adesso BLOG

In the age of digital innovation and data-driven decision-making, the efficient processing and analysis of data is a decisive competitive advantage. Especially where real-time information can make the difference between success and failure, big data streaming is indispensable. Amazon Web Services (AWS) offers a wide range of services and tools with which companies can collect, process and analyse data in real time. I show what this is all about in my blog post.

Before the introduction of computers, companies used account books, inventory lists and intuition to record key figures. The data warehouse for static reports emerged at the end of the 1980s. Digitalisation brought new challenges. Big data overwhelmed traditional data warehouses, which is why companies introduced data lakes, which also had an impact on the architecture and concepts of analytics systems. The first part of this blog post is about their development, the reasons for their emergence and the problems they solved.

In Part 1 of the blog post, we looked at the basics of data warehouses and data lakes to understand their benefits and limitations. This part is about the evolution to a hybrid solution - the data lakehouse. From the two-tier architecture to the emergence of the lakehouse concept, this part takes us through the evolution of data structures. Learn how the lakehouse combines the strengths of data lakes and warehouses to address today's data-driven challenges.

Microservice-based architectures are a current trend for transforming information systems. But how can these architectures be implemented? The most commonly used solution is Camunda's BPM engine. In my blog post, I show you how this solution also enables microservice choreography.

The goal of SOLID is to produce software that tolerates modifications, can be easily traced and that forms a basis for the components used in many software systems. In my blog post, I introduce you to the five SOLID principles in more detail.