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What are some tips for designing system architecture for real-time data processing?

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Real-time data processing is the ability to capture, analyze, and act on data as soon as it is generated, without any delay or latency. This can provide valuable insights and benefits for various applications, such as fraud detection, event processing, streaming analytics, and IoT. However, designing a system architecture for real-time data processing can be challenging, as it requires balancing performance, scalability, reliability, and cost. In this article, we will share some tips for designing system architecture for real-time data processing, based on the following aspects:

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Shaswat Raj

System Architect at Merck Life Science

1 Data sources and formats

The first step is to identify the data sources and formats that you need to process in real-time. Data sources can be internal or external, such as sensors, databases, APIs, webhooks, or third-party services. Data formats can be structured, semi-structured, or unstructured, such as JSON, XML, CSV, or binary. You need to understand the volume, velocity, and variety of the data, as well as the quality and consistency. This will help you choose the appropriate data ingestion and integration methods, such as batch, streaming, or hybrid.

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Shaswat Raj

System Architect at Merck Life Science
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Real Time data handling architecture depend upon 1. Criticality of Real Time Data: it depends on the Latency period , Security of data, Accuracy & Precision, Alert & Alarm in Case of failure of data .Scalability etc. i.e. Industrial data are of higher criticality vs chat device even though both are real time. 2. Purpose of use: where it has to be used for Analytics purpose, P&ID display, Dashboard , Chat device etc will change the Architecture model 3. Source and Format:If data is unstructured or structured or Binary set coming from Industrial PLC or Interaction between SERVICES using API. 4. Destination of Data : Data has to be stored in NO SQL or SQL data base and for Historian, sampling rate for data storage.

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2 Data processing and analysis

The next step is to define the data processing and analysis logic that you want to apply to the data in real-time. Data processing can involve filtering, transforming, enriching, aggregating, or joining the data, depending on the business requirements and use cases. Data analysis can involve applying statistical, machine learning, or complex event processing techniques to the data, such as anomaly detection, sentiment analysis, pattern recognition, or event correlation. You need to consider the latency, accuracy, and complexity of the data processing and analysis, as well as the tools and frameworks that you want to use, such as Apache Spark, Apache Flink, Apache Kafka, or AWS Lambda.

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3 Data storage and access

The third step is to design the data storage and access layer that will store and serve the data in real-time. Data storage can be persistent or ephemeral, depending on the retention and durability needs of the data. Data access can be synchronous or asynchronous, depending on the query and response needs of the data. You need to choose the appropriate data storage and access technologies, such as relational databases, NoSQL databases, data warehouses, data lakes, or in-memory databases. You also need to consider the scalability, availability, and security of the data storage and access layer, as well as the data governance and quality policies.

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4 Data visualization and action

The final step is to design the data visualization and action layer that will present and act on the data in real-time. Data visualization can involve creating dashboards, reports, charts, or alerts that will display the data in a meaningful and interactive way. Data action can involve triggering workflows, notifications, or decisions based on the data. You need to choose the appropriate data visualization and action tools and platforms, such as Tableau, Power BI, Grafana, or Zapier. You also need to consider the user experience, feedback, and collaboration of the data visualization and action layer, as well as the performance and reliability of the data delivery.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are some tips for designing system architecture for real-time data processing?

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3

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5

1 Data sources and formats

2 Data processing and analysis

3 Data storage and access

4 Data visualization and action

5 Here’s what else to consider

System Architecture

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What are some tips for designing system architecture for real-time data processing?

1

2

3

4

5

1 Data sources and formats

2 Data processing and analysis

3 Data storage and access

4 Data visualization and action

5 Here’s what else to consider

System Architecture

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