of data easy.

Flink also comes with real-time indicators and alerts which make abig difference when it comes to data processing and analysis.

Note: We have discussed the stream processing and analytics in detail in Stream Processing and Analytics with Apache Flink

Apache Spark Streaming : 

Apache Spark Streaming is a scalable fault-tolerant streaming processing system that natively supports both batch and streaming workloads. Spark Streaming is an extension of the core Spark API that allows data engineers and data scientists to process real-time data.

• Great for solving complicated transformative logic
• Easy to program
• Runs at blazing speeds
• Processes large data within a fraction of second

Stream Processing Platforms:

A stream processing platform is an end-to-end solution for processing real-time data streams. Platforms typically require less development effort and maintenance as they provide pre-built tools and functionality for processing, analyzing, and visualizing data. 

Examples: Apache Kafka, Amazon Kinesis, Google Cloud Pub-Sub

Let’s look into the most commonly used stream processing platforms: Apache Kafka & AWS Kinesis.

Apache Kafka: 

Apache Kafka is an open-source distributed event streaming platform for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

• Because it’s an open-source, “Kafka generally requires a higher skill set to operate and manage, so it’s typically used for development and testing.
• APIs allow “producers” to publish data streams to “topics;” a “topic” is a partitioned log of records; a “partition” is ordered and immutable; “consumers” subscribe to “topics.”
•  It can run on a cluster of “brokers” with partitions split across cluster nodes. 
• Messages can be effectively unlimited in size (2GB). 

‍AWS Kinesis:

Amazon Kinesis is a cloud-based service on Amazon Web Services (AWS) that allows you to ingest real-time data such as application logs, website clickstreams, and IoT telemetry data for machine learning and analytics, as well as video and audio. 

• Amazon Kinesis is a SaaS offering, reducing the complexities in the design, build, and manage stages compared to open-source Apache Kafka. It’s ideally suited for building microservices architectures. 
• “Producers” can push data as soon as it is put on the stream.  Kinesis breaks the stream across “shards” (which are like partitions). 
• Shards have a hard limit on the number of transactions and data volume per second. If you need more volume, you must subscribe to more shards. You pay for what you use.
•  Most maintenance and configurations are hidden from the user. Scaling is easy (adding shards) compared to Kafka. 
• Maximum message size is 1MB.

Three Characteristics of Event Stream processing Platform:

Publish and Subscribe:

In a publish-subscribe model, producers publish events or messages to streams or topics, and consumers subscribe to streams or topics to receive the events or messages. This is similar to a message queue or enterprise messaging system. It allows for the decoupling of the producer and consumer, enabling them to operate independently and asynchronously. 

Store streams of events in a fault-tolerant way

This means that the platform is able to store and manage events in a reliable and resilient manner, even in the face of failures or errors. To achieve fault tolerance, event streaming platforms typically use a variety of techniques, such as replicating data across multiple nodes, and implementing data recovery and failover mechanisms.

Process streams of events in real-time, as they occur

This means that the platform can process and analyze data as it is generated rather than waiting for data to be batch-processed or stored for later processing.

Challenges when designing the stream processing and analytics solution:

Stream processing is a powerful technology, but there are also several challenges associated with it, including:

• Late arriving data: Data that is delayed or arrives out of order can disrupt the processing pipeline and lead to inaccurate results. Stream processing systems need to be able to handle out-of-order data and reconcile it with the data that has already been processed.
• Missing data: If data is missing or lost, it can impact the accuracy of the processing results. Stream processing systems need to be able to identify missing data and handle it appropriately, whether by skipping it, buffering it, or alerting a human operator.
• Duplicate data: Duplicate data can lead to over-counting and skewed results. Stream processing systems need to be able to identify and de-duplicate data to ensure accurate results.
• Data skew: data skew occurs when there is a disproportionate amount of data for certain key fields or time periods. This can lead to performance issues, processing delays, and inaccurate results. Stream processing systems need to be able to handle data skew by load balancing and scaling resources appropriately.
• Fault tolerance: Stream processing systems need to be able to handle hardware and software failures without disrupting the processing pipeline. This requires fault-tolerant design, redundancy, and failover mechanisms.
• Data security and privacy: Real-time data processing often involves sensitive data, such as personal information, financial data, or intellectual property. Stream processing systems need to ensure that data is securely transmitted, stored, and processed in compliance with regulatory requirements.
• Latency: Another challenge with stream processing is latency or the amount of time it takes for data to be processed and analyzed. In many applications, the results of the analysis need to be produced in real-time, which puts pressure on the stream processing system to process the data quickly.
• Scalability: Stream processing systems must be able to scale to handle large amounts of data as the amount of data being generated continues to grow. This can be a challenge because the systems must be designed to handle data in real-time while also ensuring that the results of the analysis are accurate and reliable.
• Maintenance: Maintaining a stream processing system can also be challenging, as the systems are complex and require specialized knowledge to operate effectively. In addition, the systems must be able to evolve and adapt to changing requirements over time.

