Implementing the lakehouse architecture

Figure 4.3 shows a possible implementation of a data lakehouse architecture in a Lambda design. The diagram shows the common lakehouse layers and the technologies used to implement this on Kubernetes. The first group on the left represents the possible data sources to work with this architecture. One of the key advantages of this approach is its ability to ingest and store data from a wide variety of sources and in diverse formats. As shown in the diagram, the data lake can connect to and integrate structured data from databases as well as unstructured data such as API responses, images, videos, XML, and text files. This schema-on-read approach allows the raw data to be loaded quickly without needing upfront modeling, making the architecture highly scalable. When analysis is required, the lakehouse layer enables querying across all these datasets in one place using schema-on-query. This makes it simpler to integrate data from disparate sources...