If you can store an integer and a value you have a time-series data store. Pick from any of your favorites: CSV files, Microsoft Excel, SQL Server, Oracle, and anyone else. Niche database platforms have popped up over the past few years claiming the time-series use case demands special purpose built solutions. It simply does not.
As you continue your time-series journey I encourage you to put your critical thinking hat on and when you read benchmark results from blog posts and other articles, pay attention to who employs the author. Is the author self-promoting their company? Are they experts in the tuning and configuration of all the platforms under consideration? Is the article from a consulting firm that was paid to produce the analysis? Is the author just stating results or are they intentionally creating fear, uncertainty and doubt? Do yourself a favor and do your own proof of concept by testing with your own data. Reach out directly to the vendor and you will be amazed at the free resources they can provide to help you make your final platform decision. I hope you consider MongoDB for your time-series use case. I love MongoDB and I am confident you will have success with the platform. But don’t take my word for it, test it yourself and reach out to the MongoDB User Google Group or fill out the MongoDB contact form if you have any questions.
MongoDB is a general purpose database that excels at operational workloads including time-series data. So what makes MongoDB great at time-series data? A flexible document model, transactional consistency, horizontal scaling, native datetime support, real-time analytics and visualizations, and millions of users worldwide to name a few. Listen to our webinar on Designing Time-Series Database Applications - Best Practices on Thursday 10/25 to learn more. https://www.mongodb.com/webinar/designing-timeseries-database-applications-best-practices