Next-generation-sequencing (NGS) has had a tremendous impact in most spheres of biology but has completely revolutionized the field of microbial ecology, where scientists can now better understand the genetic (and potential functional) diversity of microbial populations, even when culturing and isolation of individual species is not possible for most members of the community. One key to this progress has been the throughput (and lower costs) of NGS technologies which allow the genomic exploration of even minor players within a microbial community. This extra throughput and resulting 'big data', coupled with lack of tools for processing and visualization, bring a new set of challenges in the interpretation of these complex datasets. While the computationally expensive processing of these large datasets (often querying even larger databases) comprises one issue, it is generally the biological complexity coupled with the complex nature of the specific questions asked of sequencing that create the greatest challenges in interpreting NGS data and inferring biological processes. Examples of biological complexity include the very large number of different organisms present within a community, the dearth of reference genomes compared with the sampled biological complexity, the sequence similarity among disparate genomes, juxtaposed to sequence divergence within an otherwise clonal population (population heterogeneity). A final challenge for biological interpretation of NGS data is the lack of standard algorithms nor of options to visualize such complex data. Some of these issues will be discussed, including a series of efforts designed to lower the barrier for non-experts to use NGS for routine genomics applications by developing a user-friendly, web-based platform with a suite of bioinformatics tools that provide complementary views of shotgun datasets.