<story><title>A one-size-fits-all database doesn&apos;t fit anyone</title><url>https://www.allthingsdistributed.com/2018/06/purpose-built-databases-in-aws.html</url></story><parent_chain></parent_chain><comment><author>okket</author><text>A one-size-fits-all database like PostgreSQL fits most. Moving to a specialised solution is always an option, if necessary. Starting with specialised solutions is a bad idea that will limit flexibility needlessly.</text></comment>
<story><title>A one-size-fits-all database doesn&apos;t fit anyone</title><url>https://www.allthingsdistributed.com/2018/06/purpose-built-databases-in-aws.html</url></story><parent_chain></parent_chain><comment><author>niftich</author><text>This sort of post was most useful in the days when the term &amp;#x27;NoSQL&amp;#x27; was being thrown around as the silver bullet that would revolutionize one&amp;#x27;s business, and there was a significant knowledge gap between the domain experts -- who quite often weren&amp;#x27;t the promoters -- and everyone else. While it&amp;#x27;s still plausible that someone obtains this information for the first time right now, today the mystique around the term &amp;#x27;NoSQL&amp;#x27; has more muted, the marketing has become more factual, and a fair number of people understand the different types of data paradigms (KV, document, graph, timeseries) that the newer offerings provide.&lt;p&gt;As it stands, this post is a brief comparison sheet of AWS&amp;#x27;s datastore offerings, with bite-sized anecdotes that hint at a valuable usecase. It doesn&amp;#x27;t make a particularly strong case for the necessities, justifications, and tradeoffs of each particular type of paradigm or concrete offering,  but is instead a brief content marketing piece with just enough practicality to justify its existence and make its point.</text></comment>