Amazon DynamoDB vs Google Cloud Bigtable: What are the differences?
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; Google Cloud Bigtable: The same database that powers Google Search, Gmail and Analytics. Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
Amazon DynamoDB and Google Cloud Bigtable 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 Bigtable provides the following key features:
- Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.
- Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google’s big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.
- Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable’s total cost of ownership is less than half the cost of its direct competition.
"Predictable performance and cost" is the primary reason why developers consider Amazon DynamoDB over the competitors, whereas "High performance" was stated as the key factor in picking Google Cloud Bigtable.
Lyft, New Relic, and Sellsuki are some of the popular companies that use Amazon DynamoDB, whereas Google Cloud Bigtable is used by Spotify, Resultados Digitais, and Rainist. Amazon DynamoDB has a broader approval, being mentioned in 430 company stacks & 173 developers stacks; compared to Google Cloud Bigtable, which is listed in 17 company stacks and 3 developer stacks.
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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.
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.