Amazon DynamoDB vs Google Cloud Bigtable

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Amazon DynamoDB
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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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What is Amazon DynamoDB?

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 Bigtable?

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.
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    What are some alternatives to Amazon DynamoDB and Google Cloud Bigtable?
    Google Cloud Datastore
    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.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Amazon SimpleDB
    Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.
    Amazon S3
    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
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    How developers use Amazon DynamoDB and Google Cloud Bigtable
    Avatar of Karma
    Karma uses Amazon DynamoDBAmazon DynamoDB

    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.

    Avatar of Volkan Özçelik
    Volkan Özçelik uses Amazon DynamoDBAmazon DynamoDB

    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).

    Avatar of CloudRepo
    CloudRepo uses Amazon DynamoDBAmazon DynamoDB

    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.

    Avatar of nrise
    nrise uses Amazon DynamoDBAmazon DynamoDB

    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.

    Avatar of HyperTrack
    HyperTrack uses Amazon DynamoDBAmazon DynamoDB

    To store device health records as it allows super fast writes and range queries.

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