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Data Engineering, DevOps (K8s, OS), AI

Justin Güse, Data Engineer @ DataFortress.cloud

From Dusty Data to Revenue Gold – We’re Here to Help

Many companies struggle to unlock the potential of their data – that's where we come in.

While AWS and public clouds can be costly and are often restricted by German finance and healthcare regulations, we offer secure Kubernetes hosting solutions.

With a proven track record working with enterprises like VW, HPE, Porsche, and major banks, we can transform your data into valuable revenue.

Integration of a new software into the existing enterprise material sampling process, improving the speed to market of new models and reducing the time spent on the sampling process in general.

Volkwagen / HPEEnterprise Solution Architecture

Replacing Hadoop with a Data Warehouse built on top of Trino, built with an autoscaling microservice architecture to handle finance data of millions of German customers.

Atruvia (Finance)Data Engineer / DevOps

Solution Architecture and creation of a data warehouse fulfilling German healthcare regulations, including an ETL pipeline for medical data of the leading fasting clinic, to provide personalised app based suggestions to improve the treatment.

Buchinger Wilhelmi (Healthcare)Data Engineering / K8s Administration

Solution Architecture for the world wide backup system of VM based systems, including planning of network routing limitations in AWS/Google Cloud/Azure.

BMW (automotive)Enterprise Solution Architecture

Unlock Your Customer Data's True Potential for Enhanced Experience and New Revenue Streams

DSGVO, HIPAA, VDA-standards experience

As a German based company we have an extensive knowledge about finance, healthcare and automotive regulations and compliance.

 

  • DSGVO / Patriot act compliant, German based (K8s) hosting
  • Built up a compliant data warehouse for a German clinic
  • VDA 231-300 compliant automotive OpenShift/Azure architectures
  • BaFin compliance

Image on the right created by our Germany-hosted AI

Image generated using our Germany hosted AI

Don't know where to start?

Creating a data landscape can be overwhelming, and not every company needs a full Kubernetes multi-cluster autoscaling setup. That’s why consulting with a professional to determine your specific requirements is crucial.

 

With extensive experience working with international enterprises like VW, HPE, and BMW, as well as small startups like Vios Investing in Taiwan and Otto.ai in New York, we’ve found tailored solutions for every need.

The key is finding what works best for you!

Our projects

Google TimesFM – Opensource contribution

Google TimesFM – Opensource contribution

July 22, 2024

Vios Investments – Trading Infrastructure

Vios Investments – Trading Infrastructure

July 22, 2024

Doku-Chat.de – AI Document chat

Doku-Chat.de – AI Document chat

July 22, 2024

Clinic Buchinger Wilhelmi

Clinic Buchinger Wilhelmi

July 22, 2024

Atruvia / Sparkasse / Volksbank – Data Warehouse

Atruvia / Sparkasse / Volksbank – Data Warehouse

July 22, 2024

BMW / HPE: Worldwide backup solution for VMs

BMW / HPE: Worldwide backup solution for VMs

July 22, 2024

VW / HPE: Solution Architecture data flow of sampling report data

VW / HPE: Solution Architecture data flow of sampling report data

July 22, 2024

Use your data

Use your data

Use your data

Use your data

Use your data

Grow your business

Grow your business

Grow your business

Grow your business

Grow your business

Frequently asked questions

What is a Data Warehouse?

A data warehouse can take many forms – for example as a simple collection of SQL databases, or as a mixture of unstructured and structured data. Therefore there is no “click and run” provider, and each data warehouse needs to be tailored to the requirements. For example, just “dumping” all company data into one huge database will both not improve load times, and also not solve the problem that no one has an idea of what the data could be used for!

This is a mistake many companies are doing – just “collecting” data to say one is doing “Big Data” does not provide any benefits. Employees need to be enabled to easily work with data, and be provided with tools which solve problems they are not familiar with (autoscaling, storage, backups, …) for them, such that they can focus on their main expertise.

Is it hard to implement?

Many consultants will directly quote millions in order to scrap the whole existing landscape and start anew – this is not always the best approach (except if the consultant wants to cash in ;)). Rather the existing bottlenecks and possible improvements from a business perspective should be observed and a solution provided which solves these bottlenecks.

An example: If no one in the company is able to make sense of the data, it does not matter if you can query it in 200 milliseconds, or 20 minutes!

Therefore, our approach is to usually start small, have a look at the different business requirements, and even start with a simple co-existing copy of relevant data, which does not compromise existing workloads. Then, an iterative solution can be worked out in a close cooperation between the end-users and us.

Where and how should I start?

It can easily be overwhelming, especially in huge established systems. Sometimes, it can be quite easy to get started, in setting up a parallel system which does not touch existing workflows.

Do you already know what your “quick win” would be? Or are you not sure yourself? How about a free 15 minute consultation with us – no strings attached?

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