What is a Data Mesh?
New Technology or just an Approach for efficient Data Platforms?
Data Lakes and Data Warehouses or even the combination in a Data Lakehouse are considered to be the key to the use of Big Data. However, these approaches often bring the problem that with data from many sources and formats, they can become too complex and prevent efficient use. This often results in frustrated users.
Recap Data Lake vs. Data Warehouse
To understand the topic holistically a short recap on the topic Data Warehouse vs. Data Lake and where the differences lie. Data Lakes and Data Warehouses are established terms when it comes to storing Big Data, but the two terms are not synonymous. A Data Lake is a vast pool of raw data that has not a specific use case yet. A Data Warehouse, on the other hand, is a repository for structured and organized data that has already been dedicated to a specific purpose [1].
While Data Warehouses use the common ETL process in combination with already organized data in a relational database, a Data Lake uses paradigms such as ELT and a schema on read in relation to unstructured data [2].
