About our case studies. To guard from disclosing information that our clients may not want shared, we do not use company per individual names in our articles.
When our client first came to us, their database costs were only a few thousand dollars per year. However, forecasts of business growth put their database costs in up into the six figures. Upon careful review we characterized their data needs as follows:
Here's what we did.
Scrapped the relational database. As wonderful as MSSQL is, it just wasn't the right solution for this system. It was expensive, and as the business grew, it would be difficult to maintain performance of the time-series-data analysis.
Introduced NoSQL databases, For basic entities like account data, customer information, etc. that needed to be stored, we created a solution that leveraged Google Datastore (now Firestore). This involved denormalizing the database and setting up structured entities to save in DataStore. Fast and inexpensive, we could store all of the data entities we needed with minimal cost
Caching strategies. For user interfaces that needed fast response times, we implemented a caching strategy that functioned much like a real-time database. Keeping commonly accessed information readily available while performing longer data interactions asynchronously in the background, we were able to match, and even exceed response time when compared to leveraging a SQL server for active storage.
As a result of these changes, for the first year of operation data storage costs were reduced by 99.7%. We expected the costs savings to continue to be strong, but not that strong. After a few years in production and having reached anticipated data volumes (many orders of magnitude beyond year one volumes) we followed up by pricing out hosted SQL server instances necessary to meet the current demands and found that depending on the solution provider, our chosen technology stack still represented a 99.7% to 99.9% savings over the original architecture.
This kind of savings is atypical. This system happened to have just the right combination of requirements to allow us to leverage high performance, low cost solutions. It is also important to note that there is not anything inherently inexpensive about Google's data storage solutions. In fact, this same combination of storage technologies, when used without careful attention to implementation could cost thousands of times more per month.
The key, is knowing what your needs are, knowing what technology solutions can best meet those needs, and knowing how to best implement those solutions. It's all about choosing the right tool for the job.
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