{"id":35537,"date":"2024-05-30T07:22:03","date_gmt":"2024-05-30T07:22:03","guid":{"rendered":"https:\/\/www.mfec.co.th\/?p=35537"},"modified":"2024-07-04T10:07:03","modified_gmt":"2024-07-04T10:07:03","slug":"confluent-real-time-data-monitoring","status":"publish","type":"post","link":"https:\/\/www.mfec.co.th\/en\/tech-talk\/confluent-real-time-data-monitoring\/","title":{"rendered":"Confluent Kafka Real-time data monitoring use case"},"content":{"rendered":"\n

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MFEC x Confluent<\/strong><\/p>\n\n\n\n

MFEC is proud to be a part of the Build with Confluent initiative. By verifying our streaming-based use cases with Confluent, you can have confidence that our Confluent-based service offering is not only built on the leading data streaming platform but also verified by the experts at Confluent.<\/p>\n\n\n\n

Confluent is the data streaming platform <\/strong>that is pioneering a fundamentally new category of data infrastructure that sets data in motion. Confluent\u2019s cloud-native offering is the foundational platform for data in motion \u2013 designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven back-end operations.<\/p>\n\n\n\n

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Real-time data monitoring use case<\/strong><\/p>\n\n\n\n

In the current era, there’s a growing preference for online shopping, necessitating stores to access real-time data for effective inventory and stock management to boost customer satisfaction. Store owners, however, encounter a significant challenge: their data originates from various sources, such as online platforms, physical stores, and third parties, all in different data formats. The question then arises: how can stores efficiently manage this diverse data?<\/p>\n\n\n\n

Confluent Cloud plays a crucial role right from the start by pulling data from multiple sources, including both online and offline stores as well as associated third parties, and aggregating this data within Confluent Cloud. Before forwarding the data to the desired target system, it undergoes stream processing transformation to ensure it aligns with specific business needs.<\/p>\n\n\n\n

Complex data is frequently presented via a visualization dashboard to simplify it for store owners and make it easily understandable and accessible. To address this need, MFEC has created an integration application that retrieves data from Redis. This application processes the data and then pushes it to the dashboard, enabling real-time display and insights.<\/p>\n\n\n\n

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Real-time data allows:<\/strong><\/p>\n\n\n\n

1. The inventory team to immediately know which items are about to run out of stock and which items have the potential to become dead stock.<\/p>\n\n\n\n

2. The marketing team can promote products that are not selling well to boost sales.<\/p>\n\n\n\n

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In this use case example, MFEC has incorporated Confluent into the solution as follows:<\/p>\n\n\n\n

  • Pre-built connector: Change Data Capture (CDC) connector is integrated with Microservice application and databases e.g., store customer, product, and order information. This part ensures real-time data retrieval from each source.<\/li>
  • Stream processing: data transformation using for
    • Joining product list with store inventory to manipulate master product inventory.<\/li><\/ul>
      • Aggregate data to counting remaining of inventory stock<\/li><\/ul>
        • Count the total amount of products sold<\/li><\/ul>
          • Verify orders which order do not have enough inventory in stock<\/li><\/ul><\/li>
          • Integration Application by MFEC: extracting data from Redis to a visualization dashboard (in this example, the data is displayed on Tableau).<\/li><\/ul>\n\n\n\n
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