The series of data from different sources that are fed to a common source is called data streaming. Out of all the possible data analytics solutions available in the market, it is a necessity for many businesses. Let us know all about data streaming- its features, examples, its importance, data stream processing, and many more.
Features of data streaming:
The key features of data streams that separate them from other verticals include:
- Continuous: It is the main feature of any data stream. While there is no beginning or end of the data stream, these are always in real-time. Hence, any regular data stream should be break-free, and the same can be accessed and analyzed according to the system requirements.
- Imperfection: The continuous data streams are full of imperfection. The data elements are often out of order, and they may further have missing data elements or damaged data elements.
- Unrepeated or Highly Volatile: There is never a repeat of real-time data streaming. Further, the uniqueness of the data streams makes it highly volatile, and the new data is never the same as the previous data. Hence, data streams are recorded in real-time and can be processed at any time for getting quick insights.
- Time-sensitive: The timestamp in the data stream ensures that it remains time-sensitive and can be analyzed accordingly. Further, it makes the data streaming feasible from a security point of view as any urgent data patterns can be handled accordingly.
- Different contributing sources: The data streams can be from multiple sources that are located far away from each other. This difference in the sources further makes the data stream a mix of different formats.
Examples of data streaming:
The main examples of data streaming include:
- Stock market monitors: The real-time finance data from the stock markets are always transmitted in a data stream. The processing of this data that includes current market trends and stock prices help companies make quick decisions.
- Process monitors: Many manufacturing companies involve different levels of processes that require strict monitoring. The data thus generated from multiple systems can be used to monitor the processes and involve necessary improvements according to the requirements. Hence, it becomes easy for the companies to assess risk in production and avoid any outages.
- Logs management: The activity logs generated by the web browsers using real-time internet data are useful for deciding the course of the different marketing activities. Further, the real-time data generated from online financial transactions, credit card purchases, etc., are useful for real-time actions to reduce the downtime in different services.
- Internet of Things (IoT): There are multiple IoT devices like privacy systems, cybersecurity, smart home appliances, wearable health monitors, etc., that produce data streams. These streams are recorded and analyzed at required times to generate useful insights that are one of the biggest uses of IoT devices.
Why is data streaming important?
It is crucial for any business as the amount of data is increasing daily. This increase in volume comes at additional costs, and hence the only solution is to process the data in real-time.
The data streaming is also crucial as the incoming sources are numerous, including IoT, financial data, sales data, marketing data, customer experience data, etc. Hence, it is difficult to prioritize single feature data from the others.
How is data streaming processed?
The data stream processing collects, analyzes, and visualizes the continuous data flow. In other words, data stream processing is the key step to draw valuable insights from the incoming data streams.
Why is data stream processing important?
The main factors that stress the need for the data stream processing or data processors include:
- Scalability: The data stream varies in size due to its discontinuous and volatile nature. Hence, many times the system gets small amounts of data, while at other times, it may see a surge in data streams. Hence, data processors should respond with high accuracy to these scalability needs.
- Availability: The data stream processor should be available even if the components fail to respond due to any reason. Hence, the data processor should be fault-tolerant or available at all times to ensure the proper worth of the data streaming. Further, the data processor should quickly collect, process, and passes the data to the next level for a quick presentation.
- High-speed relevancy: High-speed data stream processing may seem like the primary concern, but the relevancy or accuracy is the real game-changer. Any delay in the processing of the data stream may cause a loss of relevancy of the real-time data. Further, the data processor should be accurate and should never miss a single stream that causes accuracy issues.
Constituents of the stream processor:
The main components of the stream processor include:
- Complex event processing: It is the main use case for all IoT device’s data streams. While IoT devices generate multiple data streams, the processor’s main work is to work on the event processing. It should locate the defined events, conclude meaningful insights, and feeds information to the next level for quick action according to the insights.
- Datastream management: It is another main component that is required to build models or generate an incoming data summary. It can be used to evaluate the user’s preference based on user click data, create a list of facial figures based on a continuous stream of facial data, etc.
Architecture of the stream processor
The simple architecture of the stream processor must include:
- Data collection: The source clients are the data collection points that collaborate the data from multiple source clients, send the data in a motion, and use a centralized buffer for quick aggregation of the generated data.
- Storage and preparation: These are the two main systems of any stream processor. The storage system maintains a record of the input stream data that is useful for future references. The presentation system includes high levels of analytical systems and alerts and is used for data visualization to end-users.
- Data generation: It resembles the various sources of raw data that include transaction monitors, web browsers, sensors, etc. These sources generate the data streams that are used in the processors.
- Message buffering: The data stream from the aggregation agent is stored in the message buffering before it is fed to the logic processor. The message buffering is available in two types, i.e., queue-based and topic-based. The queue-based messaging buffer system reads from a single producer and delivers to the single data customer. The topic-based messaging buffer system stores the incoming data stream in the form of records called topics and is created by one or more data producers.
- Message broker: It consists of the data collection, data aggregation, and message buffering system. It aggregates the data streams from different sources, formats the data streams, and feeds the same to the continuous logic processing.
- Continuous logic processing: It is the brainchild of the entire architecture of the data stream processor. It runs multiple pre-defined queries on the incoming data streams that are used to conclude the useful insights. It offers scalability and fault tolerance.
How does data stream processing differ from batch data processing?
The main difference between data stream processing and batch data processing includes:
|Sr. No.||Data Stream Processing||Batch Data Processing|
|1||Data is not stored||Data is stored in a warehouse|
|2||Data is processed in real-time only||Data is processed in batches|
|3||It uses immediate data||It uses historical data|
|4||The data used is highly critical and volatile||The used is not critical|
|5||It is a quick process||It is a slow process|
|6||It has reduced infrastructure requirements||It has significant infrastructure needs|
Advantages of data streaming and data stream processing:
The benefits of data streaming and processing for any business include:
- Reduced infrastructure costs: There is no need to store data in warehouses when the data streaming and processing have reduced hardware costs.
- Improves customer satisfaction: All the customer issues are resolved on time due to the real-time data stream processing.
- Improves competitiveness: The issues are resolved well in advance before the situations become worse, and hence it improves the competitive situation of the business.
- Reduced preventable losses: It is possible to reduce the manufacturing issues, financial meltdowns, security breaches, social image disruption, customer dissatisfaction, etc., using data streaming and processing.
- Improved returns: The improved response times to different situations help businesses generate high returns compared to the businesses having no data streaming or processing systems.
Possible challenges in data streaming:
Like any other technology, data streaming has its limitations. Some of the possible challenges in data streaming and its easy resolution includes:
- Fault tolerance: The data streaming should be fault-tolerant. The chances of downtime affect the accuracy of the data streaming. This problem can be resolved by ensuring that all data streaming processors are highly faulted tolerance irrespective of the system components’ conditions.
- Volume and diversity challenges: The data streaming is in large volumes and further comes from different sources. This remains a strong challenge to any data streaming processor. This problem can be resolved by improving the message buffer and message broker in the processor’s architecture.
To sum up, data streaming is an important function of many modern businesses. The data stream processing is used to draw optimized insights from incoming data. It is impossible for any business to ignore the benefits of data stream processing.