To utilize different, scalable services (databases, queues, storages, middleware, etc.), software needs to be architected towards this goal. Of course, not all solutions were designed with that in mind, so we help in refactoring and rearchitecting it by applying cloud-patterns and plan a close-to-zero downtime migration. Changing the foundation of any software is a tough mission which affects data as well as code. We support you all the way into the cloud with all the assets and dependencies you need.

After identifying layers and components that are suitable to be stand-alone and separated, they can be decoupled and moved into their own space. This allows you to develop and scale them individually without impacting much of the remaining system. Key components become more resilient and are less affected by other, frequently modified parts. For more information you can check Microsoft's architecture e-book

Different components in a distributed system are vulnerable to different kinds of attacks and must live up specific regulations introduced by governments all across the globe. Techniques from SDL help us to eliminate issues before they make it into the final product. The remaining concerns can be found and tackled repeatedly using code analysis, penetration testing and behaviour analysis during runtime.

Making use of connectors and ML-supported tools, many solutions provide efficient and flexible access to on-premise and cloud data storage. ETL and ELT processes feed the data with an optimized format into tools that require fast access on specific structures. By transforming and squashing, queries on your data sources can be improved, optimized and enriched to allow new perspectives and insights on your business. We work with DataBricks and Microsoft Azure to enhance your data on a fundamental level, so your workflows improve.

Different user roles inside a company have different requirements on which they can base their decisions. Identify those requirements to aggregate your datasets into meaningful dashboards and views to support decision-making. We use different frameworks to offer you the ideal representation of your data so you can make the most of it.

Building on existing technologies for CI/CD pipelines, automated testing and monitoring is a reliable, proven foundation for many solutions. Different technologies for identifying faults and security concerns are available for almost any programming language and framework. They run your test suites and provide feedback via hooks and pipes back into CI/CD pipelines and logging/monitoring solutions. Most technologies offer flexibility and customization down to code level, this enables you to seamlessly integrate them into your workflows.

The CI/CD pipeline is but a mere framework to plug your tools into. Static code analysis, third party security analyzers, self-written test suites and many other options reduce the potential of building, approving, and releasing a bug. These methods need to be established and integrated. Ideally, they grow and adjust with your solution’s needs and challenges.

Business and related data across the whole ecosystem can be collected raw and dumped into stashes (i.e. Logstash). The connection between different raw data is made in alerting (i.e. Graylog) or visualization (i.e. Kibana). This is the kind of data that grows over time and can be aggregated to provide historical insights.
Real-time data is mostly used to determine system health and usage and rarely is retained over time, therefore different technologies are available (i.e. Prometheus & Grafana) to cover ephemeral datasets. In a micro-service world, the different modules communicate with each other all the time, different data snippets travel far. Understanding how and when and where they travel can increase awareness and impact software deployment and implementation structures.