Lake ecosystems are hotspots for important ecosystem functions, yet linking their microbiome to physicochemical parameters remains a challenge. Here, we compare 16S rRNA gene-based amplicon sequencing, metagenomics, and metatranscriptomics across 21 European lakes using three strategies: (i) mapping shotgun reads to amplicon-derived OTUs, (ii) marker-specific profiling (rpS3 for metagenomes, rRNA reads for metatranscriptomes), and (iii) recovery of 16S rRNA genes from shotgun assemblies. Strategy (iii) proved unfeasible due to chimeric and highly variable assemblies and was excluded from further analyses. Both strategies (i) and (ii) revealed systematic methodological constraints. Amplicons yielded significantly lower richness and Shannon diversity than metagenomes and metatranscriptomes, while marker-based profiling highlighted broader detection of rare and active taxa. Despite these differences, all lakes showed the same relative ranking of diversity (metatranscriptomes > metagenomes > amplicons), indicating consistent methodological signatures across ecosystems. Beta-diversity analyses confirmed stronger concordance between metagenomes and metatranscriptomes than between either of these and amplicons. Differential abundance analyses further revealed method-specific detection biases, particularly for Proteobacteria and Bacteroidetes, that persisted even after correcting for 16S rRNA gene copy number and primer bias. Linking communities to physicochemical parameters, Mantel and Procrustes analyses showed the strongest global associations for metatranscriptomes, while metagenomes yielded the most stable explanatory OTUs in dbRDA and BioEnv models. Our results demonstrate that the sequencing approach is not merely a technical choice but represents an analytical dimension that fundamentally influences how microbiome–environment interactions are detected and interpreted.