See moreĪ few months ago, I had decided to use Postgres because since its version 9 it showed a lot of progress for being a high-availability database. It feels like you have most experience with SQL/RDBMS technologies, so for the simplest learning curve, and if your application fits it, then I'd personally start by looking at AWS Aurora. RDBMS/SQL stores are great for having as many indexes as you want, other than the slow-down in write speed, whereas databases like Amazon DynamoDB provide blisteringly fast read/write performance, but are very limited on key indexing capabilities. MongoDB, with its document-store type solution is a very different model to key-value-pair stores (like AWS DynamoDB), or column stores (like AWS RedShift) or for more complex data relationships, Entity Graph Stores (like AWS Neptune), to stores designed for tokenisation and text search (ElasticSearch) etc.Īlso critical in all this is how many items you believe you need to index by. however please don't fall into the trap of considering 'NoSQL' as being single category. Your question regards 'Relational or not' is obviously key, and you need to consider both your required data structure, as well as the ACID requirements of your application model, as well as the non-functional requirements in terms of scalability, resilience, whether you want security authorisation at the highest application tier, or right down to 'row' level in the database, etc. Particularly if you are planning to host in either AWS or Azure, then your first point of call should be the PaaS (Platform as a Service) databases supplied by these vendors, as you will find yourself requiring a lot less effort to support them, much easier Disaster Recovery options, and also, depending on how PAYG the database is that you use, potentially also much cheaper costs than having a dedicated database server.
The most important question is where are you planning to host? On-premise, or in the cloud. MongoDB has a broader approval, being mentioned in 2175 company stacks & 2143 developers stacks compared to Google Cloud SQL, which is listed in 71 company stacks and 28 developer stacks. Lyft, MIT, and are some of the popular companies that use MongoDB, whereas Google Cloud SQL is used by Implisit, Policygenius, and OTOBANK. Here's a link to MongoDB's open source repository on GitHub. MongoDB is an open source tool with 16.2K GitHub stars and 4.08K GitHub forks. "Fully managed" is the top reason why over 12 developers like Google Cloud SQL, while over 788 developers mention "Document-oriented storage" as the leading cause for choosing MongoDB. Google Cloud SQL can be classified as a tool in the "SQL Database as a Service" category, while MongoDB is grouped under "Databases". MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema.
On the other hand, MongoDB is detailed as " The database for giant ideas". Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs. MySQL databases deployed in the cloud without a fuss. Google Cloud SQL vs MongoDB: What are the differences?ĭevelopers describe Google Cloud SQL as " Store and manage data using a fully-managed, relational MySQL database".
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