How to Leverage Data Streaming in Modern Data Architecture?



According to the Forrester study, approximately 60% to 73% of enterprise data is not used for analysis. This may harm the organization.  If your company is facing a similar problem, you should consider data engineering services to get the most out of your data. In this article, we will look at how these data resources can be effectively utilized through data streaming in a modern data architecture.

Data streaming

Data streams represent an uninterrupted and dynamic flow of data that undergoes real-time processing and immediate access. This can be data from various sources, such as:

  • IoT sensors
  • Financial transactions
  • User interactions
  • Or any data generated within the organization

To illustrate how data streaming works, it is best to compare it to a river. Looking at it, it is difficult to find its beginning and end, and the water keeps flowing without slowing down, even for a moment. This is the same as how data works. Data streaming is based on their continuous flow. These data streams find applications in real-time monitoring, analysis, reporting, and decision-making. Explore the power of data streaming with expert Data Engineering Services to harness real-time insights and enhance your business.

Modern data architecture

Modern data architecture refers to an innovative approach to managing, storing, processing, and using data in organizations. This concept is particularly important in the digital era, where the amount of generated data is growing exponentially. Its aim is to enable companies to improve analysis and decision-making and achieve their business goals.

Here are some key elements of a modern data architecture:

  • Cloud data storage
  • Storing and processing large amounts of data from different sources
  • Using distributed data processing technologies such as Apache Hadoop or Apache Spark
  • Real-time data analysis
  • Using a variety of data management technologies and tools. For example, NoSQL databases, stream processing tools, and data visualization tools
  • Ensuring data security, both during storage, processing, and transmission, is a key aspect
  • Integrating data from a variety of sources, including external and internal
  • Using automation to manage, process, and generate reports

The use of data streaming in modern data architecture

Data streaming is a key element of modern data architecture. It enables organizations to access real-time data and ongoing analytics. Here are some ways you can leverage data streaming in a modern data architecture.


Data streaming enables the continuous collection of data from various types of sensors and IoT devices, such as

  • Temperature and humidity sensors
  • GPS devices
  • Cameras

Thanks to this, organizations can monitor and analyze data on an ongoing basis. This is crucial in the case of monitoring systems, network traffic analysis, or failure response.


Streaming data is processed on an ongoing basis. This means that you can apply various data analysis techniques almost immediately after collecting the data. It could be:

  • Filtering and transformations

You can use filters to capture only the data you are interested in. Moreover, you can perform transformations on the fly by converting measurement units, for example.

  • Anomaly detection

Stream processing is also used to detect anomalies in real-time data. It is especially important in security and monitoring applications.


To process streaming data, you can use tools such as Apache Kafka, Apache Flink, and Apache Storm, or cloud solutions such as AWS Kinesis or Azure Stream Analytics. These tools offer various features such as scalability, fault tolerance, and support for multiple data sources.

  • Apache Kafka

It is a popular platform for transmitting and streaming data. It enables scalable, durable, and highly available stream processing.

  • Apache Flink

This is a stream processing tool that offers advanced processing capabilities.

  • AWS Kinesis and Azure Stream Analytics

Cloud platforms like AWS and Azure offer data streaming services that are easy to configure and scale.


Streaming data undergoes on-the-fly processing. However, data can also be saved to data stores such as Apache Cassandra, Apache HBase, or traditional SQL databases, or cloud storage. These warehouses allow for longer storage of data and subsequent analysis.


Streaming data can be easily integrated with

  • Data analytics tools such as Apache Spark, and Apache Beam
  • Or cloud analytics platforms such as AWS Redshift or Azure Synapse Analytics

With it, organizations can perform advanced analyses and generate reports. They can also create predictive models and draw valuable conclusions.


A key element of using data streaming is monitoring and managing streams. Monitoring tools such as Prometheus, Grafana, or specialized tools provided by stream processing platforms help track data performance, availability, and quality.


It is also worth ensuring appropriate security for streaming data, such as:

  • Encryption
  • Authentication
  • Access management

Additionally, in some cases, such as in the financial industry, there are special regulatory requirements for the processing and storage of streaming data.


In summary, data streaming is a key element of modern data architectures. It enables organizations to effectively leverage their information assets. It has been used in many areas, such as IoT data collection, storage in data warehouses, and integration with analytical tools. With these applications, data streaming becomes a key tool supporting effective data management. 

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