Amazon DynamoDB vs Google Cloud Datastore: What are the differences?
What is Amazon DynamoDB? Fully managed NoSQL database service. All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
What is 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.
Amazon DynamoDB and Google Cloud Datastore belong to "NoSQL Database as a Service" category of the tech stack.
Some of the features offered by Amazon DynamoDB are:
- Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs.
- Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity. If your throughput requirements change, simply update your table's request capacity using the AWS Management Console or the Amazon DynamoDB APIs. You are still able to achieve your prior throughput levels while scaling is underway.
- Fully Distributed, Shared Nothing Architecture – Amazon DynamoDB scales horizontally and can seamlessly scale a single table over hundreds of servers.
On the other hand, Google Cloud Datastore provides the following key features:
- Schemaless access, with SQL-like querying
- Managed database
- Autoscale with your users
"Predictable performance and cost" is the primary reason why developers consider Amazon DynamoDB over the competitors, whereas "High scalability" was stated as the key factor in picking Google Cloud Datastore.
Lyft, New Relic, and Sellsuki are some of the popular companies that use Amazon DynamoDB, whereas Google Cloud Datastore is used by Policygenius, Teleport, and Como. Amazon DynamoDB has a broader approval, being mentioned in 430 company stacks & 173 developers stacks; compared to Google Cloud Datastore, which is listed in 45 company stacks and 16 developer stacks.
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I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.
For most of the stuff we use MySQL. We just use Amazon RDS. But for some stuff we use Amazon DynamoDB. We love DynamoDB. It's amazing. We store usage data in there, for example. I think we have close to seven or eight hundred million records in there and it's scaled like you don't even notice it. You never notice any performance degradation whatsoever. It's insane, and the last time I checked we were paying $150 bucks for that.
zerotoherojs.com ’s userbase, and course details are stored in DynamoDB tables.
The good thing about AWS DynamoDB is: For the amount of traffic that I have, it is free. It is highly-scalable, it is managed by Amazon, and it is pretty fast.
It is, again, one less thing to worry about (when compared to managing your own MongoDB elsewhere).
We store customer metadata in DynamoDB. We decided to use Amazon DynamoDB because it was a fully managed, highly available solution. We didn't want to operate our own SQL server and we wanted to ensure that we built CloudRepo on high availability components so that we could pass that benefit back to our customers.
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.
몇몇 로그는 현재 AWS DynamoDB 에 기록되고 있습니다. 개선을 통해 mongodb 로 옮길 계획을 하고 있습니다. 아주 간단한 데이터를 쌓는 용도로는 나쁘지 않습니다. 다만, 쿼리가 아주 제한적입니다. 사용하기 전에 반드시 DynamoDB 의 스펙을 확인할 필요가 있습니다.
worked with a client that used datastore as their backend database. helped plan out their schema and architecture. loved the speed and simplicity.
To store device health records as it allows super fast writes and range queries.