Design of a Cloud Data Analytics Platform

Our client, a widely branched company with several autonomous organisational units, faced a significant IT and data infrastructure challenge. Each unit operated its own IT and data infrastructure, which made the consolidation and use of all company-wide data an extremely complex matter. Our goal was to develop a unified platform that enabled this data integration without compromising the autonomy of the individual units. In addition, the platform had to cover both development and production operations, which presented another challenge in terms of scalability and flexibility.

With our focus on the use of cloud technologies and our deep understanding of data science and artificial intelligence, we were well equipped to tackle this challenge. We started the process with a comprehensive requirements gathering for the platform in collaboration with all organisational units. This was critical to understanding the specific needs of each unit and developing a solution that was tailored to their individual requirements.

In the next phase, we tested the most important scenarios on possible technologies to ensure that our solution would meet the client's requirements. We chose a modular design based on cloud technologies such as AWS and Azure to make the platform flexible and adaptable.

Data access was enabled both via a central data lake and directly via the source systems of the individual organisational units. This ensured that the autonomy of the units was maintained, while at the same time a central, cloud-based data analysis platform was established.

The end result was a highly flexible, efficient and adaptable cloud data analytics platform, designed on the basis of Collibra and one Azure/AWS stack each. This customised solution enabled all of the client's organisational units to work seamlessly and efficiently with enterprise-wide data without adapting their existing IT infrastructure.

The platform was characterised by its high adaptability, which gave the organisational units maximum flexibility in data use. At the same time, the use of cloud technologies ensured that overall costs were kept low. This is a key advantage when it comes to optimising business efficiency.

Another significant advantage was the end-to-end coverage of all common data use cases. Regardless of the type of data evaluation or analysis required by the different units, our solution was able to meet these requirements.

In summary, we have developed a powerful, cost-effective and flexible cloud-based data analytics platform that enables the client to centralise and optimise its data-driven decisions while preserving the autonomy and individual needs of each organisational unit.

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