Despite these challenges, stream processing remains an important technology for organizations that need to process data in real time and make informed decisions based on that data. By understanding these challenges and designing the systems to overcome them, organizations can realize the full potential of stream processing and improve their operations.

Key benefits of stream processing and analytics:

• Real-time processing keeps you in sync all the time:

For Example: Suppose an online retailer uses a distributed system to process orders. The system might include multiple components, such as a web server, a database server, and an application server. The different components could be kept in sync by real-time processing by processing orders as they are received and updating the database accordingly. As a result, orders would be accurate and processed efficiently by maintaining a consistent view of the data.

• Real-time data processing is More Accurate and timely:

For Example a financial trading system that processes data in real-time can help to ensure that trades are executed at the best possible prices, improving the accuracy and timeliness of the trades. 

• Deadlines are met with Real-time processing:

For example: In a control system, it may be necessary to respond to changing conditions within a certain time frame in order to maintain the stability of the system. 

• Real-time processing is quite reactive:

For example, a real-time processing system might be used to monitor a manufacturing process and trigger an alert if it detects a problem or to analyze sensor data from a power plant and adjust the plant's operation in response to changing conditions.

• Real-time processing involves multitasking:

For example, consider a real-time monitoring system that is used to track the performance of a large manufacturing plant. The system might receive data from multiple sensors and sources, including machine sensors, temperature sensors, and video cameras. In this case, the system would need to be able to multitask in order to process and analyze data from all of these sources in real time and to trigger alerts or take other actions as needed. 

• Real-time processing works independently:

For example, a real-time processing system may rely on a database or message queue to store and retrieve data, or it may rely on external APIs or services to access additional data or functionality.

Use case studies:

There are many real-life examples of stream processing in different industries that demonstrate the benefits of this technology. Here are a few examples:

• Financial Trading: In the financial industry, stream processing is used to analyze stock market data in real time and make split-second decisions to buy or sell stocks. This allows traders to respond to market conditions in real time and improve their chances of making a profit.
• Network Security: Stream processing is also used in network security to detect and respond to cyber-attacks in real-time. By processing network data in real time, security systems can quickly identify and respond to threats, reducing the risk of a data breach.
• Industrial Monitoring: In the industrial sector, stream processing is used to monitor production line efficiency and quickly identify and resolve any issues. For example, it can be used to monitor the performance of machinery and identify any potential problems before they cause a production shutdown.
• Social Media Analysis: Stream processing is also used to analyze social media data in real time. This allows organizations to monitor brand reputation, track customer sentiment, and respond to customer complaints in real time.
• Healthcare: In the healthcare industry, stream processing is used to monitor patient data in real time and quickly identify any potential health issues. For example, it can be used to monitor vital signs and alert healthcare providers if a patient's condition worsens.

These are just a few examples of the many real-life applications of stream processing. Across all industries, stream processing provides organizations with the ability to process data in real time and make informed decisions based on the data they are receiving.

How to start stream analytics?

• Our recommendation in building a dedicated platform is to keep the focus on choosing a diverse stream processor to pair with your existing analytical interface. 
• Or, keep an eye on vendors who offer both stream processing and BI as a service.

Resources:

Here are some useful resources for learning more about stream processing:

Videos:

• Velotio Tech Shorts | Stream Processing & Analytics : Velotio Tech Shorts | Stream Processing & Analytics
• Velotio tech Shorts | Streaming Analytics using Flink Velotio Tech Shorts | Streaming Analytics Using Flink
• Apache Kafka: https://www.youtube.com/watch?v=0IADOFI3uV4&ab_channel=Confluent
• Apache Flink: https://www.youtube.com/watch?v=UyJ6UzorX9s&ab_channel=FlinkForward
• Apache Spark Streaming: https://www.youtube.com/watch?v=dbBgKjhKGOA&ab_channel=ApacheSpark

Tutorials:

• Stream Processing with Apache Kafka: https://kafka.apache.org/quickstart
• Getting Started with Apache Flink: https://flink.apache.org/docs/stable/getting-started/
• Apache Spark Streaming: https://spark.apache.org/docs/latest/streaming-programming-guide.html

Articles:

• Stream Processing: https://en.wikipedia.org/wiki/Stream_processing
• What is Stream Processing and Why is it Important?: https://databaseline.com/what-is-stream-processing-and-why-is-it-important/
• An Introduction to Stream Processing: https://blog.softwaremill.com/an-introduction-to-stream-processing-39f1ee0c36d

These resources will provide a good starting point for learning more about stream processing and how it can be used to solve real-world problems. 

Conclusion:

Real-time data analysis and decision-making require stream processing and analytics in diverse industries, including finance, healthcare, and e-commerce. Organizations can improve operational efficiency, customer satisfaction, and revenue growth by processing data in real time. A robust infrastructure, skilled personnel, and efficient algorithms are required for stream processing and analytics. Businesses need stream processing and analytics to stay competitive and agile in today's fast-paced world as data volumes and complexity continue to increase.