Azure Cosmos DB vs Google Cloud Datastore: What are the differences?
Azure Cosmos DB: A fully-managed, globally distributed NoSQL database service. Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development; Google Cloud Datastore: A Fully Managed NoSQL Data Storage Service. Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
Azure Cosmos DB and Google Cloud Datastore belong to "NoSQL Database as a Service" category of the tech stack.
Some of the features offered by Azure Cosmos DB are:
- Fully managed with 99.99% Availability SLA
- Elastically and highly scalable (both throughput and storage)
- Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes
On the other hand, Google Cloud Datastore provides the following key features:
- Schemaless access, with SQL-like querying
- Managed database
- Autoscale with your users
"Best-of-breed NoSQL features" is the primary reason why developers consider Azure Cosmos DB over the competitors, whereas "High scalability" was stated as the key factor in picking Google Cloud Datastore.
According to the StackShare community, Google Cloud Datastore has a broader approval, being mentioned in 45 company stacks & 16 developers stacks; compared to Azure Cosmos DB, which is listed in 24 company stacks and 23 developer stacks.
What is Azure Cosmos DB?
What is Google Cloud Datastore?
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When creating proofs of concept or small personal projects that are hosted primarily in GCP, with non-relational data models, this is the NoSQL managed database I usually pair them with.
This is our primary database, though most of our actual data is stored in static storage. This database houses the metadata necessary for indexing and finding static data.
worked with a client that used datastore as their backend database. helped plan out their schema and architecture. loved the speed and simplicity.
If you need a document-based database with geo-redundancy (imagine AU-HU distance), this is the way to go.