Real Time Data Processing

Real Time Data Processing

With real time data processing it is possible to receive alerts as a problem occurs. This leads to faster solutions and a better customer experience.


The power of Stream Processing

Real Time Data Processing

In today's fast-paced and dynamic world, which offers thousands of possibilities and options, it is essential to monitor the delivery of your services as closely as possible to know exactly how your customers perceive your service at the exact moment. Only in this way is it possible to respond immediately to potential quality issues and resolve them in the shortest possible time to minimize the impact.

The real time aspect of data processing means that data is continuously streaming through a pipeline that prepares, enriches, analyses and stores the data in a database. In this way, problems can be identified on the fly and corrected immediately to minimize the impact.

Batch pipelines run periodically, which could mean that data is aggregated and at the end of the day everything is processed at once. The clear disadvantage of this approach is that problems are identified only after a considerable delay and the impact could already be significant.


Let us accompany you on your data-to-value journey

When we speak of data processing we refer to the procedure of taking raw data and turning it into something useful. We support you in all steps of the data-to-value journey.

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1. Data acquisition

We help you acquire the right data at the right time with minimal costs.

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2. Data preparation

In this step the raw information collected is structured in a way so that the subsequent systems are able to process it.

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3. Data enrichment

This step involves a set of microservices that recieve the prepared data and enrich it according to a set of rules.

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4. Analysis

The analysis stage is a set of machine learning models that take as input the enriched and fully prepared data and produce a set of insights as output.

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5. Storage

In the final step the data as well as the insights generated by the machine learning models are stored in databases for future use.