Near Real-Time Analytics with Datavault Builder



Real-time analytics is a powerful asset for any organization, but it requires the right tools and expertise. By leveraging Datavault Builder and Kafka, and with the support of experienced partners like Cimt AG, businesses can transform raw data into valuable insights, enabling timely and informed decision-making.

In our recent webinar, presented by Petr Beles and Daniel Koch, we showcased how to harness the power of near real-time data through practical examples and expert insights. If you missed it, stay tuned for future sessions or reach out to us for more information and personalized guidance on your real-time analytics journey.

Understanding the Challenge

Real-time data, while abundant, can be overwhelming. For instance, data from train delays becomes truly useful only when it is processed, contextualized, and visualized. In this blog, we will explore how to make sense of near real-time train delay data using Kafka and Datavault Builder. This methodology ensures that the data is not only understandable but also actionable.

Step 1: Integrate your Kafka data with other data sources automatically with Datavault Builder

Datavault Builder, a model-driven Data Warehouse (DWH) automation tool, is instrumental in this process. Combined with Kafka, a powerful stream-processing platform, it enables the efficient handling and transformation of real-time data. During the webinar, we walked through the implementation process, showing how to integrate these tools to create a seamless data pipeline.

Step 2: A Real-Time Dashboard Example

To bring theory into practice, we presented a live example using real-time train delay data. This demonstration highlighted how to process, contextualize, and visualize the data on a real-time dashboard. By the end of the session, participants gained a clear understanding of the steps involved and the benefits of real-time analytics.

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