MongoDB is a safe choice for genomics research project
February 23, 2019
MongoDB is a safe choice for genomics research project
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with MongoDB
My company is a non-profit healthcare delivery institute, and has been conducting a number of data science/advanced analytics projects collaboratively with other healthcare and research organizations. We had an experience with using MongoDB for a recent big data project with a university that stores genomic sequencing data in a non-SQL database, and developed a web-based visualization tool presenting phenotype patterns based on the data.
- It is basically a well known and popularly used non-SQL database. It provides great performance, especially when reading big sized document or text (such as sequencing), well-developed functions, and online support.
- There are many database developers who are already familiar with MongoDB, like other major non-SQL products. It is easy to hire engineers with reasonable payment.
- Since our project was genomics research, we handled tables with numerous rows and large file size. MongoDB was performing well in hard conditions and very stable.
- There is no JOIN and TRANSACTION, so it was required to add those by application developers. It was mandatory for us to do it since we had to merge genomics data in MongoDB with RDBS based clinical data.
- MongoDB doesn't provide good data wrangling functionalities, such as parsing JSON or XML.
- Data type definition in MongoDB is somewhat different than other databases, and results in some learning curves for our DB and app developers.
- There is little negative impact in using MongoDB in terms of ROI, as it comes with reasonable pricing policies.
- In project and workforce management perspective, it is easy to predict the budget since many developers are familiar with MongoDB. We don't need a "special" developer for it, which causes uncertainty of salary for the specialty.
- MongoDB runs well with most of operating systems, and that's one aspect that makes the product easy to adopt with minimum expenses.
In the beginning, we considered several products in the market. Since our project was a science and research project, our budget wasn't as big as a commercial project, but still, we wanted the product to be scalable so that we could deal with "smooth transition" from research to commercialization. At the end of the discussion, we choose MongoDB rather than other products like Cassandra, simply because we could hire good developers who had expertise in MongoDB. Our management left that decision to developers and let them choose since they knew what's good for them.