Lossless lightweight data compression is a very important optimization technique in various application domains like database systems, information retrieval or machine learning. Despite this importance, currently, there exists no comprehensive and non-Technical abstraction. To overcome this issue, we have developed a systematic approach using metamodeling that focuses on the non-Technical concepts of these algorithms. In this paper, we describe COLLATE, the metamodel we developed, and show that each algorithm can be described as a model conforming with COLLATE. Furthermore, we use COLLATE to specify a compression algorithm language COALA, so that lightweight data compression algorithms can be specified and modified in a descriptive and abstract way. Additionally, we present an approach to transform such descriptive algorithms into executable code. As we are going to show, our abstract and non-Technical approach offers several advantages.