Data transmission used to be a one-way process, with large amounts of data being transferred from sender to receiver at once.
Data Streaming solves the challenges of traditional technologies that cannot handle the explosion of data. It enables efficient and continuous transmission of data sets in real time, instead of hours or days of compiling all the data. This involves collecting data from various sources such as an IoT device, a database, or other external sources.
This streaming data can be filtered using algorithms, aggregated, and processed using Big Data technologies - all under one umbrella. It is also possible to train machine learning models with the real time data in order to categorise, segment or classify it.
Main Components of Data Streaming Applications
Data Stream Processing (also called Event Stream Processing) consists of the following main components:
Producers (Sources)
Databases, applications, systems, or devices that produce or collect the data and send it to the processing unit.
Data Stream (Processing Unit)
This is the heart of the data stream, e.g. Apache Kafka. It receives the streaming data and processes it in real time. To accomplish this, high-volume data is managed as a stream to transmit it in easy-to-process chunks (events).
Consumers (Targets)
Databases, applications, analysis tools or other systems that receive the streaming data.
What is Streaming Data?
Streaming data means continuous data streams that are provided in real time. Data istransmitted continuously and processed by customers without the need for acomplete data set. Examples of streaming data are live logging/telemetry data,real time sensor data or financial data from stock exchanges.
Why is StreamingData Important?
Processing and analysing streaming data in real time significantly increases responsiveness and efficiency. Customers nowadays expect a seamless experience, starting from the sales phase all the way to the after-sales process. By using the data sets from the different data sources in the company, services, products, or applications can be improved, and thus customer confidence can be increased.
The application enables more flexible processing of data and faster responsiveness to changing data queries by using event streaming models instead of databases or messaging systems. The event streaming model uses logs instead of databases to record events instead of storing them.
Use cases of Data Streaming
Use Case Mobility | Monitoring Vehicle Data in Real Time
Modern vehicles are equipped with numerous sensors that record data such as speed, fuel consumption, exhaust emissions and much more. By collecting and processing this data, it is possible to improve vehicle performance and detect potential problems at an early stage.
It is also possible to predict the lifespan of components, adjust maintenance intervals and increase vehicle safety. Vehicles equipped with autonomous driving functions can be improved through data collection and potential problems can be detected at an early stage.
Use Case Industry | Monitoring Production Process Data in Real Time
By using data streams, real time data from machines and plants can be collected and analysed in order to optimise processes and avoid breakdowns. Based on the collected sensor data in a factory, machines and plants can be monitored in real time. By detecting potential failures at an early stage, they can be remedied, and production stops can be avoided.
In addition, the generated insights can be used to optimise processes and use resources more efficiently. For example, machine utilisation can be optimised, maintenance intervals can be adjusted, and energy efficiency can be improved.
Through forecasts, it is possible to make predictions about production quantities in order to react quickly to changes in the industry environment and adjust production plans accordingly.
What Should be Considered when Implementing Stream Processing Systems?
To successfully implement stream processing, the following points should be considered:
Data quality
The integrity and quality of the streamed data must be guaranteed. Technologies such as data validation and data cleaning are used for this purpose.
Platform
The choice is between a managed or self-operated platform. Managed platforms, such as those from Confluent, can simplify operations, though this reduces the flexibility and control of the platform. Self-operation, on the other hand, involves more effort and know-how, but offers more opportunities for adjustments and extensions.
Real time data processing
Some data must be processed in real time, while other data can be processed later. It is important to correctly identify the requirements of data streams and the type of processing.
Container orchestration
Using a self-managed platform such as Kubernetes (with Docker) enables the management, control and automation of applications, services and resources in a distributed environment. This can improve the scalability, availability, security and efficiency of applications and services. However, its use requires a certain amount of experience and expertise.
Data storage
To store and process data, large amounts of storage are required. This requires the use of technologies such as NoSQL databases and cloud storage.
Scalability
Processing large data volumes in real time requires the use of technologies that are scalable and fault-tolerant.
Security
Data leaks and attacks must be prevented using technologies such as encryption and authentication.
Latency
Data Streaming Platforms must be able to process real time data. This requires the use of technologies that have low latency.
Operation
A decision must also be made between on-premise and cloud-based solutions. On-premise solutions assume a higher investment in hardware and infrastructure, but usually offer more control and security. Cloud-based solutions, on the other hand, are more flexible and can be deployed faster and scaled more easily depending on the load, but require a stable internet connection and are dependent on the availability and security of the provider. The priority of the company's data sovereignty, e.g. hosting on European servers, should also be considered.
Cost
Stream processing software requires high computing power and storage, which can lead to higher costs. It is important to weigh the costs against the benefits of a Data Streaming Platform.
The Advantages of a Data Streaming Platform
Data stream applications offer a variety of benefits, including:
Process data in real time
Data Streaming makes it possible to capture and process data in real time.
Automation
Stream processing can be used to collect and process data automatically. Processes can be carried out automatically without human intervention. This can increase efficiency and reduce costs.
Increased security
It enables to monitor real time data and detect potential problems at an early stage in order to be able to react quickly. This increases security and reduces the risk of data leaks or attacks.
Better decisions
By collecting and analysing substantial amounts of data, better decisions can be made and problems can be identified early.
Forecasting capability
Forecasts can be made using the collected data. This enables companies to react quickly to changes and remain competitive.
Should You Implement Data Streams? - Conclusion
Data Streaming enables data to be collected, processed, and analysed in real time. It offers companies the opportunity to optimise their business processes, identify problems at an early stage and react faster to changes in the market environment. By using Data Streaming, companies can gain competitive advantages and increase the efficiency of their processes.
When implementing a data stream, it is essential to consider requirements for scalability, data quality, security, latency, and cost. To be successful, it is important to correctly identify the requirements of the data streams and the type of processing needed.
Overall, the use of a Data Streaming application is a key step towards data-driven corporate management, allowing companies to take advantage of processing data in real time.
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